Safety in Self-Evolving Agents: A Survey
Large language models (LLMs) exhibit strong general capabilities, yet their parameters typically remain fixed after deployment, limiting learning from new interactions. In open-ended environments, this motivates self-evolving agents that continually update reusable state-including model parameters, memories, tool definitions, skills, and workflows-from data, feedback, and accumulated experience. This shift changes the safety problem: once experience becomes reusable state, past events become future causes, and information harmless in one context may later influence decisions with greater persistence, authority, or scope. Self-evolving agent safety therefore asks not only whether a respons...
| Authors | Jiahao Chen, Zhou Feng, Oubo Ma et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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