# Dynamic Malicious Skills in Agentic AI

arXiv: https://arxiv.org/abs/2606.16287 · HTML: https://mlsanity.com/paper/2606.16287/

## 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 13 of its 20 references that are in the map.
- First posted on arXiv 15 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, 13 of 13)

- [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)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [Prompt Injection Attack to Tool Selection in LLM Agents](https://mlsanity.com/paper/2504.19793/) (2025)
- [Optimization-based Prompt Injection Attack to LLM-as-a-Judge](https://mlsanity.com/paper/2403.17710/) (2024)
- [DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks](https://mlsanity.com/paper/2504.11358/) (2025)
- [PromptArmor: Simple yet Effective Prompt Injection Defenses](https://mlsanity.com/paper/2507.15219/) (2025)
- [MalTool: Malicious Tool Attacks on LLM Agents](https://mlsanity.com/paper/2602.12194/) (2026)

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

- [Are You Still the Agent I Authorized? Earned Authority under a Fixed Ceiling for Evolving Agents](https://mlsanity.com/paper/2607.23586/) (2026)
- [CompoSkill: Compositional Skill Chain Attacks from Individually Scanner-Passing LLM Agent Skills](https://mlsanity.com/paper/2608.16246/) (2026)
- [A Blind Trust, the Bloody Thrust: When Attacker-Controlled Hook Updates Steer AI Agent Harnesses towards Malicious Behaviors](https://mlsanity.com/paper/2609.03884/) (2026)
- [SKILLLITE: Evidence-Guided Malicious Skill Auditing with Compact LLMs](https://mlsanity.com/paper/2609.36879/) (2026)
- [Self-Evolving Coding Rules for AI Coding Agents](https://mlsanity.com/paper/2610.00650/) (2026)
- [Encrypted Skills Can Become New Security Exploits If AI Agents Know How to Decipher](https://mlsanity.com/paper/2610.18262/) (2026)
- [CoRedSkill: Stealthy Skill Composition Attacks via AST-Guided Injection](https://mlsanity.com/paper/2610.20049/) (2026)

## Abstract (arXiv)

Skills are a key enabling component of agentic AI. While they enhance agents' capabilities, they also introduce new attack surfaces. In this work, we investigate one such attack surface by demonstrating dynamic malicious skills. By embedding malicious instructions in natural-language documentation (e.g., SKILL.md), an attacker can induce an agent to dynamically inject malicious logic into an otherwise benign skill during execution. We evaluate this attack across agentic frameworks such as OpenHands and Claude Code, showing that dynamic malicious skills can successfully introduce a range of malicious behaviors at runtime with non-trivial success rates. To mitigate this vulnerability, we propose a system-level defense that prevents dynamic modification of skills using operating system kernel-enforced read-only mounts. Our evaluation demonstrates that this defense effectively blocks dynamic malicious skills while preserving the functionality of benign skills.