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February 24, 2025

Detecting and Containing Account Takeover with Darktrace

Account takeovers are rising with SaaS adoption. Learn how Darktrace detects deviations in user behavior and autonomously stops threats before they escalate.
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
Min Kim
Cyber Security Analyst
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24
Feb 2025

Thanks to its accessibility from anywhere with an internet connection and a web browser, Software-as-a-Service (SaaS) platforms have become nearly universal across organizations worldwide. However, with this growing popularity comes greater responsibility. Increased attention attracts a larger audience, including those who may seek to exploit these widely used services. One crucial factor to be vigilant about in the SaaS landscape is safeguarding internal credentials. Minimal protection on accounts can lead to SaaS hijacking, which could allow further escalations within the network.

How does SaaS account takeover work?

SaaS hijacking occurs when a malicious actor takes control of a user’s active session with a SaaS application. Attackers can achieve this through various methods, including employees using company credentials on compromised or spoofed external websites, brute-force attacks, social engineering, and exploiting outdated software or applications.

After the hijack, attackers may escalate their actions by changing email rules and using internal addresses for additional social engineering attacks. The larger goal of these actions is often to steal internal data, damage reputations, and disrupt operations.

Account takeover protection

It has become essential to have security tools capable of outsmarting potential malicious actors. Traditional tools that rely on rules and signatures may not be able to identify new events, such as logins or activities from a rare endpoint, unless they come from a known malicious source.

Darktrace relies on analysis of user and network behavior, tailored to each customer, allowing it to identify anomalous events that the user typically does not engage in. In this way, unusual SaaS activities can be detected, and unwanted actions can be halted to allow time for remediation before further escalations.

The following cases, drawn from the global customer base, illustrate how Darktrace detects potential SaaS hijack attempts and further escalations, and applies appropriate actions when necessary.

Case 1: Unusual login after a phishing email

A customer in the US received a suspicious email that seemed to be from the legitimate file storage service, Dropbox. However, Darktrace identified that the reply-to email address, hremployeepyaroll@mail[.]com, was masquerading as one associated with the customer’s Human Resources (HR) department.

Further inspection of this sender address revealed that the attacker had intentionally misspelled ‘payroll’ to trick recipients into believing it was legitimate

Furthermore, the subject of the email indicated that the attackers were attempting a social engineering attack by sharing a file related to pay raises and benefits to capture the recipients' attention and increase the likelihood of their targets engaging with the email and its attachment.

Figure 1: Subject of the phishing email.
Figure 1: Subject of the phishing email.

Unknowingly, the recipient, who believed the email to be a legitimate HR communication, acted on it, allowing malicious attackers to gain access to the account. Following this, the recipient’s account was observed logging in from a rare location using multi-factor authentication (MFA) while also being active from another more commonly observed location, indicating that the SaaS account had been compromised.

Darktrace’s Autonomous Response action triggered by an anomalous email received by an internal user, followed by a failed login attempt from a rare external source.
Figure 2: Darktrace’s Autonomous Response action triggered by an anomalous email received by an internal user, followed by a failed login attempt from a rare external source.

Darktrace subsequently observed the SaaS actor creating new inbox rules on the account. These rules were intended to mark as read and move any emails mentioning the file storage company, whether in the subject or body, to the ‘Conversation History’ folder. This was likely an attempt by the threat actor to hide any outgoing phishing emails or related correspondence from the legitimate account user, as the ‘Conversation History’ folder typically goes unread by most users.

Typically, Darktrace / EMAIL would have instantly placed the phishing email in the junk folder before they reached user’s inbox, while also locking the links identified in the suspicious email, preventing them from being accessed. Due to specific configurations within the customer’s deployment, this did not happen, and the email remained accessible to the user.

Case 2: Login using unusual credentials followed by password change

In the latter half of 2024, Darktrace detected an unusual use of credentials when a SaaS actor attempted to sign into a customer’s Microsoft 365 application from an unfamiliar IP address in the US. Darktrace recognized that since the customer was located within the Europe, Middle East, and Africa (EMEA) region, a login from the US was unexpected and suspicious. Around the same time, the legitimate account owner logged into the customer’s SaaS environment from another location – this time from a South African IP, which was commonly seen within the environment and used by other internal SaaS accounts.

Darktrace understood that this activity was highly suspicious and unlikely to be legitimate, given one of the IPs was known and expected, while the other had never been seen before in the environment, and the simultaneous logins from two distant locations were geographically impossible.

Model alert in Darktrace / IDENTITY: Detecting a login from a different source while the user is already active from another source.
Figure 3: Model alert in Darktrace / IDENTITY: Detecting a login from a different source while the user is already active from another source.

Darktrace detected several unusual login attempts, including a successful login from an uncommon US source. Subsequently, Darktrace / NETWORK identified the device associated with this user making external connections to rare endpoints, some of which were only two weeks old. As this customer had integrated Darktrace with Microsoft Defender, the Darktrace detection was enriched by Defender, adding the additional context that the user had likely been compromised in an Adversary-in-the-Middle (AiTM) phishing attack. AiTM phishing attacks occur when a malicious attacker intercepts communications between a user and a legitimate authentication service, potentially leading to account hijacking. These attacks are harder to identify as they can bypass security measures like MFA.

Following this, Darktrace observed the attacker using the now compromised credentials to access password management and change the account's password. Such behavior is common in account takeover incidents, as attackers seek to maintain persistence within the SaaS environment.

While Darktrace’s Autonomous Response was not fully configured on the customer’s SaaS environment, they were subscribed to the Managed Threat Detection service offered by Darktrace’s Security Operations Center (SOC). This 24/7 service ensures that Darktrace’s analysts monitor and investigate emerging suspicious activity, informing customers in real-time. As such, the customer received notification of the compromise and were able to quickly take action to prevent further escalation.

Case 3: Unusual logins, new email rules and outbound spam

Recently, Darktrace has observed a trend in SaaS compromises involving unusual logins, followed by the creation of new email rules, and then outbound spam or phishing campaigns being launched from these accounts.

In October, Darktrace identified a SaaS user receiving an email with the subject line "Re: COMPANY NAME Request for Documents" from an unknown sender using a freemail  account. As freemail addresses require very little personal information to create, threat actors can easily create multiple accounts for malicious purposes while retaining their anonymity.

Within the identified email, Darktrace found file storage links that were likely intended to divert recipients to fraudulent or malicious websites upon interaction. A few minutes after the email was received, the recipient was seen logging in from three different sources located in the US, UK, and the Philippines, all around a similar time. As the customer was based in the Philippines, a login from there was expected and not unusual. However, Darktrace understood that the logins from the UK and US were highly unusual, and no other SaaS accounts had connected from these locations within the same week.

After successfully logging in from the UK, the actor was observed updating a mailbox rule, renaming it to ‘.’ and changing its parameters to move any inbound emails to the deleted items folder and mark them as read.

Figure 4: The updated email rule intended to move any inbound emails to the deleted items folder.

Malicious actors often use ambiguous names like punctuation marks, repetitive letters, and unreadable words to name resources, disguising their rules to avoid detection by legitimate users or administrators. Similarly, attackers have been known to adjust existing rule parameters rather than creating new rules to keep their footprints untracked. In this case, the rule was updated to override an existing email rule and delete all incoming emails. This ensured that any inbound emails, including responses to potential phishing emails sent by the account, would be deleted, allowing the attacker to remain undetected.

Over the next two days, additional login attempts, both successful and failed, were observed from locations in the UK and the Philippines. Darktrace noted multiple logins from the Philippines where the legitimate user was attempting to access their account using a password that had recently expired or been changed, indicating that the attacker had altered the user’s original password as well.

Following this chain of events, over 500 emails titled “Reminder For Document Signed Agreement.10/28/2024” were sent from the SaaS actor’s account to external recipients, all belonging to a different organization within the Philippines.

These emails contained rare attachments with a ‘.htm’ extension, which included programming language that could initiate harmful processes on devices. While inherently not malicious, if used inappropriately, these files could perform unwanted actions such as code execution, malware downloads, redirects to malicious webpages, or phishing upon opening.

Outbound spam seen from the hijacked SaaS account containing a ‘.htm’ attachment.
Figure 5: Outbound spam seen from the hijacked SaaS account containing a ‘.htm’ attachment.

As this customer did not have Autonomous Response enabled for Darktrace / IDENTITY, the unusual activity went unattended, and the compromise was able to escalate to the point of a spam email campaign being launched from the account.

In a similar example on a customer network in EMEA, Darktrace detected unusual logins and the creation of new email rules from a foreign location through a SaaS account. However, in this instance, Autonomous Response was enabled and automatically disabled the compromised account, preventing further malicious activity and giving the customer valuable time to implement their own remediation measures.

Conclusion

Whether it is an unexpected login or an unusual sequence of events – such as a login followed by a phishing email being sent – unauthorized or unexpected activities can pose a significant risk to an organization’s SaaS environment. The threat becomes even greater when these activities escalate to account hijacking, with the compromised account potentially providing attackers access to sensitive corporate data. Organizations, therefore, must have robust SaaS security measures in place to prevent data theft, ensure compliance and maintain continuity and trust.

The Darktrace suite of products is well placed to detect and contain SaaS hijack attempts at multiple stages of an attack. Darktrace / EMAIL identifies initial phishing emails that attackers use to gain access to customer SaaS environments, while Darktrace / IDENTITY detects anomalous SaaS behavior on user accounts which could indicate they have been taken over by a malicious actor.

By identifying these threats in a timely manner and taking proactive mitigative measures, such as logging or disabling compromised accounts, Darktrace prevents escalation and ensures customers have sufficient time to response effectively.

Credit to Min Kim (Cyber Analyst) and Ryan Traill (Analyst Content Lead)

[related-resource]

Appendices

Darktrace Model Detections Case 1

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

SaaS / Compromise / Unusual Login and New Email Rule

SaaS / Compliance / Anomalous New Email Rule

SaaS / Unusual Activity / Multiple Unusual SaaS Activities

SaaS / Access / Unusual External Source for SaaS Credential Us

SaaS / Compromise / Login From Rare Endpoint While User is Active

SaaS / Email Nexus / Unusual Login Location Following Link to File Storage

Antigena / SaaS / Antigena Email Rule Block (Autonomous Response)

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (Autonomous Response)

Antigena / SaaS / Antigena Enhanced Monitoring from SaaS User Block (Autonomous Response)

List of Indicators of Compromise (IoCs)

176.105.224[.]132 – IP address – Unusual SaaS Activity Source

hremployeepyaroll@mail[.]com – Email address – Reply-to email address

MITRE ATT&CK Mapping

Cloud Accounts – DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS – T1078

Outlook Rules – PERSISTENCE – T1137

Cloud Service Dashboard – DISCOVERY – T1538

Compromise Accounts – RESOURCE DEVELOPMENT – T1586

Steal Web Session Cookie – CREDENTIAL ACCESS – T1539

Darktrace Model Detections Case 2

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

SaaS / Compromise / Unusual Login and Account Update

Security Integration / High Severity Integration Detection

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Compromise / Login From Rare Endpoint While User Is Active

SaaS / Compromise / Login from Rare High Risk Endpoint

SaaS / Access / M365 High Risk Level Login

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (Autonomous Response)

Antigena / SaaS / Antigena Enhanced Monitoring from SaaS user Block (Autonomous Response)

List of IoCs

74.207.252[.]129 – IP Address – Suspicious SaaS Activity Source

MITRE ATT&CK Mapping

Cloud Accounts – DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS – T1078

Cloud Service Dashboard – DISCOVERY – T1538

Compromise Accounts – RESOURCE DEVELOPMENT – T1586

Steal Web Session Cookie – CREDENTIAL ACCESS – T1539

Darktrace Model Detections Case 3

SaaS / Compromise / Unusual Login and Outbound Email Spam

SaaS / Compromise / New Email Rule and Unusual Email Activity

SaaS / Compromise / Unusual Login and New Email Rule

SaaS / Email Nexus / Unusual Login Location Following Sender Spoof

SaaS / Email Nexus / Unusual Login Location Following Link to File Storage

SaaS / Email Nexus / Possible Outbound Email Spam

SaaS / Unusual Activity / Multiple Unusual SaaS Activities

SaaS / Email Nexus / Suspicious Internal Exchange Activity

SaaS / Compliance / Anomalous New Email Rule

List of IoCs

95.142.116[.]1 – IP Address – Suspicious SaaS Activity Source

154.12.242[.]58 – IP Address – Unusual Source

MITRE ATT&CK Mapping

Cloud Accounts – DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS – T1078

Compromise Accounts – RESOURCE DEVELOPMENT – T1586

Email Accounts – RESOURCE DEVELOPMENT – T1585

Phishing – INITIAL ACCESS – T1566

Outlook Rules – PERSISTENCE – T1137

Internal Spear phishing – LATERAL MOVEMENT - T1534

Get the latest insights on emerging cyber threats

This report explores the latest trends shaping the cybersecurity landscape and what defenders need to know in 2025.

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
Min Kim
Cyber Security 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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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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