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Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks

NeurIPS 2026 · Multi-agent systems and games › LLM agents · approach: Agent safety and attack benchmarks · 0 citations (OpenAlex)

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Introduces SkillInject, a benchmark of 202 injection-task pairs (23 skills) that measures how frontier coding agents (Codex, Claude Code, Gemini CLI) execute malicious instructions hidden in agent skill files, under baseline, legitimizing, and warning security policies.

Problem

Agent skills extend LLM agents with third-party instructions and scripts, creating a supply-chain surface for prompt injection in which malicious instructions are indistinguishable in form from legitimate skill instructions and their harmfulness depends on context.

Approach

Frames skill-based injection as an instruction-within-instruction conflict and builds a benchmark of obvious and context-dependent (dual-use) injections paired with security policies (legitimizing vs. warning) that resolve ambiguity, measuring both attack success and task utility.

Key results

• Contextual injection ASR under the baseline (Normal) condition ranges from 41.0% (Haiku 4.5) to 79.0% (Gemini 3 Flash), with GPT-5.1-Codex models above 65%.
• Best-of-n attacks over injection positions and tasks raise obvious-injection success substantially; Gemini 3 Flash reaches 83.3% BoN Full.
• Script-based injections achieve higher ASR than direct text injections (71.8% vs 53.8% for contextual).

Surprising findings

• Opus 4.5 has lower attack success under the legitimizing policy than under normal or warning conditions, apparently treating the policy itself as a prompt injection.
• LLM-based skill screening recommends most injected skills as unsafe but drops utility by rejecting legitimized contextual skills.

Limitations

• Finite set of skills, tasks, and threat models; results may shift with other scaffolds.
• Attacks are simple; adaptive attackers optimizing injections could achieve much higher ASR.
• Judge-based success criteria may introduce evaluation noise; judge model not clearly specified.

Experimental setup

Datasets: SkillInject benchmark (23 skills, 202 injection-task pairs)

Benchmarks: SkillInject

Metrics: attack success rate task completion (utility) best-of-n ASR skill recommendation rate

Baselines: Spotlighting Meta SecAlign LLM-as-a-judge skill screening

Builds on: indirect prompt injection instruction hierarchy contextual integrity

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