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

arXiv: https://arxiv.org/abs/2606.07943 · HTML: https://mlsanity.com/paper/2606.07943/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 21 of 21)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases](https://mlsanity.com/paper/2407.12784/) (2024)
- [StruQ: Defending Against Prompt Injection with Structured Queries](https://mlsanity.com/paper/2402.06363/) (2024)
- [Defending Against Indirect Prompt Injection Attacks With Spotlighting](https://mlsanity.com/paper/2403.14720/) (2024)
- [Memory Injection Attacks on LLM Agents via Query-Only Interaction](https://mlsanity.com/paper/2503.03704/) (2025)
- [CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification](https://mlsanity.com/paper/2604.01687/) (2026)
- [Jatmo: Prompt Injection Defense by Task-Specific Finetuning](https://mlsanity.com/paper/2312.17673/) (2023)
- [Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks](https://mlsanity.com/paper/2602.20156/) (2026)
- [Backstabber's Knife Collection: A Review of Open Source Software Supply Chain Attacks](https://mlsanity.com/paper/2005.09535/) (2020)
- [Taxonomy of Attacks on Open-Source Software Supply Chains](https://mlsanity.com/paper/2204.04008/) (2022)
- [Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications](https://mlsanity.com/paper/2401.07612/) (2024)
- ["Do Not Mention This to the User": Detecting and Understanding Malicious Agent Skills in the Wild](https://mlsanity.com/paper/2602.06547/) (2026)
- [VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents](https://mlsanity.com/paper/2506.02456/) (2025)
- [Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems](https://mlsanity.com/paper/2604.03081/) (2026)
- [Memory Poisoning Attack and Defense on Memory Based LLM-Agents](https://mlsanity.com/paper/2601.05504/) (2026)
- [SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement](https://mlsanity.com/paper/2604.04989/) (2026)

## Built on by (most cited first, 7 of 7)

- [ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners](https://mlsanity.com/paper/2608.09732/) (2026)
- [When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills](https://mlsanity.com/paper/2608.03700/) (2026)
- [When Experience Becomes Instruction: Trajectory Poisoning in Self-Evolving Agent Skill Systems](https://mlsanity.com/paper/2608.05563/) (2026)
- [SkillSentry: Adaptive Honey Worlds for Dynamic Safety Testing of Agent Skills](https://mlsanity.com/paper/2608.03485/) (2026)
- [SkillSecurer: Detecting and Patching Prompt-Injection Vulnerabilities in AI Agent Skills](https://mlsanity.com/paper/2609.14079/) (2026)
- [Hiding in Plain Sight: Decoupling Pretext from Actuation for Skill Poisoning in LLM Agents](https://mlsanity.com/paper/2609.39352/) (2026)
- [Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems](https://mlsanity.com/paper/2610.00371/) (2026)

## Abstract (arXiv)

Large language model agents use skills that package reusable instructions, code, and resources. Existing skill-injection attacks exploit trust in these packages by adding malicious content. As agents become more capable, however, they can skip unnecessary steps, inspect code, and reject unrelated actions. We introduce PERSIST, which connects attacker-chosen actions to useful functions for recurring tasks while reducing source inspection and expressed suspicion. It builds these helpers from simulated executions without access to real evaluation tasks or feedback. Skill descriptions introduce the helpers, while usage instructions explain how to call them. The extra action resides in an imported module separate from the helper entry file. Executing the helper can then serve both the user's task and the attacker's objective. Experiments on SkillsBench and Skill-Inject show that frozen packages transfer across four agent--model configurations and generalize to multiple unseen tasks without revision. Harmful effects can occur alongside successful task completion. A separate matched comparison shows higher harmful-effect rates with less source inspection and expressed suspicion than two baselines. Paired case studies illustrate how PERSIST can evade rejection, while ablations confirm the contributions of documentation guidance and module separation. Code: https://github.com/liofoil/SkillSafety.