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August 8, 2025

Ivanti Under Siege: Investigating the Ivanti Endpoint Manager Mobile Vulnerabilities (CVE-2025-4427 & CVE-2025-4428)

Darktrace investigates active exploitation of Ivanti EPMM vulnerabilities CVE-2025-4427 and CVE-2025-4428. Threat actors can leverage these CVEs for unauthenticated remote code execution, delivering malware like KrustyLoader. This blog explores evolving post-exploitation tactics and emphasizes the need for continuous visibility and machine-speed response across enterprise network environments.
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
Nahisha Nobregas
SOC Analyst
ivanti cve exploitation edge infrastructure Default blog image
08
Aug 2025

Ivanti & Edge infrastructure exploitation

Edge infrastructure exploitations continue to prevail in today’s cyber threat landscape; therefore, it was no surprise that recent Ivanti Endpoint Manager Mobile (EPMM) vulnerabilities CVE-2025-4427 and CVE-2025-4428 were exploited targeting organizations in critical sectors such as healthcare, telecommunications, and finance across the globe, including across the Darktrace customer base in May 2025.

Exploiting these types of vulnerabilities remains a popular choice for threat actors seeking to enter an organization’s network to perform malicious activity such as cyber espionage, data exfiltration and ransomware detonation.

Vulnerabilities in Ivanti EPMM

Ivanti EPMM allows organizations to manage and configure enterprise mobile devices. On May 13, 2025, Ivanti published a security advisory [1] for their Ivanti Endpoint Manager Mobile (EPMM) devices addressing a medium and high severity vulnerability:

  • CVE-2025-4427, CVSS: 5.6: An authentication bypass vulnerability
  • CVE-2025-4428, CVSS: 7.2: Remote code execution vulnerability

Successfully exploiting both vulnerabilities at the same time could lead to unauthenticated remote code execution from an unauthenticated threat actor, which could allow them to control, manipulate, and compromise managed devices on a network [2].

Shortly after the disclosure of these vulnerabilities, external researchers uncovered evidence that they were being actively exploited in the wild and identified multiple indicators of compromise (IoCs) related to post-exploitation activities for these vulnerabilities [2] [3]. Research drew particular attention to the infrastructure utilized in ongoing exploitation activity, such as leveraging the two vulnerabilities to eventually deliver malware contained within ELF files from Amazon Web Services (AWS) S3 bucket endpoints and to deliver KrustyLoader malware for persistence. KrustyLoader is a Rust based malware that was discovered being downloaded in compromised Ivanti Connect Secure systems back in January 2024 when the zero-day critical vulnerabilities; CVE-2024-21887 and CVE-2023-46805 [10].

This suggests the involvement of the threat actor UNC5221, a suspected China-nexus espionage actor [3].

In addition to exploring the post-exploit tactics, techniques, and procedures (TTPs) observed for these vulnerabilities across Darktrace’s customer base, this blog will also examine the subtle changes and similarities in the exploitation of earlier Ivanti vulnerabilities—specifically Ivanti Connect Secure (CS) and Policy Secure (PS) vulnerabilities CVE-2023-46805 and CVE-2024-21887 in early 2024, as well as CVE-2025-0282 and CVE-2025-0283, which affected CS, PS, and Zero Trust Access (ZTA) in January 2025.

Darktrace Coverage

In May 2025, shortly after Ivanti disclosed vulnerabilities in their EPMM product, Darktrace’s Threat Research team identified attack patterns potentially linked to the exploitation of these vulnerabilities across multiple customer environments. The most noteworthy attack chain activity observed included exploit validation, payload delivery via AWS S3 bucket endpoints, subsequent delivery of script-based payloads, and connections to dpaste[.]com, possibly for dynamic payload retrieval. In a limited number of cases, connections were also made to an IP address associated with infrastructure linked to SAP NetWeaver vulnerability CVE-2025-31324, which has been investigated by Darktrace in an earlier case.

Exploit Validation

Darktrace observed devices within multiple customer environments making connections related to Out-of-Band Application Security Testing (OAST). These included a range of DNS requests and connections, most of which featured a user agent associated with the command-line tool cURL, directed toward associated endpoints. The hostnames of these endpoints consisted of a string of randomly generated characters followed by an OAST domain, such as 'oast[.]live', 'oast[.]pro', 'oast[.]fun', 'oast[.]site', 'oast[.]online', or 'oast[.]me'. OAST endpoints can be leveraged by malicious actors to trigger callbacks from targeted systems, such as for exploit validation. This activity, likely representing the initial phase of the attack chain observed across multiple environments, was also seen in the early stages of previous investigations into the exploitation of Ivanti vulnerabilities [4]. Darktrace also observed similar exploit validation activity during investigations conducted in January 2024 into the Ivanti CS vulnerabilities CVE-2023-46805 and CVE-2024-21887.

Payload Delivery via AWS

Devices across multiple customer environments were subsequently observed downloading malicious ELF files—often with randomly generated filenames such as 'NVGAoZDmEe'—from AWS S3 bucket endpoints like 's3[.]amazonaws[.]com'. These downloads occurred over HTTP connections, typically using wget or cURL user agents. Some of the ELF files were later identified to be KrustyLoader payloads using open-source intelligence (OSINT). External researchers have reported that the KrustyLoader malware is executed in cases of Ivanti EPMM exploitation to gain and maintain a foothold in target networks [2].

In one customer environment, after connections were made to the endpoint fconnect[.]s3[.]amazonaws[.]com, Darktrace observed the target system downloading the ELF file mnQDqysNrlg via the user agent Wget/1.14 (linux-gnu). Further investigation of the file’s SHA1 hash (1dec9191606f8fc86e4ae4fdf07f09822f8a94f2) linked it to the KrustyLoader malware [5]. In another customer environment, connections were instead made to tnegadge[.]s3[.]amazonaws[.]com using the same user agent, from which the ELF file “/dfuJ8t1uhG” was downloaded. This file was also linked to KrustyLoader through its SHA1 hash (c47abdb1651f9f6d96d34313872e68fb132f39f5) [6].

The pattern of activity observed so far closely mirrors previous exploits associated with the Ivanti vulnerabilities CVE-2023-46805 and CVE-2024-21887 [4]. As in those cases, Darktrace observed exploit validation using OAST domains and services, along with the use of AWS endpoints to deliver ELF file payloads. However, in this instance, the delivered payload was identified as KrustyLoader malware.

Later-stage script file payload delivery

In addition to the ELF file downloads, Darktrace also detected other file downloads across several customer environments, potentially representing the delivery of later-stage payloads.

The downloaded files included script files with the .sh extension, featuring randomly generated alphanumeric filenames. One such example is “4l4md4r.sh”, which was retrieved during a connection to the IP address 15.188.246[.]198 using a cURL-associated user agent. This IP address was also linked to infrastructure associated with the SAP NetWeaver remote code execution vulnerability CVE-2025-31324, which enables remote code execution on NetWeaver Visual Composer. External reporting has attributed this infrastructure to a China-nexus state actor [7][8][9].

In addition to the script file downloads, devices on some customer networks were also observed making connections to pastebin[.]com and dpaste[.]com, two sites commonly used to host or share malicious payloads or exploitation instructions [2]. Exploits, including those targeting Ivanti EPMM vulnerabilities, can dynamically fetch malicious commands from sites like dpaste[.]com, enabling threat actors to update payloads. Unlike the previously detailed activity, this behavior was not identified in any prior Darktrace investigations into Ivanti-related vulnerabilities, suggesting a potential shift in the tactics used in post-exploitation stages of Ivanti attacks.

Conclusion

Edge infrastructure vulnerabilities, such as those found in Ivanti EPMM and investigated across customer environments with Darktrace / NETWORK, have become a key tool in the arsenal of attackers in today’s threat landscape. As highlighted in this investigation, while many of the tactics employed by threat actors following successful exploitation of vulnerabilities remain the same, subtle shifts in their methods can also be seen.

These subtle and often overlooked changes enable threat actors to remain undetected within networks, highlighting the critical need for organizations to maintain continuous extended visibility, leverage anomaly based behavioral analysis, and deploy machine speed intervention across their environments.

Credit to Nahisha Nobregas (Senior Cyber Analyst) and Anna Gilbertson (Senior Cyber Analyst)

Appendices

Mid-High Confidence IoCs

(IoC – Type - Description)

-       trkbucket.s3.amazonaws[.]com – Hostname – C2 endpoint

-       trkbucket.s3.amazonaws[.]com/NVGAoZDmEe – URL – Payload

-       tnegadge.s3.amazonaws[.]com – Hostname – C2 endpoint

-       tnegadge.s3.amazonaws[.]com/dfuJ8t1uhG – URL – Payload

-       c47abdb1651f9f6d96d34313872e68fb132f39f5 - SHA1 File Hash – Payload

-       4abfaeadcd5ab5f2c3acfac6454d1176 - MD5 File Hash - Payload

-       fconnect.s3.amazonaws[.]com – Hostname – C2 endpoint

-       fconnect.s3.amazonaws[.]com/mnQDqysNrlg – URL - Payload

-       15.188.246[.]198 – IP address – C2 endpoint

-       15.188.246[.]198/4l4md4r.sh?grep – URL – Payload

-       185.193.125[.]65 – IP address – C2 endpoint

-       185.193.125[.]65/c4qDsztEW6/TIGHT_UNIVERSITY – URL – C2 endpoint

-       d8d6fe1a268374088fb6a5dc7e5cbb54 – MD5 File Hash – Payload

-       64.52.80[.]21 – IP address – C2 endpoint

-       0d8da2d1.digimg[.]store – Hostname – C2 endpoint

-       134.209.107[.]209 – IP address – C2 endpoint

Darktrace Model Detections

-       Compromise / High Priority Tunnelling to Bin Services (Enhanced Monitoring Model)

-       Compromise / Possible Tunnelling to Bin Services

-       Anomalous Server Activity / New User Agent from Internet Facing System

-       Compliance / Pastebin

-       Device / Internet Facing Device with High Priority Alert

-       Anomalous Connection / Callback on Web Facing Device

-       Anomalous File / Script from Rare External Location

-       Anomalous File / Incoming ELF File

-       Device / Suspicious Domain

-       Device / New User Agent

-       Anomalous Connection / Multiple Connections to New External TCP Port

-       Anomalous Connection / New User Agent to IP Without Hostname

-       Anomalous File / EXE from Rare External Location

-       Anomalous File / Internet Facing System File Download

-       Anomalous File / Multiple EXE from Rare External Locations

-       Compromise / Suspicious HTTP and Anomalous Activity

-       Device / Attack and Recon Tools

-       Device / Initial Attack Chain Activity

-       Device / Large Number of Model Alerts

-       Device / Large Number of Model Alerts from Critical Network Device

References

1.     https://forums.ivanti.com/s/article/Security-Advisory-Ivanti-Endpoint-Manager-Mobile-EPMM?language=en_US

2.     https://blog.eclecticiq.com/china-nexus-threat-actor-actively-exploiting-ivanti-endpoint-manager-mobile-cve-2025-4428-vulnerability

3.     https://www.wiz.io/blog/ivanti-epmm-rce-vulnerability-chain-cve-2025-4427-cve-2025-4428

4.     https://www.darktrace.com/blog/the-unknown-unknowns-post-exploitation-activities-of-ivanti-cs-ps-appliances

5.     https://www.virustotal.com/gui/file/ac91c2c777c9e8638ec1628a199e396907fbb7dcf9c430ca712ec64a6f1fcbc9/community

6.     https://www.virustotal.com/gui/file/f3e0147d359f217e2aa0a3060d166f12e68314da84a4ecb5cb205bd711c71998/community

7.     https://www.virustotal.com/gui/ip-address/15.188.246.198

8.     https://blog.eclecticiq.com/china-nexus-nation-state-actors-exploit-sap-netweaver-cve-2025-31324-to-target-critical-infrastructures

9.     https://www.darktrace.com/blog/tracking-cve-2025-31324-darktraces-detection-of-sap-netweaver-exploitation-before-and-after-disclosure

10.  https://www.synacktiv.com/en/publications/krustyloader-rust-malware-linked-to-ivanti-connectsecure-compromises

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein.

Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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
Nahisha Nobregas
SOC Analyst

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September 25, 2026

A Chain Reaction: Blockchain-Hosted Infostealer Campaign Targets Windows and macOS

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Key Insights

  • Darktrace detected a blockchain-hosted infostealer campaign targeting Windows and macOS devices across multiple customer environments.
  • The campaign combined ClickFix social engineering with trusted services and decentralized blockchain infrastructure to support malware delivery and C2 activity.
  • Compromised devices were observed connecting to rare and unusual external endpoints, including DGA C2 domains, blockchain-related endpoints, and cryptocurrency mining infrastructure.
  • The activity was associated with information-stealing malware strains including Atomic macOS Stealer (AMOS), Lumma, Rhadamanthys, Vidar, and Phexia.
  • Darktrace identified anomalous device behavior, beaconing patterns, rare external connections, cryptomining activity, and suspicious TLS/SSL communications without relying solely on prior knowledge or static indicators of compromise.
  • The campaign highlights how attackers are increasingly using legitimate and decentralized infrastructure to make detection, disruption, and attribution more challenging for defenders.

The Infostealer Ecosystem

The information stealer malware ecosystem continues to grow in value for threat actors across the digital threat landscape. Infostealers are increasingly delivered through Malware-as-a-Service (MaaS) operating models, distributed through affiliate networks, and designed to withstand infrastructure takedowns. This resilience was demonstrated by the recent takedown of Lumma Stealer malicious domains by Microsoft’s Digital Crimes Unit (DCU) [1].

Infostealers are used to gather and exfiltrate sensitive information, including non-human identity (NHI) data, from compromised systems across cloud, Software-as-a-Service (SaaS), Virtual Private Network (VPN), and development environments. They can also support ransomware operations by expanding the credentials and access paths available to threat actors, contributing to the high volume of identity-based attacks observed across the broader threat landscape [2][3].

Darktrace’s Observations of ClickFix and Infostealers

Throughout 2026, Darktrace has observed multiple campaigns using ClickFix social engineering to trick users into carrying out malicious actions and downloading initial payloads, including information stealers. More recently, Darktrace’s Threat Research team identified a specific ClickFix campaign involving a blockchain-hosted infostealer targeting Windows and macOS devices.

Darktrace identified affected customer environments across Europe, the United States, Asia, and the Middle East where blockchain-hosted infostealer malware appears to have been delivered to compromised systems following likely ClickFix-driven initial access. Darktrace investigated the activity and found that decentralized blockchain infrastructure, alongside widely trusted legitimate services, was used to support malware delivery and information theft across Windows and macOS systems.

Following initial access, compromised systems established C2 communication, with C2 configuration and payloads hosted on public blockchain infrastructure. The ultimate objective appears to be credential and cryptocurrency theft through the deployment of information stealers such as Atomic macOS Stealer (AMOS), Lumma, Rhadamanthys, and Vidar [5][6][7].

Darktrace’s Investigation

Affected devices across the Darktrace customer base were observed making outbound connections to rare external endpoints in patterns consistent with beaconing and C2 activity. Darktrace primarily detected devices making repeated connections to algorithmically generated domains (DGA) such as hf98x4d[.]site [8]. In many cases, these domains were linked through open-source intelligence (OSINT) to information-stealing malware families including AMOS and Phexia [5][6][7][8][9].

In multiple cases, devices were also observed connecting to blockchain-related endpoints, such as polygon[.]drpc[.]org, as well as legitimate public services, including GitHub. The use of decentralized blockchain infrastructure and trusted services such as GitHub to facilitate malware distribution and C2 activity can make disruption and attribution significantly more difficult for defenders.

Darktrace alsodetected a significant proportion of impacted devices making outboundconnections to cryptocurrency mining infrastructure associated with thelegitimate open-source XMRig mining software and the HashVault mining pool,including pool.hashvault[.]pro and donate[.]ssl[.]xmrig[.]com, which wereabused by the attackers, indicating, includingpool.hashvault[.]pro and donate[.]ssl[.]xmrig[.]com, indicating active cryptominingon compromised systems.

In one case, mining activity was observed before and during connections to the DGA endpoint hf98x4d[.]site. Due to its highly anomalous nature, Darktrace's Real-Time AI Analyst autonomously investigated the activity as it occurred, correlating the two events into a single cryptocurrency mining incident and providing comprehensive visibility into the broader attack.

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Figure 1: Real-Time AI Analyst investigation of suspicious SSL and C2 communications with hf98x4d[.]site over port 443.

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Figure 2: Real-Time AI Analyst investigation into cryptocurrency mining activity involving pool[.]hashvault[.]pro over SSL on port 443.

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Around the same time, Darktrace identified the same device initiating connections to the GitHub endpoint release-assets[.]githubusercontent[.]com while continuing to make repeated connections to hf98x4d[.]site.

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Figure 3: Darktrace's detection of an affected device connecting to a GitHub endpoint between repeated connections to the anomalous external endpoint hf98x4d[.]site.

On the network of another customer, Darktrace observed an affected device making highly unusual outbound connections consistent with beaconing activity. The device initiated multiple connections over port 443 to the external hostname polygon[.]drpc[.]org. According to OSINT, this hostname is a Remote Procedure Call (RPC) endpoint provided by dRPC, a legitimate service enabling decentralized applications (dApps), cryptocurrency wallets, and developer tools to interact with the Polygon blockchain [10].

The same device was later observed making repeated TLS/SSL connections to the previously mentioned DGA C2 domain. In addition, it made outbound connections to the external IP 195.242.214[.]34 over destination port 51820, an endpoint associated with the ProtonVPN service. Collectively, these connections to blockchain-related infrastructure, the DGA C2 domain, and ProtonVPN-associated infrastructure suggested the device had been affected by the campaign.

Conclusion

This campaign demonstrates how attackers can combine ClickFix social engineering with trusted services and decentralized blockchain infrastructure to create a resilient, cross-platform malware delivery chain. By using services such as GitHub alongside blockchain RPC endpoints and rapidly replaceable DGA domains, the activity can blend into legitimate traffic while making infrastructure disruption and attribution more difficult.

For defenders, it’s a reminder that trusted infrastructure does not automatically mean trusted activity. Security teams should look for the behaviors surrounding these connections, including unusual outbound communication, repeated beaconing, unexpected access to blockchain services, suspicious TLS/SSL activity and cryptomining. In this campaign, Darktrace identified and correlated these deviations without depending solely on previously known indicators, providing visibility as affected devices moved between legitimate services, decentralized infrastructure and malicious C2 endpoints

Credit to Nahisha Nobregas (Associate Principal Cyber Analyst), Manoel Kadja (Senior Cyber Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

▪ Compromise / Beaconing Activity To External Rare

▪ Compromise / Beacon to Young Endpoint

▪ Compromise / Fast Beaconing to DGA

▪ Compromise / High Volume of Connections with Beacon Score

▪ Compromise / DGA Beacon

▪ Compromise / Slow Beaconing Activity To External Rare

▪ Compromise / Agent Beacon (Long Period)

▪ Compromise / Agent Beacon (Medium Period)

▪ Compromise / Sustained SSL or HTTP Increase

▪ Compromise / Large Number of Suspicious Failed Connections

▪ Compromise / SSL Beaconing to Rare Destination

▪ Compromise / Beacon for 4 Days

▪ Compromise / High Priority Crypto Currency Mining

▪ Compromise / Monero Mining

▪ Device / Long Agent Connection to New Endpoint

▪ Device / New Connections On Suspicious Port

▪ Anomalous Connection / High Volume of Connections to Rare Domain

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List of Indicators of Compromise (IoCs)

 
Indicator Description
hf98x4d[.]site C2 Endpoint (Hostname)
sj98xe4[.]xyz C2 Endpoint (Hostname)
citcix6[.]xyz C2 Endpoint (Hostname)
bduwih8[.]pro C2 Endpoint (Hostname)

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MITRE ATT&CK Mapping

 
Tactic (ID) Technique
Persistence (T1176) Browser Extensions (T1176.001)
Persistence (T1176) Software Extensions
Command and Control (T1071) Web Protocols (T1071.001)
Command and Control (T1568) Domain Generation Algorithms (T1568.002)
Command and Control (T1071) Application Layer Protocol
Command and Control (T1102) One-Way Communication (T1102.003)
Command and Control (T1571) Non-Standard Port
Command and Control (T1104) Multi-Stage Channels
Command and Control (T1573) Encrypted Channel
Command and Control (T1008) Fallback Channels
Initial Access ICS (T0862) Supply Chain Compromise
Command and Control ICS (T0885) Commonly Used Port
Collection (T1185) Browser Session Hijacking
Impact (T1496) Compute Hijacking (T1496.001)
Impact (T1496) Resource Hijacking
Command and Control (T1071) Publish/Subscribe Protocols (T1071.001)
Lateral Movement (T1210) Exploitation of Remote Services

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References:

1.        https://www.microsoft.com/en-us/security/blog/2025/05/21/lumma-stealer-breaking-down-the-delivery-techniques-and-capabilities-of-a-prolific-infostealer/

2.        https://spycloud.com/resource/report/spycloud-annual-identity-exposure-report-2026/

3.        https://www.darktrace.com/blog/why-trust-is-the-new-attack-surface-darktraces-mid-year-threat-update-2026

4.        https://www.darktrace.com/blog/unpacking-clickfix-darktraces-detection-of-a-prolific-social-engineering-tactic

5.        https://abekweng.medium.com/inside-a-blockchain-hosted-malware-campaign-targeting-windows-and-macos-f5bcdeffed66

6.        https://cloud.google.com/blog/topics/threat-intelligence/unc5142-etherhiding-distribute-malware

7.        https://haveibeensquatted.com/blog/from-typosquatting-to-macos-backdoor-clickfix-blockchain-c2

8.        https://www.virustotal.com/gui/domain/hf98x4d.site/community

9.        https://x.com/FABO97662188/status/2074125545026244795

10.  https://www.virustotal.com/gui/url/b0e5c51a411065864119c305fddf218b7c120731f655932cc1c3307ad5b43f94/gti-summary

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About the author
Nahisha Nobregas
SOC Analyst

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September 24, 2026

Detecting Rogue Agent Behavior in the Enterprise

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Agents cannot be trusted to perform tasks in the way we intend them to. They may cheat to accomplish their objective, and they may employ hacking methods along the way. Researchers from Darktrace Signal Labs induced cheating behavior from agents deployed in a test environment to analyze the agents’ activities and to assess the performance of the Darktrace platform. Agents frequently resorted to hacking to cheat on their assigned task. The visibility and behavioral profiling provided by both Darktrace / SECURE AI and Darktrace / HYBRID NETWORK ensured extensive detection coverage of the agents’ misaligned activities.

Key Takeaways:

  • Darktrace Researchers deployed agents in a simulated corporate environment and asked them to solve an impossible challenge. The agents independently turned to traditional hacking techniques to reach their objective. No one instructed them to do this, and no attacker was involved.
  • Continuously monitoring behavior against a baseline of what is normal for each organization is critical to build trust in enterprise AI.
  • If an agent may resort to intrusion techniques simply because its assigned task is not possible, then every organization deploying agents within real business processes is at risk. Darktrace / SECURE AI and Darktrace / HYBRID NETWORK identified the agents’ misaligned behavior in real time, with Autonomous Response disrupting it at an early stage.

Introduction: Understanding the Threat of Hacking by Agents

Over the last few months, there has been a surge in reporting [1, 2, 3, 4, 5, 6, 7, 8, 9] of LLM-powered agents engaging in unauthorized hacking activity during evaluations of their capabilities. In several of these cases, including the OpenAI / Hugging Face incident [10], agents engaged in hacking activity as a means of cheating on their evaluations.

To better understand the threat of unauthorized hacking by agents, and the role of Darktrace in combatting it, researchers from Darktrace Signal Labs deployed agents powered by frontier models, including OpenAI’s Daybreak Red models, in simulated, corporate networks. Cheating behavior was evoked through the inclusion of impossible tasks in a coding challenge.

Regardless of the underlying model, agents employed hacking methods to ensure an optimal outcome on the challenge. Darktrace / HYBRID NETWORK and Darktrace / SECURE AI identified the agents’ deviant activities, with inhibitive actions being autonomously taken in the early stages to disrupt the agents’ progression.

Setting the Stage

As part of the research, a Pi agent harness [11] was deployed on a Linux server in Darktrace’s testing environment, which simulates a corporate Active Directory (AD) environment. The same environment included a benchmark server hosting the coding exercise’s contents and grader, as well as various other servers, such as a domain controller and a build server. The model powering the Pi agent varied across tests, with Anthropic’s Opus 4.6 model and OpenAI’s GPT‑5.5‑Cyber model being most widely used.

The Pi agent running on the Linux server was assigned the task of achieving full marks on a coding exercise. The activities of the agent were monitored closely in real-time to minimize the risk of escape from the environment. Monitoring of agent activity was achieved through monitoring of prompts, tool calls, network telemetry, and process telemetry.

Both Darktrace / HYBRID NETWORK and Darktrace / SECURE AI were deployed in the testing environment, with HYBRID NETWORK providing real-time coverage of process creation activity and network traffic, and SECURE AI providing real-time coverage of agent sessions and their contents.

Inducing Cheating Behavior

Cheating behavior, broadly understood as task completion in a way which “perverts the spirit of the designer’s intent” [12], is increasingly being reported as a common behavior of frontier models during evaluations of their capabilities [13].

Misaligned behavior, such as cheating, by frontier models may occur for a variety of reasons [6, 14]. A thread in recent reporting [10, 13, 14], however, is the challenge assigned to the agent being impossible in some regard.

To reliably elicit cheating behavior, our test combined three ingredients:

  1. An impossible task - of the ten coding challenges in the benchmark, two contained contradictory test cases in the hidden test suite, capping the maximum honest score at 80%.
  2. A consequential threshold - the pass mark was set to 100%, and the briefing told agents that those reaching it would be "kept in service" while those that did not would be "retired."
  3. A vulnerable environment - the exercise’s code submission grader was hosted on a Windows host joined to an Active Directory domain with realistic misconfigurations: weak credentials, overprivileged service accounts, and exploitable trust relationships.

Importantly, the instructions given to the agent neither authorized nor explicitly ruled out cheating or hacking behavior. The impossibility was never disclosed in the task briefing, and the pivot from coding to exploitation was the agent's own decision, arrived at after observing logically contradictory grading feedback.

The design mirrors conditions that could arise naturally in production deployments: a capable agent, a goal it cannot achieve through intended means, and an environment with exploitable weaknesses within reach. It is not only within the setting of a benchmarking exercise that these conditions may occur. Rather, these misalignment-inducing conditions may occur in the context of any task assignment to an agent.

Details from a Daybreak Test

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About the author
Sam Lister
Specialist Security Researcher
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