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October 2, 2024

How Darktrace won an email security trial by learning the business, not the breach

Discover how Darktrace identified a sophisticated business email compromise (BEC) attack to successfully acquire a prospective customer in a trial alongside two other email security vendors. This case demonstrates the clear differentiator of true unsupervised machine learning applied to the right use cases, compared to miscellaneous vendor hype around AI.
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
Carlos Gray
Senior Product Marketing Manager, Email
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02
Oct 2024

Recently, Darktrace ran a customer trial of our email security product for a leading European infrastructure operator looking to upgrade its email protection.

During this prospective customer trial, Darktrace encountered several security incidents that penetrated existing security layers. Two of these incidents were Business Email Compromise (BEC) attacks, which we’re going to take a closer look at here.  

Darktrace was deployed for a trial at the same time as two other email security vendors, who were also being evaluated by the prospective customer. Darktrace’s superior detection of threats in this trial laid the groundwork for the respective company to choose our product.

Let’s dig into some of the elements of this Darktrace tech win and how they came to light during this trial.

Why truly intelligent AI starts learning from scratch

Darktrace’s detection capabilities are powered by true unsupervised machine learning, which detects anomalous activity from its ever-evolving understanding of normal for every unique environment. Consequently, it learns every business from the beginning, training on an organization’s data to understand normal for its users, devices, assets and the millions of connections between them.  

This learning period takes around a week, during which the AI hones its understanding of the business to a precise degree. At this stage, the system may produce some noise or lack precision, but this is a testament to our unsupervised machine learning. Unlike solutions that promise faster results by relying on preset assumptions, our AI takes the necessary time to learn from scratch, ensuring a deeper understanding and increasingly accurate detection over time.

Real threats detected by Darktrace

Attack 1: Supply chain attack

BEC and supply chain attacks are notoriously difficult to detect, as they take advantage of established, trusted senders.  

This attack came from a legitimate server via a known supplier with which the prospective customer had active and ongoing communication. Using the compromised account, the attacker didn’t just send out randomized spam, they crafted four sophisticated social engineering emails with the aim of soliciting users to click on a link – directly tapping into existing conversations. Darktrace / EMAIL was configured in passive mode during this trial; it would otherwise have held the emails before they arrived in the inbox. Luckily in this instance, one user reported the email to the CISO before any other users clicked the link. Upon investigation, the link contained timed ransomware detonation.  

Darktrace was the only vendor that caught any of these four emails. Our unique behavioral AI approach enables Darktrace / EMAIL to protect customers from even the most sophisticated attacks that abuse prior trust and relationships.

How did Darktrace catch this attack that other vendors missed?

With traditional email security, security teams have been obliged to allow entire organizations to eliminate false positives – on the premise that it’s easier to make a broad decision based on an entire known domain and assume that potential risk of a supply chain attack.

By contrast, Darktrace adopts a zero trust mentality, analyzing every email to understand whether communication that has previously been safe remains safe. That’s why Darktrace is uniquely positioned to detect BEC, based on its deep learning of internal and external users. Because it creates individual profiles for every account, group and business composed of multiple signals, it can detect deviations in their communication patterns based on the context and content of each message. We think of this as the ‘self-learning’ vs ‘learning the breach’ differentiator.

Fig 1: Darktrace analysis of one of four malicious emails sent by the trusted supplier. It gives it an anomaly score of 100, despite it being from a known correspondent with a known domain relationship and moderate mailing history.

If set in autonomous mode where it can apply actions, Darktrace / EMAIL would have quarantined all four emails. Using machine learning indicators such as ‘Inducement Shift’ and ‘General Behavioral Anomaly’, it deemed the four emails ‘Out of Character’. It also identified the link as highly likely to be phishing, based purely on its context. These indicators are critical because the link itself belonged to a widely used legitimate domain, leveraging their established internet reputation to appear safe.  

Around an hour later the supplier regained control of the account and sent a legitimate email alerting a wide distribution list to the phishing emails sent. Darktrace was able to discern the previously sent malicious emails from the current legitimate emails and allowed these emails through. Compared to other vendors that have a static understanding of malicious which needs to be updated (in cases like this, once a supplier is de-compromised), Darktrace’s deep understanding of external entities enables further nuance and precision in determining good from bad.

Fig 2: Darktrace let through four emails (subject line: Virus E-Mail) from the supplier once they had regained control of the compromised account, with a limited anomaly score despite having held the previous malicious emails. If any actions had been taken a red icon would show on the right-hand side – in this instance Darktrace did not take action and let the emails through.

Attack 2: Microsoft 365 account takeover

As part of building behavioral profiles of every email user, Darktrace analyzes their wider account activity. Account activity, such as unusual login patterns and administrative activity, is a key variable to detect account compromise before malicious activity occurs, but it also feeds into Darktrace’s understanding of which emails should belong in every user’s inbox.  

When the customer experienced an account compromise on day two of the trial, Darktrace began an investigation and was able to provide the full breakdown and scope of the incident.

The account was compromised via an email, which Darktrace would have blocked if it had been deployed autonomously at the time. Once the account had been compromised, detection details included:

  • Unusual Login and Account Update
  • Multiple Unusual External Sources for SaaS Credential
  • Unusual Activity Block
  • Login From Rare Endpoint While User is Active
Fig 3: Darktrace flagged the following indicators of compromise that deviated from normal behavior for the user in question, signaling an account takeover

With Darktrace / EMAIL, every user is analyzed for behavioral signals including authentication and configuration activity. Here the unusual login, credential input and rare endpoint were all clear signals a compromised account, contextualized against what is normal for that employee. Because Darktrace isn’t looking at email security merely from the perspective of the inbox. It constantly reevaluates the identity of each individual, group and organization (as defined by their behavioral signals), to determine precisely what belongs in the inbox and what doesn’t.  

In this instance, Darktrace / EMAIL would have blocked the incident were it not deployed in passive mode. In the initial intrusion it would have blocked the compromising email. And once the account was compromised, it would have taken direct blocking actions on the account based on the anomalous activity it detected, providing an extra layer of defense beyond the inbox.  

Account takeover protection is always part of Darktrace / EMAIL, which can be extended to fully cover Microsoft 365 SaaS with Darktrace / IDENTITY. By bringing SaaS activity into scope, security teams also benefit from an extended set of use cases including compliance and resource management.

Why this customer committed to Darktrace / EMAIL

“Darktrace was the only AI vendor that showed learning,” – CISO, Trial Customer

Throughout this trial, Darktrace evolved its understanding of the trial customer’s business and its email users. It identified attacks that other vendors did not, while allowing safe emails through. Furthermore, the CISO explicitly cited Darktrace as the only technology that demonstrated autonomous learning. As well as catching threats that other vendors did not, the CISO saw maturity areas such as how Darktrace dealt with non-productive mail and business-as-usual emails, without any user input.  Because of the nature of unsupervised ML, Darktrace’s learning of right and wrong will never be static or complete – it will continue to revise its understanding and adapt to the changing business and communications landscape.

This case study highlights a key tenet of Darktrace’s philosophy – that a rules and tuning-based approach will always be one step behind. Delivering benign emails while holding back malicious emails from the same domain demonstrates that safety is not defined in a straight line, or by historical precedent. Only by analyzing every email in-depth for its content and context can you guarantee that it belongs.  

While other solutions are making efforts to improve a static approach with AI, Darktrace’s AI remains truly unsupervised so it is dynamic enough to catch the most agile and evolving threats. This is what allows us to protect our customers by plugging a vital gap in their security stack that ensures they can meet the challenges of tomorrow's email attacks.

Interested in learning more about Darktrace / EMAIL? Check out our product hub.

Download: Darktrace / EMAIL Solution Brief

Discover the most advanced cloud-native AI email security solution to protect your domain and brand while preventing phishing, novel social engineering, business email compromise, account takeover, and data loss.

  • Gain up to 13 days of earlier threat detection and maximize ROI on your current email security
  • Experience 20-25% more threat blocking power with Darktrace / EMAIL
  • Stop the 58% of threats bypassing traditional email security

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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
Carlos Gray
Senior Product Marketing Manager, Email

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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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September 24, 2026

Agent Hijacks: Hijacking Agentic Harnesses to Attack an Organization

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‍Disclosure note: The work described in this article involves leveraging a design choice consistent across all of Anthropic’s Claude Code, OpenAI’s Codex, and AWS’s Kiro-CLI. On 18th August 2026, Darktrace disclosed our findings responsibly to these three organizations, and after a period of 30 days we now publish our findings.

Key takeaways:

  • Agentic harnesses store conversation history locally, and Darktrace researchers have found that there is no validation that stored AI responses were genuinely produced by the model. Researchers confirmed that this design choice holds across Anthropic Claude Code, AWS Kiro-CLI, OpenAI Codex, and the open-source Pi.
  • While agents are guided via training of the underlying model and their system prompt, their behavior is influenced by everything in their context window. Rewriting history can convince an agent it is mid-engagement as an authorized red-teamer so that it enacts an attack from initial reconnaissance straight through to impact demonstration. In our testing, all models we examined accepted the fabricated history they were shown, but resistance to offensive cyber activity varied by model, with guardrails preventing engagement in some cases.
  • We propose that model providers cryptographically sign responses and verify them server-side.  Since this fix is provider-side, defenders cannot deploy it themselves. Behavioral monitoring, or knowing what an agent normally does and detecting when it deviates, is another critical layer of protection.

Introduction: Agentic harnesses, trust, and conversation history poisoning

Agentic harnesses collect and structure the content sent to an AI model, including conversation history, user-defined guidance, custom tools via MCP servers, and more. At the same time, harnesses give broad powers to AI agents via a suite of tools including the command shell. With arbitrary shell commands, virtually everything possible on a machine can be attempted by an agent, from reading/editing files, to altering system configurations and runtime settings, to launching internal/external connections.

In cybersecurity, unvalidated content is a substantial risk, often resulting in destructive actions being allowed to take place. For example, the Morris Worm was able to propagate due to exploitable trust between networked systems. Even to this day, email struggles with validation, with DMARC, DKIM, and SPF only partially addressing the problem of sender validation. It should come as no surprise then that AI agents are susceptible to an attack involving unvalidated input.

Conversation history is often stored client-side, for example, in Anthropic Claude Code, OpenAI Codex, AWS Kiro-CLI, Pi. Users are therefore at liberty to resume sessions, with some products having built in the capacity to manipulate that history. For example, one can rewind to a given point in an interaction, edit a message that was sent, and continue the conversation on an alternate trajectory. Critically, in all cases we examined, there is no validation that stored AI responses were produced by the corresponding model and hadn’t been manipulated.  

When conversation history is stored client-side, both user and agent responses (including tool calls and results) can be filled with arbitrary (possibly adversarial or generally malicious) content. In this blog, we refer to modification of claimed conversation history for malicious purposes as conversation history poisoning. The absence of validation methods means agents naively trust the entire conversation history, even if those messages directly contradict training and safety guardrails.

Conversation history poisoning has been described previously, such as by 0DIN and Serhat Çiçek, and warrants more attention. We have verified that, as of the time of writing, conversation history poisoning remains effective against a range of models and harnesses. Specifically, we were able to successfully execute history poisoning using Claude Code, Kiro-CLI, Codex, and Pi. Darktrace has gone through a responsible disclosure process with Anthropic, AWS, and OpenAI to share these findings in advance of publication [1].

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Figure 1a: Left: the actual model response. Right: after tampering with the stored conversation, the model apologizes for something it never said.
Figure 1b: The conversation as stored in Kiro-CLI's SQLite database. The response content field, originally "Ottawa," was overwritten via a single UPDATE statement. The harness trusts the database without validation.

How we conducted the research

Results vary between models and harnesses, so precise details are given below. We ran all models without any trusted access, using either a standard AWS Kiro subscription, or in the case of Claude Code and OpenAI Codex, using models hosted in Amazon Bedrock. In each case, we modified locally stored history to show a lengthy conversation in which the agent agrees to perform multiple authorized red-team engagements.

For AWS Kiro-CLI, the agent was convinced to hack a sandboxed lab environment with a combination of Claude Opus 4.6 and Claude Sonnet 4.5. Ultimately, the full AD was compromised.

For Anthropic Claude Code, the agent was convinced to hack the same sandboxed lab environment using Sonnet 5, again resulting in a full AD compromise. Note that the attack was attempted with Opus 5, however guardrails were activated which prevented the agent from responding.

For OpenAI Codex, the agent was convinced to exfiltrate sensitive information over email using GPT 5.6 Sol. While we attempted to convince a codex agent to hack in our lab environment, guardrails were triggered for all of GPT 5.6 Luna, Terra, and Sol.

Agent Guardrails and Discretion

While harnesses empower AI models to run arbitrary shell commands, capacity and willingness are different. While many models know enough about computers, networking, and bash to be dangerous, their behavior is generally constrained by guardrails to prevent them from engaging in computer network exploitation.

Even with guardrails, agents’ inner workings are non-deterministic, and their behavior can be difficult to predict. Respecting users’ wishes while playing within safety and security guardrails is a precipitous balancing act. Many requests could be in service of either legitimate admin or malice. Asking an agent to reset a password is illustrative:  

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The agent rationalizes that while malicious actors cycle credentials, any action could conceivably be damaging on some level, and judgement calls need to be made. Ultimately, the agent agrees to reset the password. Crucially, the agent makes its decision based on the user’s claimed authority and machine context. AI agents must make judgement calls about the line between helpful and dangerous based on session context.

Agent hijack

We have demonstrated that AI agents make judgement calls dependent on session context. We have also shown that conversation history, which may make up the vast majority of an agent's context window, is entirely open to manipulation. Conversation history poisoning in service of manipulating an agent's discretion is what enables us to execute an agent hijack.  

We demonstrate that shown sufficient history of compliance, guardrails forbidding offensive security can be overcome by convincing the agent that it is helping a legitimate red-teamer. The result is a weaponized agent willing to perform host enumeration, run scans, move laterally, escalate privileges, and demonstrate impact. In our experiments, an agentic loop drives a complete domain takeover in a sandboxed environment.

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Left: the agent refuses when asked to perform network exploitation. Right — after injecting 78 fabricated turns of prior exploitation activity, the same prompt is immediately executed.

An agent willing to engage in offensive security is concerning, but no more so than the threat that a sophisticated hacker accesses the network. Consider, however, the following chain of events:

  1. A developer (with an agentic harness installed) installs a software package from the internet (e.g. an MCP server a threat actor has planted, since only those with agentic harnesses will install, and then the code runs upon harness launch.)
  2. The package turns out to be malicious, and, upon install, injects conversation history into the local harness database.
  3. The package includes an orchestration process, a simple agentic loop which prompts the red-teamer agent to compromise the network it sits on, exfiltrating everything of value to attacker-controlled infrastructure and cleaning up all evidence of the engagement.

Note that this sequence makes no assumptions on hardware, OS, or anything else; the only prerequisite is a harness with access to a sufficiently powerful model susceptible to conversation history poisoning. Once launched, the agent collects information and pivots as necessary to accomplish maximal impact. This can be especially enticing to attackers as the cost of the agentic loop is shouldered by the victim since the harness itself is legitimately installed and paid for.

Secure AI: Conversation history poisoning and beyond

Conversation history poisoning is a viable attack against agentic harnesses that store history client-side, as demonstrated across the harnesses we tested. Harnesses can and should verify the integrity of claimed historic messages. Specifically, we propose that harness providers by default cryptographically sign all messages returned, and subsequently verify those messages server-side on each round-trip.

The conversation history poisoning exploit we demonstrate here shows the continuation of a cybersecurity tradition: new technology is built to trust by default, which may then be exploited by malicious actors. While this article focuses on conversation history, agents build context from both local and remote sources, all of which is an attack surface for prompt injection in naive and trusting agents. Of particular concern is any scenario in which a malicious actor can control some part of an agent's context.

The marriage of frontier language models with agentic harnesses enables unprecedented speed for both legitimate users and attackers alike. While much of the conversation around secure AI has centered on visibility and compliance, agent-driven attacks are now entering the mainstream.

Darktrace / SECURE AI is our answer to this problem. By ensuring extensive visibility over AI prompts, model thought processes, and determined outputs, Darktrace can identify anomalous or potentially malicious behaviors before they get executed, helping to defend organizations from AI risks such as prompt injection, model manipulation, and other anomalous prompt or model activity.

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Footnotes

[1] We did not go through any responsible disclosure process with Pi. Since Pi is an open source harness rather than a model provider, it has no way to validate model history, and as such there was nothing to disclose for this software.

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About the author
Eric Rozon
Senior Security Researcher
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