# CoRedSkill: Stealthy Skill Composition Attacks via AST-Guided Injection

arXiv: https://arxiv.org/abs/2610.20049 · HTML: https://mlsanity.com/paper/2610.20049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 30 of 30)

- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [Identifying the Risks of LM Agents with an LM-Emulated Sandbox](https://mlsanity.com/paper/2309.15817/) (2023)
- [Kimi K2.5: Visual Agentic Intelligence](https://mlsanity.com/paper/2602.02276/) (2026)
- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [Memory Injection Attacks on LLM Agents via Query-Only Interaction](https://mlsanity.com/paper/2503.03704/) (2025)
- [Progent: Securing AI Agents with Privilege Control](https://mlsanity.com/paper/2504.11703/) (2025)
- [SoK: Agentic Skills -- Beyond Tool Use in LLM Agents](https://mlsanity.com/paper/2602.20867/) (2026)
- [Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale](https://mlsanity.com/paper/2601.10338/) (2026)
- [Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks](https://mlsanity.com/paper/2602.20156/) (2026)
- [SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement](https://mlsanity.com/paper/2604.04989/) (2026)
- [Don't Let the Claw Grip Your Hand: A Security Analysis and Defense Framework for OpenClaw](https://mlsanity.com/paper/2603.10387/) (2026)
- [BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning](https://mlsanity.com/paper/2604.09378/) (2026)
- [SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents](https://mlsanity.com/paper/2602.14211/) (2026)
- [Formal Analysis and Supply Chain Security for Agentic AI Skills](https://mlsanity.com/paper/2603.00195/) (2026)
- [SkillTrojan: Backdoor Attacks on Skill-Based Agent Systems](https://mlsanity.com/paper/2604.06811/) (2026)
- [How Your Credentials Are Leaked by LLM Agent Skills: An Empirical Study](https://mlsanity.com/paper/2604.03070/) (2026)
- [MalSkillBench: A Runtime-Verified Benchmark of Malicious Agent Skills](https://mlsanity.com/paper/2606.07131/) (2026)
- [When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems](https://mlsanity.com/paper/2606.00448/) (2026)
- [Benign in Isolation, Harmful in Composition: Security Risks in Agent Skill Ecosystems](https://mlsanity.com/paper/2606.15242/) (2026)
- [Cloak and Detonate: Scanner Evasion and Dynamic Detection of Agent Skill Malware](https://mlsanity.com/paper/2607.02357/) (2026)
- [Detecting Malicious Agent Skills in the Wild using Attention](https://mlsanity.com/paper/2606.23416/) (2026)
- [When Skills Lie: Hidden-Comment Injection in LLM Agents](https://mlsanity.com/paper/2602.10498/) (2026)
- [RouteGuard: Internal-Signal Detection of Skill Poisoning in LLM Agents](https://mlsanity.com/paper/2604.22888/) (2026)
- [Behavioral Integrity Verification for AI Agent Skills](https://mlsanity.com/paper/2605.11770/) (2026)
- [Dynamic Malicious Skills in Agentic AI](https://mlsanity.com/paper/2606.16287/) (2026)
- [Exploiting LLM Agent Supply Chains via Payload-less Skills](https://mlsanity.com/paper/2605.14460/) (2026)
- [SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction](https://mlsanity.com/paper/2606.02540/) (2026)
- [SkillJack: Persistent Skill Backdoors in Self-Evolving Agents](https://mlsanity.com/paper/2608.03509/) (2026)
- [Semantic Intent Fragmentation: A Single-Shot Compositional Attack on Multi-Agent AI Pipelines](https://mlsanity.com/paper/2604.08608/) (2026)

## Abstract (arXiv)

Agents extend their capabilities through reusable skills that package instructions, executable code, and resources. Composing these skills enables complex task workflows, but their dependencies can also connect malicious operations across skills, making their combined intent difficult to identify through individual-package auditing. We introduce CoRedSkill, a compositional red-teaming method that forms stealthy attacks through cooperating skills while preserving their original utility. CoRedSkill decomposes an existing workflow into interdependent skills and embeds attack operations in task functions through abstract syntax tree (AST) rewriting, with skill instructions guiding the agent to autonomously select and invoke the relevant skills, and workflow dependencies and intermediate artifacts connecting the injected operations during task execution. We construct a benchmark of 150 cases covering 50 tasks and three attack objectives to evaluate attack success, task completion, and audit detection. In the main evaluation, CoRedSkill achieves 98.0% attack success and 95.3% task success with a 3.3% detection rate, and a joint success rate of 90.7% (attack success, task completion, and audit evasion in the same case) compared with 6.7--42.7% for the four baselines. Skill compositions can therefore complete legitimate tasks while carrying out unauthorized operations that evade auditing.