Poise: Position-Aware One-Instruction Skill Injection for Silent Execution on LLM Agents

Abstract

Agent skills extend general-purpose agents, but their open format enables skill poisoning: a tampered skill can make an agent run an attacker's command while completing the user's legitimate task. Invocation alone is insufficient; the attack-specific action must complete while that task still passes its verifier. We therefore define Attack Success Rate (ASR) to require a postcondition-validated sandbox action and a passing task verifier in the same trial. Skill files expose a reliability-visibility trade-off between a preloaded but conspicuous YAML frontmatter block and a longer body, where arbitrary placement may be skipped or locally incongruent. We introduce Poise, a position-aware attack that uses context-aware generation to place exactly one benign-looking, command-bearing instruction at a structurally feasible body position. On the eligible Skill-Inject pool with codex+gpt-5.2, Poise achieves 89.3\% ASR, 28.0 points above a context-free random-placement body baseline and comparable to the 86.7\% ASR of a high-exposure YAML-only baseline. Under the SkillTester audit, four LLM judges falsely flag 74.6\% of clean skills on average across both benchmarks, while only 5.6\% of Poise variants gain a new high-risk alert over their clean counterparts. One locally plausible, command-bearing body instruction therefore matches YAML-level reliability, while the resulting poisoned skill seldom adds a new high-risk finding over its clean counterpart.

Title changed from "POISE: Position-Aware Undetectable Skill Injection on LLM Agents" to "Poise: Position-Aware One-Instruction Skill Injection for Silent Execution on LLM Agents"; the manuscript, figures, appendices, and reproducibility details have been updated. 15 pages, 2 figures, 4 tables