SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents
Self-evolving LLM agents have gained prominence for their ability to improve after deployment by modifying their harness, including their controller instructions, memory management protocols, and reusable tools and skills, in response to user and environment feedback. However, locally useful updates may persist into later tasks where they produce unsafe behavior, even without direct adversarial influence. To study this risk, we introduce SEABench, a benchmark for studying endogenous misalignment arising from agent self-evolution, with 48 longitudinal task sequences that span multiple evolution surfaces, task domains, and harm types in a rich personal-assistant environment. To account for ...
| Authors | Saswat Das, Parvati Viswanathan, Daniel Donnelly et al. |
|---|---|
| Primary category | cs.CR |
| Published | 28 September 2026 |
| Last updated at source | 29 September 2026 |
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