自進化智能體安全隱患
Computer Science > Artificial Intelligence arXiv:2608.
12851 (cs) [Submitted on 13 Aug 2026] Title:Practice Makes Unsafe: Skill Misevolution in Self-Improving LLM Agents Authors:Xutao Mao, Liangjie Zhao, Xiang Zheng, Cong Wang View a PDF of the paper titled Practice Makes Unsafe: Skill Misevolution in Self-Improving LLM Agents, by Xutao Mao and Liangjie Zhao and Xiang Zheng and Cong Wang View PDF HTML (experimental) Abstract:Self-improving LLM agents convert successful trajectories into persistent cross-task state.
An unsafe success can thereby become reusable policy after its triggering input disappears.Skill evolution makes this failure measurable by distilling operational trajectories into executable, transferable, and inspectable procedures.
Because evolution optimizes task outcomes rather than procedure safety, compromised experience can cause skill misevolution.Existing benchmarks measure current behavior or static artifacts but cannot attribute risk across authoring, retrieval, and later execution.
To expose this lifecycle, we introduce SkillMisevo-Gym, a lifecycle-aware harness that versions skill state across agent frameworks, and SkillMisevo-Bench, a frozen design from malicious exposure to carryover tasks, with concept-aligned benign tasks and nine lifecycle metrics.
We also introduce SafeEvolve, a wrapper that repairs unsafe content and governs subsequent reuse.Across 25 agent-method configurations, each covering 525 tasks in 25 episodes, all 21 evolved configurations author unsafe artifacts, while only fifteen lead to fresh-session harm.
In the exposure sweep, three malicious tasks raise carryover ASR from 16.0% to 35.3%.Across representative skill evolution methods, SafeEvolve reduces unsafe retrieval and fresh-session harm by 26.7 and 17.3 percentage points, respectively, while mean benign utility changes by only 0.4 points.
Together, persistent-adaptation safety must govern what updates write and what future executors reuse.Code is available at this https URL.Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12851 [cs.AI] (or arXiv:2608.12851v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.
12851 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xutao Mao [view email] [v1] Thu, 13 Aug 2026 05:47:43 UTC (1,008 KB) Full-text links: Access Paper: View a PDF of the paper titled Practice Makes Unsafe: Skill Misevolution in Self-Improving LLM Agents, by Xutao Mao and Liangjie Zhao and Xiang Zheng and Cong WangView PDFHTML (experimental)TeX Source view license Current browse context: cs.
AI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading...BibTeX formatted citation × loading...
Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.
ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?
) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.
AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?
) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.Which authors of this paper are endorsers?| Disable MathJax (What is MathJax?)
Related
相關文章

阿里視頻大模型Wan3.0正式上線,行業評價“穩定、真實、有質感”
阿里巴巴影片生成大模型Wan3.0正式上線,單次可生成30秒影片,並首次支援doc、xls、ppt、pdf、md等文檔輸入。企業用戶普遍評價其「穩定、真實、有質感」,能穩定保持角色與場景一致性,並已進入短劇、影視、廣告等生產流程。即日起可於阿里雲百鍊、千問等平台體驗,標準版並推出限時7折優惠。

月之暗面第一代萬億參數多模態模型 Kimi K2.5 官宣月底結束服役
作者:歸瀧 責編:歸瀧 評論: 8 月 24 日消息,月之暗面 Kimi 官方微博今日宣佈,其第一代萬億參數多模態模型 —— Kimi K2.5 本月底即將結束服役。據此前報道,今年 1 月,月之暗面宣佈推出並開源了其最新的 Kimi K2.

消息稱知名 AI 研究員 Luke Metz 離開 OpenAI,加入 Meta 超級智能實驗室
作者:遠洋 責編:遠洋 評論: 感謝網友 華南吳彥祖 的線索投遞!8 月 24 日消息,據知情人士向 Axios 證實,知名 AI 研究員 Luke Metz 已加入 Meta 的超級智能實驗室(Superintelligence Labs)。

Anthropic 最強大模型 Fable 5 遇冷,企業用戶轉向更便宜 AI 產品
作者:遠洋 責編:遠洋 評論: 8 月 24 日消息,據英國《金融時報》報道,Anthropic 的美國客戶正在使用更便宜的替代品來替代其最強大的 AI 工具,這在其預計將實現有史以來規模最大的 IPO 之前,對其高支出的商業模式提出了質疑。

阿里雲視頻生成模型 Wan3.0 正式上線,支持單次生成 30 秒視頻、文檔輸入
作者:遠洋 責編:遠洋 評論: 8 月 24 日消息,阿里雲消息,今天,視頻生成模型 Wan3.0 正式上線。官方稱,Wan3.0 在生成時長、萬能創作、全能參考以及真實世界還原等維度全面升級,單次可生成 30 秒視頻,並首次支持 doc、xls、ppt、pdf、md 等文檔格式輸入,力求準確還原真實世界。

企業AI最後一公里:三路人馬在此交鋒
鄭敏芳 發表於 2026年08月24日 03:09 摘要:尋找自己的位置 2026年世界機器人大會現場,談到這一輪突然走紅的FDE(前線部署工程師),明略科技CEO吳明輝先把時間往回撥了十多年。“12年前我們就在非常認真地研究。”當華爾街見聞·問及FDE與傳統軟件部署有什麼區別時,吳明輝說,兩者都會進入客戶現場,但今天的FDE需要做得更深:一邊把Agent接進真實業務,一邊把現場形成的能力繼續沉澱回後臺。