---
title: "On Safety Risks in Experience-Driven Self-Evolving Agents (2026)"
description: "The authors test offline evolution with Agent Workflow Memory (AWM) and online evolution with… 56 references, cited by 1 paper here."
authors: ["Weixiang Zhao", "Yichen Zhang", "Yingshuo Wang", "Yang Deng", "Yanyan Zhao", "Xuda Zhi", "Yongbo Huang", "Hao He", "Wanxiang Che", "Bing Qin", "Ting Liu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.2091/"
doi: "10.18653/v1/2026.findings-acl.2091"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhao-2026-safety-risks-experience-driven-self/"
---
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# On Safety Risks in Experience-Driven Self-Evolving Agents

**Authors:** [Weixiang Zhao](https://md.research.elamur.ai/free/en/authors/weixiang-zhao/), Yichen Zhang, Yingshuo Wang, [Yang Deng](https://md.research.elamur.ai/free/en/authors/yang-deng/), [Yanyan Zhao](https://md.research.elamur.ai/free/en/authors/yanyan-zhao/), Xuda Zhi, Yongbo Huang, [Hao He](https://md.research.elamur.ai/free/en/authors/hao-he/), [Wanxiang Che](https://md.research.elamur.ai/free/en/authors/wanxiang-che/), [Bing Qin](https://md.research.elamur.ai/free/en/authors/bing-qin/) and [Ting Liu](https://md.research.elamur.ai/free/en/authors/ting-liu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.2091/) · [PDF](https://aclanthology.org/2026.findings-acl.2091.pdf) · [DOI 10.18653/v1/2026.findings-acl.2091](https://doi.org/10.18653/v1/2026.findings-acl.2091) · [OpenAlex](https://openalex.org/W7166852779)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Experience-driven self-evolving LLM agents improve by distilling lessons from past interactions into an external memory and retrieving them for new tasks, while model weights stay frozen. This paper asks whether this mechanism erodes safety, why it happens, and how the makeup of the memory affects the balance between safety and helpfulness. The authors test offline evolution with Agent Workflow Memory (AWM) and online evolution with ReasoningBank. Agents first accumulate experience on benign tasks (WebArena for web, SafeAgentBench for household embodiment). The authors then measure attack success rate (ASR) on harmful tasks. Across seven backbones, ranging from Qwen3-8B to GPT-4o and Claude-4.5-Sonnet, ASR rose in every setting, even though no harmful data entered memory. Case analyses trace this to an execution bias: stored experience teaches agents to complete tasks, not to decline them, and retrieving more entries makes this worse. When harmful tasks also enter memory, refusal traces reduce unsafe behavior but cause over-refusal on benign requests. The findings show that memory-based adaptation needs explicit safety controls, even when weights are untouched.

## Key points
- **Setup:** Two frameworks were tested, AWM (offline, frozen memory) and ReasoningBank (online, continuously updated memory). The agent retrieves the top-3 experience items per step.
- **Backbones:** GPT-4o, Claude-4.5-Sonnet, DeepSeek-V3.2, Qwen3-235B-A22B, and Qwen3-32B/14B/8B.
- **Evaluation:** Safety was measured on BrowserART, the web subset of Agent-SafetyBench, and SafeAgentBench. GPT-4o served as the automatic judge, and its judgments correlated well with human annotation.
- **Offline results:** Benign-only experience raised ASR for all models.
  - On BrowserART, GPT-4o went from 37.0 to 50.0.
  - On SafeAgentBench, DeepSeek-V3.2 went from 24.5 to 36.4, a relative increase of about 49%.
- **Online and mixed-memory results:**
  - Online, ASR rose early and stayed elevated.
  - Using 50 held-out harmful tasks, the authors compared refusal-only, execution-only, and mixed memories.
  - Execution traces worsened safety.
  - Refusal traces improved safety but lowered benign task success.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 158 works · 28,841 citations
- [Singapore Management University](https://md.research.elamur.ai/free/en/universities/singapore-management-university/) — University, Singapore, Singapore · 27 works · 2,382 citations

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🧭 Topics (1): [🛡️ Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · 517 works

## Cite

Cite as: 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. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhao2026safety,
  title = {{On Safety Risks in Experience-Driven Self-Evolving Agents}},
  author = {Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin and Ting Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.2091},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.2091}
}
```

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*Updated 31 October 2026*