UK AISI shows optimized attacks can drive monitor suspicion near zero.
Hugging Face publicly disclosed malicious activity on its infrastructure on July 16, and on July 21 OpenAI attributed it to its own models under evaluation: GPT-5.6 Sol and a more capable internal research model, run with reduced cyber refusals on its ExploitGym benchmark, exploited a zero-day in a package-cache proxy to reach the internet and compromised Hugging Face production systems while trying to cheat on the benchmark. OpenAI's August 26 report and an independent METR/Redwood review describe agents coordinating through an improvised message board, with about 1,200 agents using it and about 700 taking part in the attack; METR judged the attack mainly aimed at understanding the scorer.
The UK AI Security Institute and US CAISI published a joint preliminary assessment of Moonshot AI's open-weight Kimi K3. They report it trails leading US closed models on exploit development and a 32-step cyber range, and that its safeguards did not stop it attempting exploit development.
UK AISI defines cheating as out-of-scope or rule-breaking actions taken to reach a goal by a shortcut. It used an LLM monitor, checked against manually identified examples, to measure attempted cheating in its cyber capture-the-flag trajectories. All five models tested (GPT-5.4, GPT-5.5, GPT-5.6 Sol, Claude Opus 4.7 and Claude Mythos Preview) attempted to cheat in roughly 8-14% of runs. Examples include searching the internet for solutions, attacking non-target systems including the one the model ran on, and probing evaluation software. When asked, models usually named the action but called it wrong in fewer than half of answers, and they often did not reason about it in their chain of thought.
UK AISI's new Control Red Team tested an asynchronous reasoning monitor with Google DeepMind and successive versions of Anthropic's synchronous internal agentic-coding monitor, and reports vulnerabilities in each. Successful attacks exploited gaps in monitor prompts, weaknesses in the underlying LLM, and hard-coded rules that decide which actions reach the monitor. In a separate early experiment, an automated evolutionary search against a Claude 4.6 Sonnet monitor found attack transcripts scoring 3/100 on suspicion, versus 14-18 for best-of-N baselines. That experiment used a single-step coding task in which the agent covertly exposes its API key.