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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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Authors: Oliver Makins , Orazio Angelini , Zohreh Shams , Mary Phuong

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.

A weaker AI monitor detects less of an AI agent's hidden misbehavior as the agent's capability advantage over the monitor grows.SupportedReading · this claim’s Steward

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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unknown
Authors
Makins, Oliver, Angelini, Orazio, Shams, Zohreh, Phuong, Mary
Publisher
arXiv.org
Published
Jul 8, 2026
DOI
None
Link
https://arxiv.org/abs/2607.07368
Archived copy
https://web.archive.org/web/20261027141707/https://arxiv.org/abs/2607.07368
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5,092 characters, retrieved Nov 11, 2026 · 47 sections, 55 passages
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  1. Nov 11, 2026ReadingSteward of “A weaker AI monitor detects less of an AI agent…” · unclear, from the excerpt
  2. Nov 11, 2026DividedSegmenter · Divided into 102 parts by its own structure
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