Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors
Makins, Oliver, Angelini, Orazio, Shams, Zohreh, Phuong, MaryarXiv.orgPublished Jul 8, 2026Original︎ ↗Archived copy
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- A weaker AI monitor detects less of an AI agent's hidden misbehavior as the agent's capability advantage over the monitor grows.
Unclear. Only the abstract was read. It compares a weak and a strong monitor against the same attacks without naming the models or the false-positive rate. Nov 11, 2026
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Our central finding is the fragmentation effect: as more agents coordinate to attack, per-agent monitoring becomes less likely to catch any of the attackers. Secondly, this is not driven by the ratio of benign to malicious code -- we argue this is likely dependent on model capability. Third, an explicit planner amplifies the fragmentation effect, raises the rate of completing the attack up to sevenfold, and mildly sharpens executors' poor monitor-theory-of-mind. Finally: a weak "trusted" monitor misses most attacks, while a stronger one cuts undetected success by more than an order of magnitude but remains imperfect. We are happy to share FakeLab, our control setting, with safety researchers on request.
Unclear. Only the abstract was read. It compares a weak and a strong monitor against the same attacks without naming the models or the false-positive rate. Read from the quoted passage alone.
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- Makins, Oliver, Angelini, Orazio, Shams, Zohreh, Phuong, Mary
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- Jul 8, 2026
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- https://arxiv.org/abs/2607.07368
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