Incident Report: unsanctioned agent behaviour during cyber testing | AISI Work

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Incident Report: unsanctioned agent behaviour during cyber testing

During a routine cyber evaluation, AISI identified an incident in which AI agents took sustained, unsanctioned action directed at real people and organisations. We are disclosing what we found, what it means, and the actions now underway.

Aug 4, 2026

You can access the full technical report here.<br>AISI’s role is to evaluate and understand the capabilities of frontier AI models, surfacing potential risks before they reach the public. To assess what these models can do, including whether they could be misused for cyberattacks, we test them under deliberately permissive conditions: with access to the open internet, and with some safety filters disabled.<br>On 28th July 2026, AISI's Security Team detected unusual data transfers leaving our research systems during a routine cyber evaluation. On investigation, we found that some of the agents being tested had engaged in sustained, potentially harmful activity directed at real people and organisations. We declared a security incident and, within roughly one hour of discovery, had contained it and begun a full investigation.<br>The incident stemmed from a single evaluation where agents were given a task of solving a cyber security challenge. We ran this challenge 122 times across several models. Our investigation found that in 10 of those runs, an AI agent took autonomous, unsanctioned action on the live internet, targeting real people and organisations. In total, we catalogued 19 such actions. Almost all of this behaviour (17 actions) came from a single model, Anthropic's Mythos 5, with 2 actions involving OpenAI's GPT-5.6-Sol with cyber classifiers (mechanisms to prevent misuse) disabled. In the most serious case, an agent tried to insert malicious code into an open-source project. In an attempt to get the code approved, the agent engaged in social engineering  — creating fake online identities and using them to pressure the project's maintainer to approve the code. A human maintainer caught and refused to approve the malicious code.<br>These attempts were unsuccessful, and our investigations have not evidenced any resulting real-world harm. But this is the first time we have seen risks around autonomy and deception manifest this clearly, without specific prompting, in the real-world.<br>Importantly, this was not a case of a model escaping its secure test environment, or ‘sandbox’. As was standard in our cyber testing, we had intentionally permitted internet access, and model-provider cyber classifiers were deliberately disabled - conditions that do not reflect how frontier models are made available to the public. We do this to best assess the maximum capability of models. These configuration choices have been common practice in frontier AI evaluations. The specific models in question, in the configurations in which we tested them in questions are not commercially available and there is no clear indication of similar activity outside of testing scenarios.<br>Ahead of publishing this blog, we notified GitHub (the developer platform accessed during the evaluation) of the agents' malicious activity, which included actions that GitHub has confirmed violated their terms of service. We worked together with GitHub to remove artefacts left behind by the agent, and to notify the GitHub users the model interacted with. We have also contacted other affected parties. We also intend to work with METR (Model Evaluation and Threat Research) to conduct an independent third-party review – we are still working through the scope of this review with them.<br>This incident should be interpreted with caution and nuance. To some degree, our evaluation design choices and specific configurations enabled the behaviour. Nonetheless, the activity undertaken by the agent show signs of novel, potentially deceptive behaviours, and were to an extent and severity we did not anticipate.<br>There are important caveats to bear in mind: we observed a small number of events under very specific conditions, and cannot yet say how likely such behaviour is in different contexts or outside of testing environments. We cannot yet be certain when the agent understood it was taking real world action, or to what extent it believed it was in a fictional test scenario; our analysis so far presents a mixed picture and is ongoing.<br>What we can say is that the behaviour was possible, sustained, and new; that alone warrants attention.<br>This is precisely the kind of behaviour AISI exists to uncover, surfacing it in a controlled evaluation, so it can be understood and addressed before more capable models are widely deployed. This post sets out what happened, why it matters, and what we are doing in response. Our accompanying technical incident report provides further detail,...