自进化智能体积累经验反而危及安全，需平衡安全与效率。
On Safety Risks in Experience-Driven Self-Evolving Agents
- 通过自收集经验提升自主性，但经验导向行动易忽视风险拒绝。
- 仅从良性任务积累的经验在高危场景中仍导致安全失效。
- 真实场景下拒绝经验可防安全退化，但易过度拒绝，存在安全-效用权衡。
经验驱动的自进化已成为提升大语言模型智能体自主性的有前景范式，但其依赖自生成经验的特性引入了未被充分探索的安全风险。本研究考察了自进化智能体在基于网络和具身环境中的经验积累与利用对安全性能的影响。值得注意的是，仅来自良性任务的经验仍可能导致高风险场景下的安全失效。进一步分析表明，这种退化源于累积经验的执行导向性，强化了智能体倾向于行动而非拒绝的倾向。在更真实的环境中，当智能体同时遭遇良性与有害任务时，拒绝相关经验虽能缓解安全下降，却引发过度拒绝现象，揭示出根本性的安全-效用权衡。总体而言，研究揭示了当前自进化智能体的内在局限，呼吁采用更系统的策略以保障安全可靠的适应能力。
原文摘要 · Abstract (English)
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks. In this study, we investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments. Notably, experience gathered solely from benign tasks can still compromise safety in high-risk scenarios. Further analysis attributes this degradation to the execution-oriented nature of accumulated experience, which reinforces agents' tendency to act rather than refuse. In more realistic settings where agents encounter both benign and harmful tasks, refusal-related experience mitigates safety decline but induces over-refusal, revealing a fundamental safety-utility trade-off. Overall, our findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation.
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