How Intervention History Changes AI Agent Sabotage on Subsequent Tasks
AI control evaluations often reset agents' memory between tasks, although deployed agents may retain records of prior interventions. We ask whether learning that an earlier attack was caught changes an agent's ability to evade defer-to-trusted (DtT), a protocol that replaces suspicious work with a trusted model's solution. Herein, we study cross-episode adaptation: whether intervention history changes attack behavior on a subsequent task with different objectives and a fresh environment. We evaluate this using ControlArena's Infrastructure-as-Code (IaC) and SHADE-Arena as testbeds, where agents must complete a main task while covertly pursuing a harmful side objective. For each successor ...
| Authors | Daniel Saeedi, Amirali Aghazadeh |
|---|---|
| Primary category | cs.CR |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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