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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

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  

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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December 18, 2025

Why organizations are moving to label-free, behavioral DLP for outbound email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

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Carlos Gray
Senior Product Marketing Manager, Email

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December 17, 2025

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

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What is an Adversary-in-the-middle (AiTM) attack?

Adversary-in-the-Middle (AiTM) attacks are a sophisticated technique often paired with phishing campaigns to steal user credentials. Unlike traditional phishing, which multi-factor authentication (MFA) increasingly mitigates, AiTM attacks leverage reverse proxy servers to intercept authentication tokens and session cookies. This allows attackers to bypass MFA entirely and hijack active sessions, stealthily maintaining access without repeated logins.

This blog examines a real-world incident detected during a Darktrace customer trial, highlighting how Darktrace / EMAILTM and Darktrace / IDENTITYTM identified the emerging compromise in a customer’s email and software-as-a-service (SaaS) environment, tracked its progression, and could have intervened at critical moments to contain the threat had Darktrace’s Autonomous Response capability been enabled.

What does an AiTM attack look like?

Inbound phishing email

Attacks typically begin with a phishing email, often originating from the compromised account of a known contact like a vendor or business partner. These emails will often contain malicious links or attachments leading to fake login pages designed to spoof legitimate login platforms, like Microsoft 365, designed to harvest user credentials.

Proxy-based credential theft and session hijacking

When a user clicks on a malicious link, they are redirected through an attacker-controlled proxy that impersonates legitimate services.  This proxy forwards login requests to Microsoft, making the login page appear legitimate. After the user successfully completes MFA, the attacker captures credentials and session tokens, enabling full account takeover without the need for reauthentication.

Follow-on attacks

Once inside, attackers will typically establish persistence through the creation of email rules or registering OAuth applications. From there, they often act on their objectives, exfiltrating sensitive data and launching additional business email compromise (BEC) campaigns. These campaigns can include fraudulent payment requests to external contacts or internal phishing designed to compromise more accounts and enable lateral movement across the organization.

Darktrace’s detection of an AiTM attack

At the end of September 2025, Darktrace detected one such example of an AiTM attack on the network of a customer trialling Darktrace / EMAIL and Darktrace / IDENTITY.

In this instance, the first indicator of compromise observed by Darktrace was the creation of a malicious email rule on one of the customer’s Office 365 accounts, suggesting the account had likely already been compromised before Darktrace was deployed for the trial.

Darktrace / IDENTITY observed the account creating a new email rule with a randomly generated name, likely to hide its presence from the legitimate account owner. The rule marked all inbound emails as read and deleted them, while ignoring any existing mail rules on the account. This rule was likely intended to conceal any replies to malicious emails the attacker had sent from the legitimate account owner and to facilitate further phishing attempts.

Darktrace’s detection of the anomalous email rule creation.
Figure 1: Darktrace’s detection of the anomalous email rule creation.

Internal and external phishing

Following the creation of the email rule, Darktrace / EMAIL observed a surge of suspicious activity on the user’s account. The account sent emails with subject lines referencing payment information to over 9,000 different external recipients within just one hour. Darktrace also identified that these emails contained a link to an unusual Google Drive endpoint, embedded in the text “download order and invoice”.

Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Figure 2: Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.
Figure 3: Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.

As Darktrace / EMAIL flagged the message with the ‘Compromise Indicators’ tag (Figure 2), it would have been held automatically if the customer had enabled default Data Loss Prevention (DLP) Action Flows in their email environment, preventing any external phishing attempts.

Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.
Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.

Darktrace analysis revealed that, after clicking the malicious link in the email, recipients would be redirected to a convincing landing page that closely mimicked the customer’s legitimate branding, including authentic imagery and logos, where prompted to download with a PDF named “invoice”.

Figure 5: Download and login prompts presented to recipients after following the malicious email link, shown here in safe view.

After clicking the “Download” button, users would be prompted to enter their company credentials on a page that was likely a credential-harvesting tool, designed to steal corporate login details and enable further compromise of SaaS and email accounts.

Darktrace’s Response

In this case, Darktrace’s Autonomous Response was not fully enabled across the customer’s email or SaaS environments, allowing the compromise to progress,  as observed by Darktrace here.

Despite this, Darktrace / EMAIL’s successful detection of the malicious Google Drive link in the internal phishing emails prompted it to suggest ‘Lock Link’, as a recommended action for the customer’s security team to manually apply. This action would have automatically placed the malicious link behind a warning or screening page blocking users from visiting it.

Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.
Figure 6: Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.

Furthermore, if active in the customer’s SaaS environment, Darktrace would likely have been able to mitigate the threat even earlier, at the point of the first unusual activity: the creation of a new email rule. Mitigative actions would have included forcing the user to log out, terminating any active sessions, and disabling the account.

Conclusion

AiTM attacks represent a significant evolution in credential theft techniques, enabling attackers to bypass MFA and hijack active sessions through reverse proxy infrastructure. In the real-world case we explored, Darktrace’s AI-driven detection identified multiple stages of the attack, from anomalous email rule creation to suspicious internal email activity, demonstrating how Autonomous Response could have contained the threat before escalation.

MFA is a critical security measure, but it is no longer a silver bullet. Attackers are increasingly targeting session tokens rather than passwords, exploiting trusted SaaS environments and internal communications to remain undetected. Behavioral AI provides a vital layer of defense by spotting subtle anomalies that traditional tools often miss

Security teams must move beyond static defenses and embrace adaptive, AI-driven solutions that can detect and respond in real time. Regularly review SaaS configurations, enforce conditional access policies, and deploy technologies that understand “normal” behavior to stop attackers before they succeed.

Credit to David Ison (Cyber Analyst), Bertille Pierron (Solutions Engineer), Ryan Traill (Analyst Content Lead)

Appendices

Models

SaaS / Anomalous New Email Rule

Tactic – Technique – Sub-Technique  

Phishing - T1566

Adversary-in-the-Middle - T1557

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