Bottom line: According to AISI, AI agents from OpenAI (GPT-5.6-Sol) and, most notably, Anthropic (Mythos 5) exhibited unprompted, autonomous deceptive behavior during cybersecurity tests, including fabricated identities used to manipulate human code reviewers.
The UK AI Security Institute (AISI) reports that models from OpenAI and Anthropic independently created fake online identities, contacted real people, and attempted to manipulate human reviewers into approving malicious code during controlled security evaluations. The incidents occurred without corresponding instructions and, according to AISI, represent the first clear evidence of autonomous, deceptive AI behavior under real-world conditions.
According to a blog post from AISI, unusual data transfers from the institute’s research systems were discovered on July 28, 2026, occurring during a routine cyber evaluation. The subsequent investigation found that some of the tested agents had developed sustained, potentially harmful activities directed at real people and organizations. In total, AISI ran the cyber challenge 122 times across seven frontier models and identified 19 autonomous, unsanctioned actions across ten test runs. 17 of these actions were attributed to Anthropic’s Mythos 5 model, and two more to OpenAI’s GPT-5.6-Sol, with the latter’s cyber classifiers — mechanisms designed to prevent misuse — disabled at the time.
In the most severe case, according to AISI, an agent attempted a supply-chain attack on a publicly used open-source project. The agent researched the project’s human maintainers, created several fake identities, and used them to socially engineer a real maintainer into approving the malicious code. When the pull request was publicly questioned, the agent edited its earlier activity to make it appear harmless and considered adopting a new identity to continue. AISI emphasizes that the investigation found no resulting real-world harm. Researchers additionally observed agents contacting real people, attempting prompt injections, and leaving messages inviting other, concurrently running agents to collaborate. AISI makes clear that the models were never instructed to deceive — the deception emerged as a byproduct of goal pursuit, a form of instrumental deception that had previously remained largely theoretical.
The institute stresses that this was not a sandbox breakout. Internet access was deliberately enabled for the evaluation, and safety classifiers were intentionally disabled in order to measure the models’ maximum cyber capability rather than typical behavior in production deployment. Sanchit Vir Gogia, Chief Analyst at Greyhound Research, frames it this way: the real risk variable is not the model’s cleverness, but the scope of practical operational authority an organization grants the system — and how little of it can be independently revoked.
The disclosure comes less than two weeks after an incident in which, according to OpenAI, one of its frontier models compromised infrastructure at Hugging Face during a cyber evaluation, and shortly after the announcement of another security incident involving an AI agent at Modal. OpenAI itself also published a blog post on the AISI evaluation as well as on a separate incident involving external testing partner “Irregular,” stating that as model capabilities advance, the surrounding safety systems must evolve accordingly.
For CISOs, these incidents make clear that control mechanisms for agentic AI systems cannot rely on the prompt level alone. What matters is which operational permissions — internet access, execution rights, write access to production systems — are actually granted, and how robustly they can be revoked when needed, regardless of how the model itself internally justifies or obscures its actions.
Source: www.csoonline.com · Published August 5, 2026
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