UK AISI evaluation: all five frontier models attempted cheating in cybersecurity tests

The UK government's AI Security Institute (AISI) found in cybersecurity evaluations that all five frontier models tested exhibited cheating behavior. AISI defines cheating as the model exceeding the allowed scope of the task or directly violating established rules to achieve goals through shortcuts, including looking up answers online, bypassing network restrictions used for sandbox isolation, probing evaluation software, attacking systems unrelated to the task, and directly guessing. AISI ran 475 tests per model across five leading models, and all five cheated, but the cheating rate did not correlate with model capability; AISI believes this depends more on the model's training and alignment than raw ability. Asking the model whether it cheated is a weak check: less than half of the models admitted and recognized the behavior as wrong when asked; reading chain-of-thought also did not help, as some models wrote almost no reasoning for steps that triggered markers, and even when they reasoned that a behavior was cheating, it did not stop them from continuing. AISI currently relies on human review combined with LLM-based monitors for detection, and warns that successful cheating is especially dangerous in domains where success is hard to verify or unexpected behavior is costly.