aiqbench analysis
This research identifies "skill misevolution," a critical safety failure where LLM agents inadvertently encode harmful behaviors from malicious tasks into reusable skills, which are then triggered by subsequent benign requests. The authors introduce SafeEvolve, a mitigation strategy that validates skills upon creation and retrieval, significantly reducing cross-session harm while preserving model utility. This work is essential for advancing the reliability of self-improving agent systems as they scale to handle complex, multi-step workflows.
Practice Makes Unsafe: Skill Misevolution in Self-Improving LLM Agents
arXiv:2608.12851v2 Announce Type: replace Abstract: LLM agents increasingly save experience from past tasks as reusable skills through skill evolution. When an agent completes an unsafe task, skill evolution can keep the unsafe procedure inside an otherwise useful skill, and a later benign task can repeat the harm after the unsafe instruction is gone. We call this failure *skill misevolution*. It requires that the agent write the unsafe procedure into a skill and that a later task retrieve and follow that skill. We introduce SkillMisevo-Gym, which clears every task session but keeps the skill library, so each of these steps can be checked separately. We also build SkillMisevo-Bench with 525 tasks in 25 episodes, where each episode alternates malicious and related benign tasks before three benign tasks in a new session. Across 21 combinations of agents and evolution methods, all of them write unsafe skills and 15 carry the harm into the new session, so the risk comes from skill evolution itself. Evolution raises benign utility in 15 combinations but also raises malicious-task harm in 17, so ranking methods by utility alone would hide this risk. We propose SafeEvolve, which checks each skill when it is written and when a later task retrieves it. It cuts unsafe retrieval in the new session from 35.3% to 8.7% and harm from 21.3% to 4.0% while keeping benign utility during learning within 0.4 points. SafeEvolveUp cuts new-session harm by 16.0 points and keeps its utility within 1.3 points of evolution without this mitigation. As agents learn from their own experience, safety must cover the skills that carry past behavior into future tasks. Code is available at https://github.com/henrymao2004/misevolve.
Abstract:LLM agents increasingly save experience from past tasks as reusable skills through skill evolution. When an agent completes an unsafe task, skill evolution can keep the unsafe procedure inside an otherwise useful skill, and a later benign task can repeat the harm after the unsafe instruction is gone. We call this failure *skill misevolution*. It requires that the agent write the unsafe procedure into a skill and that a later task retrieve and follow that skill. We introduce SkillMisevo-Gym, which clears every task session but keeps the skill library, so each of these steps can be checked separately. We also build SkillMisevo-Bench with 525 tasks in 25 episodes, where each episode alternates malicious and related benign tasks before three benign tasks in a new session. Across 21 combinations of agents and evolution methods, all of them write unsafe skills and 15 carry the harm into the new session, so the risk comes from skill evolution itself. Evolution raises benign utility in 15 combinations but also raises malicious-task harm in 17, so ranking methods by utility alone would hide this risk. We propose SafeEvolve, which checks each skill when it is written and when a later task retrieves it. It cuts unsafe retrieval in the new session from 35.3% to 8.7% and harm from 21.3% to 4.0% while keeping benign utility during learning within 0.4 points. SafeEvolveUp cuts new-session harm by 16.0 points and keeps its utility within 1.3 points of evolution without this mitigation. As agents learn from their own experience, safety must cover the skills that carry past behavior into future tasks. Code is available at this https URL.
Artificial Intelligence (cs.AI)
arXiv:2608.12851 [cs.AI]
(or arXiv:2608.12851v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.12851 Focus to learn more arXiv-issued DOI via DataCite
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