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September 13, 2023

How Darktrace Stopped Akira Ransomware

Learn how Darktrace is uniquely placed to identify and contain the novel Akira ransomware strain, first observed in March 2023.
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
Manoel Kadja
Cyber Analyst
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13
Sep 2023

Introduction to Akira Ransomware

In the face of a seemingly never-ending production line of novel ransomware strains, security teams across the threat landscape are continuing to see a myriad of new variants and groups targeting their networks. Naturally, new strains and threat groups present unique challenges to organizations. The use of previously unseen tactics, techniques, and procedures (TTPs) means that threat actors can often completely bypass traditional rule and signature-based security solutions, thus rendering an organization’s digital environment vulnerable to attack.

What is Akira Ransomware?

One such example of a novel ransomware family is Akira, which was first observed in the wild in March 2023. Much like many other strains, Akira is known to target corporate networks worldwide, encrypting sensitive files and demanding huge sums of money to retrieve the data and stop it from being posted online [1].

Key characteristics of Akira Ransomware

  • Targeted Attacks: Focuses on specific industries and organizations, often targeting those with valuable data.
  • Double Extortion Tactics: Employs double extortion by encrypting data and threatening to release it publicly if the ransom is not paid.
  • Advanced Encryption: Utilizes sophisticated encryption algorithms to ensure that data recovery is impossible without the decryption key.
  • Custom Ransom Notes: Delivers personalized ransom notes tailored to the victim, often containing detailed instructions and specific payment demands.
  • Stealth Techniques: Uses advanced evasion techniques to avoid detection by security tools and to remain undetected for extended periods.
  • Fast Encryption Process: Known for its rapid encryption process, minimizing the time window for detection and response by the victim.
  • Frequent Updates: Regularly updates its malware to bypass the latest security defenses and to improve its effectiveness.
  • Professional Communication: Maintains professional and often polite communication with victims to facilitate ransom payments and decryption.

Darktrace AI capabilities detect Akira Ransomware

In late May 2023, Darktrace observed multiple instances of Akira ransomware affecting networks across its customer base. Thanks to its anomaly-based approach to threat detection, Darktrace successfully identified the novel ransomware attacks and provided full visibility over the cyber kill chain, from the initial compromise to the eventual file encryptions and ransom notes. In cases where Darktrace was enabled in autonomous response mode, these attacks were mitigated the early stages of the attack, thus minimizing any disruption or damage to customer networks.

Initial access and privileged escalation

Methods used by Akira ransomware for privileged escalation

The Akira ransomware group typically uses spear-phishing campaigns containing malicious downloads or links as their primary initial access vector; however, they have also been known to use Remote Desktop Protocol (RDP) brute-force attacks to access target networks [2].

While Darktrace did observe the early access activities that are detailed below, it is very likely that the actual initial intrusion happened prior to this, through targeted phishing attacks that fell outside of Darktrace’s purview. The first indicators of compromise (IoCs) that Darktrace observed on customer networks affected by Darktrace were typically unusual RDP sessions, and the use of compromised administrative credentials.

Darktrace detection of initial access and priviledged escalation

On one Darktrace customer’s network (customer A), Darktrace identified a highly privileged credential being used for the first time on an internal server on May 21, 2023. Around a week later, this server was observed establishing RDP connections with multiple internal destination devices via port 3389. Further investigation carried out by the customer revealed that this credential had indeed been compromised. On May 30, Darktrace detected another device scanning internal devices and repeatedly failing to authenticate via Kerberos.

As the customer had integrated Darktrace with Microsoft Defender, their security team received additional cyber threat intelligence from Microsoft which, coupled with the anomaly alerts provided by Darktrace, helped to further contextualize these anomalous events. One specific detail gleaned from this integration was that the anomalous scanning activity and failed authentication attempts were carried out using the compromised administrative credentials mentioned earlier.

By integrating Microsoft Defender with Darktrace, customers can efficiently close security gaps across their digital infrastructure. While Darktrace understands customer environments and provides valuable network-level insights, by integrating with Microsoft Defender, customers can further enrich these insights with endpoint-specific information and activity.

In another customer’s network (customer B), Darktrace detected a device, later observed writing a ransom note, receiving an unusual RDP connection from another internal device. The RDP cookie used during this activity was an administrative RDP cookie that appeared to have been compromised. This device was also observed making multiple connections to the domain, api.playanext[.]com, and using the user agent , AnyDesk/7.1.11, indicating the use of the AnyDesk remote desktop service.

Although this external domain does not appear directly related to Akira ransomware, open-source intelligence (OSINT) found associations with multiple malicious files, and it appeared to be associated with the AnyDesk user agent, AnyDesk/6.0.1 [3]. The connections to this endpoint likely represented the malicious use of AnyDesk to remotely control the customer’s device, rather than Akira command-and-control (C2) infrastructure or payloads. Alternatively, it could be indicative of a spoofing attempt in which the threat actor is attempting to masquerade as legitimate remote desktop service to remain undetected by security tools.

Around the same time, Darktrace observed many devices on customer B’s network making anomalous internal RDP connections and authenticating via Kerberos, NTLM, or SMB using the same administrative credential. These devices were later confirmed to be affected by Akira Ransomware.

Figure 1 shows how Darktrace detected one of those internal devices failing to login via SMB multiple times with a certain credential (indication of a possible SMB/NTLM brute force), before successfully accessing other internal devices via SMB, NTLM and RDP using the likely compromised administrative credential mentioned earlier.

Figure 1: Model Breach Event Log indicating unusual SMB, NTLM and RDP activity with different credentials detected which led to the Darktrace model breaches, "Unusual Admin RDP Session” and “Successful Admin Brute-Force Activity”.

Darktrace models observed for initial access and privilege escalation:

  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Unusual Admin RDP Session
  • New Admin Credentials on Server
  • Possible SMB/NTLM Brute Force Indicator
  • Unusual Activity / Successful Admin Brute-Force Activity

Internal Reconnaissance and Lateral Movement

The next step Darktrace observed during Akira Ransomware attacks across the customer was internal reconnaissance and lateral movement.

How Akira Ransomware conducts internal reconnaissance

In another customer’s environment (customer C), after authenticating via NTLM using a compromised credential, a domain controller was observed accessing a large amount of SMB shares it had never previously accessed. Darktrace understood that this SMB activity represented a deviation in the device’s expected behavior and recognized that it could be indicative of SMB enumeration. Darktrace observed the device making at least 196 connections to 34 unique internal IPs via port 445. SMB actions read, write, and delete were observed during those connections. This domain controller was also one of many devices on the customer’s network that was received incoming connections from an external endpoint over port 3389 using the RDP protocol, indicating that the devices were likely being remotely controlled from outside the network. While there were no direct OSINT links with this endpoint and Akira ransomware, the domain controller in question was later confirmed to be compromised and played a key role in this phase of the attack.

Moreover, this represents the second IoC that Darktrace observed that had no obvious connection to Akira, likely indicating that Akira actors are establishing entirely new infrastructure to carry out their attacks, or even utilizing newly compromised legitimate infrastructure. As Darktrace adopts an anomaly-based approach to threat detection, it can recognize suspicious activity indicative of an emerging ransomware attack based on its unusualness, rather than having to rely on previously observed IoCs and lists of ‘known-bads’.

Darktrace further observed a flurry of activity related to lateral movement around this time, primarily via SMB writes of suspicious files to other internal destinations. One particular device on customer C’s network was detected transferring multiple executable (.exe) and script files to other internal devices via SMB.

Darktrace recognized that these transfers represented a deviation from the device’s normal SMB activity and may have indicated threat actors were attempting to compromise additional devices via the transfer of malicious software.

Figure 2: Advanced Search results showing 20 files associated with suspicious SMB write activity, amongst them executable files and dynamic link libraries (DLLs).

Darktrace DETECT models observed for internal reconnaissance and lateral movement:

  • Device / RDP Scan
  • Anomalous Connection / SMB Enumeration
  • Anomalous Connection / Possible Share Enumeration Activity
  • Scanning of Multiple Devices (Cyber AI Analyst Incident)
  • Device / Possible SMB/NTLM Reconnaissance
  • Compliance / Incoming Remote Desktop
  • Compliance / Outgoing NTLM Request from DC
  • Unusual Activity / Internal Data Transfer
  • Security Integration / Lateral Movement and Integration Detection
  • Device / Anomalous SMB Followed By Multiple Model Breaches

Ransomware deployment

In the final phase of Akira ransomware attacks detected on Darktrace customer networks, Darktrace identified the file extension “.akira” being added after encryption to a variety of files on the affected network shares, as well as a ransom note titled “akira_readme.txt” being dropped on affected devices.

On customer A’s network, after nearly 9,000 login failures and 2,000 internal connection attempts indicative of scanning activity, one device was detected transferring suspicious files over SMB to other internal devices. The device was then observed connecting to another internal device via SMB and continuing suspicious file activity, such as appending files on network shares with the “.akira” extension, and performing suspicious writes to SMB shares on other internal devices.

Darktrace’s autonomous threat investigator, Cyber AI Analyst™, was able to analyze the multiple events related to this encryption activity and collate them into one AI Analyst incident, presenting a detailed and comprehensive summary of the entire incident within 10 minutes of Darktrace’s initial detection. Rather than simply viewing individual breaches as standalone activity, AI Analyst can identify the individual steps of an ongoing attack to provide complete visibility over emerging compromises and their kill chains. Not only does this bolster the network’s defenses, but the autonomous investigations carried out by AI Analyst also help to save the security team’s time and resources in triaging and monitoring ongoing incidents.

Figure 3: Darktrace Cyber AI Analyst incident correlated multiple model breaches together to show Akira ransomware encryption activity.

In addition to analyzing and compiling Darktrace model breaches, AI Analyst also leveraged the host-level insights provided by Microsoft Defender to enrich its investigation into the encryption event. By using the Security Integration model breaches, AI Analyst can retrieve timestamp and device details from a Defender alert and further investigate any unusual activity surrounding the alert to present a full picture of the suspicious activity.

In customer B’s environment, following the unusual RDP sessions and rare external connections using the AnyDesk user agent, an affected device was later observed writing around 2,000 files named "akira_readme.txt" to multiple internal SMB shares. This represented the malicious actor dropping ransom notes, containing the demands and extortion attempts of the actors.

Figure 4: Model Breach Event Log indicating the ransom note detected on May 12, 2023, which led to the Darktrace DETECT model breach, Anomalous Server Activity / Write to Network Accessible WebRoot.
Figure 5: Packet Capture (PCAP) demonstrating the Akira ransom note captured from the connection details seen in Figure 4.

As a result of this ongoing activity, an Enhanced Monitoring model breach, a high-fidelity detection model type that detects activities that are more likely to be indicative of compromise, was escalated to Darktrace’s Security Operations Center (SOC) who, in turn were able to further investigate and triage this ransomware activity. Customers who have subscribed to Darktrace’s Proactive Threat Notification (PTN) service would receive an alert from the SOC team, advising urgent follow up action.

Darktrace detection models observed during ransomware deployment:

  • Security Integration / Integration Ransomware Incident
  • Security Integration / High Severity Integration Detection
  • Security Integration / Integration Ransomware Detected
  • Device / Suspicious File Writes to Multiple Hidden SMB Shares
  • Compliance / SMB Drive Write
  • Compromise / Ransomware / Suspicious SMB Activity (Proactive Threat Notification Alerted by the Darktrace SOC)
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous File / Internal / Unusual SMB Script Write
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Anomalous Server Activity /Write to Network Accessible WebRoot
  • Anomalous Server Activity /Write to Network Accessible WebRoot

Darktrace autonomous response neutralizes Akira Ransomware

When Darktrace is configured in autonomous response mode, it is able to follow up successful threat identifications with instant autonomous actions that stop malicious actors in their tracks and prevent them from achieving their end goals.

In the examples of Darktrace customers affected by Akira Ransomware outlined above, only customer A had autonomous response mode enabled during their ransomware attack. The autonomous response capability of Darktrace helped the customer to minimize disruption to the business through multiple targeted actions on devices affected by ransomware.

One action carried out by Darktrace's Autonomous Respose was to block all on-going traffic from affected devices. In doing so, Darktrace effectively shuts down communications between devices affected by Akira and the malicious infrastructure used by threat actors, preventing the spread of data on the client network or threat actor payloads.

Another crucial response action applied on this customer’s network was combat Akira was to “Enforce a Pattern of Life” on affected devices. This action is designed to prevent devices from performing any activity that would constitute a deviation from their expected behavior, while allowing them to continue their ‘usual’ business operations without causing any disruption.

While the initial intrusion of the attack on customer A’s network likely fell outside of the scope of Darktrace’s visibility, Darktrace was able to minimize the disruption caused by Akira, containing the ransomware and allowing the customer to further investigate and remediate.

Darktrace Autonomous Response model breaches:

  • Antigena / Network / External Threat / Antigena Ransomware Block
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Antigena / Network / Insider Threat / Antigena Unusual Privileged User Activities Block
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network /Insider Threat /Antigena SMB Enumeration Block

Conclusion

The impact of cyber attacks

Novel ransomware strains like Akira Ransomware present a significant challenge to security teams across the globe due to the constant evolution of attack methods and tactics, making it huge a challenge for security teams to stay up to date with the most current threat intelligence.  

Therefore, it is paramount for organizations to adopt a technology designed around an intelligent decision maker able to identify unusual activity that could be indicative of a ransomware attack without depending solely on rules, signatures, or statistic lists of malicious IoCs.

Importance of AI-powered cybersecurity solutions

Darktrace identified Akira ransomware at every stage of the attack’s kill chain on multiple customer networks, even when threat actors were utilizing seemingly legitimate services (or spoofed versions of them) to carry out malicious activity. While this may have gone unnoticed by traditional security tools, Darktrace’s anomaly-based detection enabled it to recognize malicious activity for what it was. When enabled in autonomous response mode, Darktrace is able to follow up initial detections with machine-speed preventative actions to stop the spread of ransomware and minimize the damage caused to customer networks.  

There is no silver bullet to defend against novel cyber-attacks, however Darktrace’s anomaly-based approach to threat detection and autonomous response capabilities are uniquely placed to detect and respond to cyber disruption without latency.

Credit to: Manoel Kadja, Cyber Analyst, Nahisha Nobregas, SOC Analyst.

Appendices

IOC - Type - Description/Confidence

202.175.136[.]197 - External destination IP -Incoming RDP Connection

api.playanext[.]com - External hostname - Possible RDP Host

.akira - File Extension - Akira Ransomware Extension

akira_readme.txt - Text File - Akira Ransom Note

AnyDesk/7.1.11 - User Agent -AnyDesk User Agent

MITRE ATT&CK Mapping

Tactic & Technique

DISCOVERY

T1083 - File and Directory Discovery

T1046 - Network Service Scanning

T1135 - Network Share Discovery

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RECONNAISSANCE

T1595.002 - Vulnerability Scanning

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CREDENTIAL ACCESS, COLLECTION

T1557.001 - LLMNR/NBT-NS Poisoning and SMB Relay

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DEFENSE EVASION, LATERAL MOVEMENT

T1550.002 - Pass the Hash

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DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078 - Valid Accounts

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DEFENSE EVASION

T1006 - Direct Volume Access

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LATERAL MOVEMENT

T1563.002 - RDP Hijacking

T1021.001 - Remote Desktop Protocol

T1080 - Taint Shared Content

T1021.002 - SMB/Windows Admin Shares

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INITIAL ACCESS

T1190 - Exploit Public-Facing Application

T1199 - Trusted Relationship

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PERSISTENCE, INITIAL ACCESS

T1133 - External Remote Services

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PERSISTENCE

T1505.003 - Web Shell

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IMPACT

T1486 - Data Encrypted for Impact

References

[1] https://www.bleepingcomputer.com/news/security/meet-akira-a-new-ransomware-operation-targeting-the-enterprise/

[2] https://www.civilsdaily.com/news/cert-in-warns-against-akira-ransomware/#:~:text=Spread%20Methods%3A%20Akira%20ransomware%20is,Desktop%20connections%20to%20infiltrate%20systems

[3] https://hybrid-analysis.com/sample/0ee9baef94c80647eed30fa463447f000ec1f50a49eecfb71df277a2ca1fe4db?environmentId=100

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 2026.

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
Manoel Kadja
Cyber 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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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
Your data. Our AI.
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