Bypassing Agent Skill Scanners With Adversarial Chains
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Key Takeaways
- ColluSkill achieves a 96.0% attack success rate by distributing malicious intent across multiple connected skills.
- The method uses LLM-based chain planning to maintain coherence and iterative rewriting to evade scanners.
- ChainGuard provides a defense mechanism that lowers the attack success rate to 22.5%.
- Current defense systems leave 99.5% of benign workflows unaffected while mitigating most adversarial attacks.
Summary & Methodology Analysis
ColluSkill operates by breaking a monolithic malicious intent into smaller, interdependent sub-payloads. Each sub-payload is packaged as a distinct skill designed to look harmless in isolation. The system uses LLM-based chain planning, a method where the language model determines the sequence of actions and predicts intermediate outputs, to ensure the distributed components maintain functional coherence across execution handoffs and artifact passing. This architecture effectively evades scanners that monitor only single skills at a time. Once sub-skills are submitted, ColluSkill employs an iterative feedback loop, rewriting only the flagged components based on scanner output until the entire chain passes inspection. The paper tests this against multiple scanners, including CISCO Skill Scanner, SkillFortify, Auditor, SlowMist, Vetter, and SkillSpector. Experimental results demonstrate that this modular approach is highly effective, yielding a 96.0% attack success rate across these platforms. While the paper introduces ChainGuard as a mitigation strategy to reduce this success rate to 22.5%, it acknowledges that the defense is not absolute. Multi-skill attacks remain a complex, unsolved problem in agent security, as ChainGuard does not fully eliminate the risk. The research does not specify the computational overhead, inference latency, or exact API costs associated with the adversarial generation process.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary vulnerability identified in agent skill scanners?
Scanners typically inspect individual skills in isolation, failing to detect malicious intent when it is distributed across a chain of interdependent, locally plausible sub-skills.
Q2. How does ColluSkill bypass existing security measures?
It decomposes malicious intents into smaller parts and uses iterative rewriting based on scanner feedback to ensure each individual component appears benign.
Q3. Is there an effective defense against these attacks?
The paper presents ChainGuard, which reduces the attack success rate to 22.5% while maintaining a 99.5% success rate for benign workflows.
Q4. What is the role of LLM-based chain planning in this method?
Chain planning is used to predict intermediate results and manage the dependencies between sub-skills to ensure the malicious workflow remains functional and coherent.
Q5. Which scanners were tested in the research?
The researchers tested CISCO Skill Scanner, SkillFortify, Auditor, SlowMist, Vetter, and SkillSpector.
Q6. Does ChainGuard provide a total fix for these security gaps?
No, the paper states that ChainGuard does not reduce the attack success rate to zero, indicating that multi-skill attacks remain a challenging defense problem.
Q7. Are there specific hardware or performance requirements listed for running ColluSkill?
The paper does not specify hardware requirements, execution latency, or compute costs.
Q8. Which models were used to evaluate or implement the proposed techniques?
The study utilized Claude Code, Codex, GPT-5.5, DeepSeek-V4-Pro, and GLM-5.2.
Q9. What datasets were utilized for benchmarking?
The evaluation utilized SkillSafetyBench and SCRBench.