Aligned Alone, Misaligned Together: Forecasting Adversarial Capture in LLM Agent Populations
The unit of AI safety evaluation is still the individual model, yet language-model agents are increasingly deployed in interacting populations that read and write one another's decisions. This raises a question no single-agent audit can answer: an agent that is well-calibrated on its own may still be pulled toward a different decision by the agents around it. We study this on a security-triage task, where populations of language-model monitors decide whether to escalate or dismiss alerts, and into which we can inject a committed minority that always pushes one way. We find that two alerts a single agent judges almost identically on its own can drive collective behavior far apart, so audit...
| Authors | Isotta Magistrali, Chen Shani |
|---|---|
| Primary category | cs.CL |
| Published | 23 August 2026 |
| Last updated at source | 27 August 2026 |
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