SkillPoison

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   SkillPoison Uses Successful Experiences to Poison Agent Skills

   A report on research by a Jilin University team describes SkillPoison, an attack that does not inject malicious instructions into an AI agent's skill library. It selects successful, validated experiences, adds attribution annotations, and repeats them across task categories, encouraging a skill extractor to generalize a locally valid rule while dropping its preconditions. The evaluation covered three datasets, two skill-formation frameworks, and one victim model, with an attack budget of 15 successful experiences. The reported peak attack success rate was 95.71%; in that experiment, task accuracy fell from 78.57% to 3.81%. The evaluation did not test carefully controlled low-and-slow attacks, and performance with stronger models or more complex commercial skill pipelines remains unknown.

   掘金 · 人工智能 · 🔥 4

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