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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
Specialist Security Researcher

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May 1, 2026

How email-delivered prompt injection attacks can target enterprise AI – and why it matters

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What are email-delivered prompt injection attacks?

As organizations rapidly adopt AI assistants to improve productivity, a new class of cyber risk is emerging alongside them: email-delivered AI prompt injection. Unlike traditional attacks that target software vulnerabilities or rely on social engineering, this is the act of embedding malicious or manipulative instructions into content that an AI system will process as part of its normal workflow. Because modern AI tools are designed to ingest and reason over large volumes of data, including emails, documents, and chat histories, they can unintentionally treat hidden attacker-controlled text as legitimate input.  

At Darktrace, our analysis has shown an increase of 90% in the number of customer deployments showing signals associated with potential prompt injection attempts since we began monitoring for this type of activity in late 2025. While it is not always possible to definitively attribute each instance, internal scoring systems designed to identify characteristics consistent with prompt injection have recorded a growing number of high-confidence matches. The upward trend suggests that attackers are actively experimenting with these techniques.

Recent examples of prompt injection attacks

Two early examples of this evolving threat are HashJack and ShadowLeak, which illustrate prompt injection in practice.

HashJack is a novel prompt injection technique discovered in November 2025 that exploits AI-powered web browsers and agentic AI browser assistants. By hiding malicious instructions within the URL fragment (after the # symbol) of a legitimate, trusted website, attackers can trick AI web assistants into performing malicious actions – potentially inserting phishing links, fake contact details, or misleading guidance directly into what appears to be a trusted AI-generated output.

ShadowLeak is a prompt injection method to exfiltrate PII identified in September 2025. This was a flaw in ChatGPT (now patched by OpenAI) which worked via an agent connected to email. If attackers sent the target an email containing a hidden prompt, the agent was tricked into leaking sensitive information to the attacker with no user action or visible UI.

What’s the risk of email-delivered prompt injection attacks?

Enterprise AI assistants often have complete visibility across emails, documents, and internal platforms. This means an attacker does not need to compromise credentials or move laterally through an environment. If successful, they can influence the AI to retrieve relevant information seamlessly, without the labor of compromise and privilege escalation.

The first risk is data exfiltration. In a prompt injection scenario, malicious instructions may be embedded within an ordinary email. As in the ShadowLeak attack, when AI processes that content as part of a legitimate task, it may interpret the hidden text as an instruction. This could result in the AI disclosing sensitive data, summarizing confidential communications, or exposing internal context that would otherwise require significant effort to obtain.

The second risk is agentic workflow poisoning. As AI systems take on more active roles, prompt injection can influence how they behave over time. An attacker could embed instructions that persist across interactions, such as causing the AI to include malicious links in responses or redirect users to untrusted resources. In this way, the attacker inserts themselves into the workflow, effectively acting as a man-in-the-middle within the AI system.

Why can’t other solutions catch email-delivered prompt injection attacks?

AI prompt injection challenges many of the assumptions that traditional email security is built on. It does not fit the usual patterns of phishing, where the goal is to trick a user into clicking a link or opening an attachment.  

Most security solutions are designed to detect signals associated with user engagement: suspicious links, unusual attachments, or social engineering cues. Prompt injection avoids these indicators entirely, meaning there are fewer obvious red flags.

In this case, the intention is actually the opposite of user solicitation. The objective is simply for the email to be delivered and remain in the inbox, appearing benign and unremarkable. The malicious element is not something the recipient is expected to engage with, or even notice.

Detection is further complicated by the nature of the prompts themselves. Unlike known malware signatures or consistent phishing patterns, injected prompts can vary widely in structure and wording. This makes simple pattern-matching approaches, such as regex, unreliable. A broad rule set risks generating large numbers of false positives, while a narrow one is unlikely to capture the diversity of possible injections.

How does Darktrace catch these types of attacks?

The Darktrace approach to email security more generally is to look beyond individual indicators and assess context, which also applies here.  

For example, our prompt density score identifies clusters of prompt-like language within an email rather than just single occurrences. Instead of treating the presence of a phrase as a blocking signal, the focus is on whether there is an unusual concentration of these patterns in a way that suggests injection. Additional weighting can be applied where there are signs of obfuscation. For example, text that is hidden from the user – such as white font or font size zero – but still readable by AI systems can indicate an attempt to conceal malicious prompts.

This is combined with broader behavioral signals. The same communication context used to detect other threats remains relevant, such as whether the content is unusual for the recipient or deviates from normal patterns.

Ask your email provider about email-delivered AI prompt injection

Prompt injection targets not just employees, but the AI systems they rely on, so security approaches need to account for both.

Though there are clear indications of emerging activity, it remains to be seen how popular prompt injection will be with attackers going forward. Still, considering the potential impact of this attack type, it’s worth checking if this risk has been considered by your email security provider.

Questions to ask your email security provider

  • What safeguards are in place to prevent emails from influencing AI‑driven workflows over time?
  • How do you assess email content that’s benign for a human reader, but may carry hidden instructions intended for AI systems?
  • If an email contains no links, no attachments, and no social engineering cues, what signals would your platform use to identify malicious intent?

Visit the Darktrace / EMAIL product hub to discover how we detect and respond to advanced communication threats.  

Learn more about securing AI in your enterprise.

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About the author
Kiri Addison
Senior Director of Product

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April 30, 2026

Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

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Anthropic’s Mythos and what it means for security teams

Recent attention on systems such as Anthropic Mythos highlights a notable problem for defenders. Namely that disclosure’s role in coordinating defensive action is eroding.

As AI systems gain stronger reasoning and coding capability, their usefulness in analyzing complex software environments and identifying weaknesses naturally increases. What has changed is not attacker motivation, but the conditions under which defenders learn about and organize around risk. Vulnerability discovery and exploitation increasingly unfold in ways that turn disclosure into a retrospective signal rather than a reliable starting point for defense.

Faster discovery was inevitable and is already visible

The acceleration of vulnerability discovery was already observable across the ecosystem. Publicly disclosed vulnerabilities (CVEs) have grown at double-digit rates for the past two years, including a 32% increase in 2024 according to NIST, driven in part by AI even prior to Anthropic’s Mythos model. Most notably XBOW topped the HackerOne US bug bounty leaderboard, marking the first time an autonomous penetration tester had done so.  

The technical frontier for AI capabilities has been described elsewhere as jagged, and the implication is that Mythos is exceptional but not unique in this capability. While Mythos appears to make significant progress in complex vulnerability analysis, many other models are already able to find and exploit weaknesses to varying degrees.  

What matters here is not which model performs best, but the fact that vulnerability discovery is no longer a scarce or tightly bounded capability.

The consequence of this shift is not simply earlier discovery. It is a change in the defender-attacker race condition. Disclosure once acted as a rough synchronization point. While attackers sometimes had earlier knowledge, disclosure generally marked the moment when risk became visible and defensive action could be broadly coordinated. Increasingly, that coordination will no longer exist. Exploitation may be underway well before a CVE is published, if it is published at all.

Why patch velocity alone is not the answer

The instinctive response to this shift is to focus on patching faster, but treating patch velocity as the primary solution misunderstands the problem. Most organizations are already constrained in how quickly they can remediate vulnerabilities. Asset sprawl, operational risk, testing requirements, uptime commitments, and unclear ownership all limit response speed, even when vulnerabilities are well understood.

If discovery and exploitation now routinely precede disclosure, then patching cannot be the first line of defense. It becomes one necessary control applied within a timeline that has already shifted. This does not imply that organizations should patch less. It means that patching cannot serve as the organizing principle for defense.

Defense needs a more stable anchor

If disclosure no longer defines when defense begins, then defense needs a reference point that does not depend on knowing the vulnerability in advance.  

Every digital environment has a behavioral character. Systems authenticate, communicate, execute processes, and access resources in relatively consistent ways over time. These patterns are not static rules or signatures. They are learned behaviors that reflect how an organization operates.

When exploitation occurs, even via previously unknown vulnerabilities, those behavioral patterns change.

Attackers may use novel techniques, but they still need to gain access, create processes, move laterally, and will ultimately interact with systems in ways that diverge from what is expected. That deviation is observable regardless of whether the underlying weakness has been formally named.

In an environment where disclosure can no longer be relied on for timing or coordination, behavioral understanding is no longer an optional enhancement; it becomes the only consistently available defensive signal.

Detecting risk before disclosure

Darktrace’s threat research has consistently shown that malicious activity often becomes visible before public disclosure.

In multiple cases, including exploitation of Ivanti, SAP NetWeaver, and Trimble Cityworks, Darktrace detected anomalous behavior days or weeks ahead of CVE publication. These detections did not rely on signatures, threat intelligence feeds, or awareness of the vulnerability itself. They emerged because systems began behaving in ways that did not align with their established patterns.

This reflects a defensive approach grounded in ‘Ethos’, in contrast to the unbounded exploration represented by ‘Mythos’. Here, Mythos describes continuous vulnerability discovery at speed and scale. Ethos reflects an understanding of what is normal and expected within a specific environment, grounded in observed behavior.

Revisiting assume breach

These conditions reinforce a principle long embedded in Zero Trust thinking: assume breach.

If exploitation can occur before disclosure, patching vulnerabilities can no longer act as the organizing principle for defense. Instead, effective defense must focus on monitoring for misuse and constraining attacker activity once access is achieved. Behavioral monitoring allows organizations to identify early‑stage compromise and respond while uncertainty remains, rather than waiting for formal verification.

AI plays a critical role here, not by predicting every exploit, but by continuously learning what normal looks like within a specific environment and identifying meaningful deviation at machine speed. Identifying that deviation enables defenders to respond by constraining activity back towards normal patterns of behavior.

Not an arms race, but an asymmetry

AI is often framed as fueling an arms race between attackers and defenders. In practice, the more important dynamic is asymmetry.

Attackers operate broadly, scanning many environments for opportunities. Defenders operate deeply within their own systems, and it’s this business context which is so significant. Behavioral understanding gives defenders a durable advantage. Attackers may automate discovery, but they cannot easily reproduce what belonging looks like inside a particular organization.

A changed defensive model

AI‑accelerated vulnerability discovery does not mean defenders have lost. It does mean that disclosure‑driven, patch‑centric models no longer provide a sufficient foundation for resilience.

As vulnerability volumes grow and exploitation timelines compress, effective defense increasingly depends on continuous behavioral understanding, detection that does not rely on prior disclosure, and rapid containment to limit impact. In this model, CVEs confirm risk rather than define when defense begins.

The industry has already seen this approach work in practice. As AI continues to reshape both offense and defense, behavioral detection will move from being complementary to being essential.

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician
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