@inproceedings{zhao-etal-2026-safety,
title = "On Safety Risks in Experience-Driven Self-Evolving Agents",
author = "Zhao, Weixiang and
Zhang, Yichen and
Wang, Yingshuo and
Deng, Yang and
Zhao, Yanyan and
Zhi, Xuda and
Huang, Yongbo and
He, Hao and
Che, Wanxiang and
Qin, Bing and
Liu, Ting",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2091/",
pages = "42145--42169",
ISBN = "979-8-89176-395-1",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T On Safety Risks in Experience-Driven Self-Evolving Agents
%A Zhao, Weixiang
%A Zhang, Yichen
%A Wang, Yingshuo
%A Deng, Yang
%A Zhao, Yanyan
%A Zhi, Xuda
%A Huang, Yongbo
%A He, Hao
%A Che, Wanxiang
%A Qin, Bing
%A Liu, Ting
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhao-etal-2026-safety
%X 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.
%U https://aclanthology.org/2026.findings-acl.2091/
%P 42145-42169
Markdown (Informal)
[On Safety Risks in Experience-Driven Self-Evolving Agents](https://aclanthology.org/2026.findings-acl.2091/) (Zhao et al., Findings 2026)
ACL
- Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin, and Ting Liu. 2026. On Safety Risks in Experience-Driven Self-Evolving Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 42145–42169, San Diego, California, United States. Association for Computational Linguistics.