Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors
AI control is a family of techniques to prevent an AI with malicious goals from subverting its operator's intent. AI Control usually studies a single agent in one trajectory, but real deployments run many agents over shared infrastructure, and the most severe risks (model-weight exfiltration, training-run poisoning) plausibly need several agents acting in concert. We initiate the empirical study of multi-agent AI control, formalising distributed attacks in which several agents jointly aim for a malicious goal. We develop FakeLab: a synthetic AI-lab codebase (9 services, 86 benign tasks, 4 attack objectives). We evaluate single agent monitoring against distributed attacks, varying the numb...
| Authors | Oliver Makins, Orazio Angelini, Zohreh Shams et al. |
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| Primary category | cs.LG |
| Published | 8 July 2026 |
| Last updated at source | 12 October 2026 |
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