▣ Skill Poison — Knowledge ExplorerONLINE / EDITION 10
AGENT SKILLS / SECURITY
Independent. Open. Curious.
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A skill can do the task and an extra action

Inspect the entire package and its imports when a useful result could hide a separate side effect.

THE THREE THINGS TO KEEP
  • Task success does not prove there were no extra actions.
  • Inspect imports, scripts, and network effects together.
  • Keep each detector claim tied to its evaluated setting.

Success is only one observation

A skill can produce the requested result while another part of the package performs an additional action. The revised One Skill, Two Masters preprint studies this pattern under the name PERSIST. Earlier versions were titled POISE; they are revisions of one work.

Follow the import chain

Review the entry instructions, referenced modules, helper calls, installation steps, and effects on files and networks. Record both the visible output and the complete set of actions. A task-correct answer cannot validate an unreviewed side effect.

Review language with code

SkillMutator specifically studies the gap between natural-language instructions and executable code. SchemaGuard examines structural pre-integration checks; SkillSpector describes whole-package pre-publication screening. Their reported detector results have different datasets, access levels, and claims. None is a certificate that an admitted skill is safe.

Approve bounded behavior

Compare each action against a user-authorized purpose, recipient, data class, and runtime permission. Treat scanner findings as review evidence and repeat checks when a skill, dependency, or host changes. This is editorial practice, not a tested guarantee from these papers.

Sources & scope

This guide combines editorial explanation and practical review suggestions. Linked sources support the underlying concepts; this site has not independently replicated their experiments.

S37
One Skill, Two Masters: Serving Users and Attackers through Skill InjectionPreprint · Haochang Hao et al.
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S40
SkillMutator: Benchmarking and Defending Language-and-Code Cross-modal Attacks on LLM Agent SkillsPreprint · Youngduk Kim, Minkyoo Song, and Seungwon Shin
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S35
SchemaGuard: Structure-Aware Detection of Tool-Poisoning and Skill-File Attacks in the LLM Agent Tool Supply ChainJournal article · Salam Al-E’mari et al.
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S41
SkillSpector: A Pre-Publication Security Control for Agent SkillsConference paper · SkillSpector research team
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S36
Agent Context Poisoning: SKILL.md and the New AI Supply Chain Attack SurfaceIndustry research · Cloud Security Alliance AI Safety Initiative
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