Hackers can split a malicious task into harmless-looking AI plugins, so security scanners that check them one at a time miss it.
Can adversarial attacks chain multiple skills to evade security checks?
This explores whether an attacker can split a malicious goal across several agent "skills" (packaged, reusable capabilities an AI agent can load) so that no single piece trips a security scanner, and what defenders can do about it.
This explores whether an attacker can split a malicious goal across several agent "skills" so that no single piece trips a security scanner. The corpus says yes, and the reason is structural: most scanners inspect skills one at a time. Can skill scanners miss attacks hidden across multiple skills? shows how a malicious intent can be broken into interdependent subpayloads, each packaged as its own skill. Each skill looks harmless in isolation. The attack exists only when they run in order, passing artifacts and context from one to the next.
Attackers can also tune this. Can attackers evade skill scanners by refining individual skills? describes ColluSkill, which pairs chain planning with scanner feedback. The attacker learns what a scanner finds suspicious in a given skill and rewrites just that skill to look calmer, while the meaning of the whole chain stays intact. It reached 96% average attack success across six scanners. Because scanners score each skill on its own, every round of feedback teaches the attacker how to soften one piece. The scanner never sees the whole.
This isn't a quirk of skills. Can task decomposition hide harmful intent across agents? finds the same pattern in multi-agent systems. Splitting tasks and specializing roles is what makes those systems useful, and it also lets harmful intent be spread over steps that each look benign, with harm emerging only in composition. The weak point is the unit of inspection. Anything that reviews pieces in isolation can be beaten by an attack that lives in the relationships between the pieces.
The defensive results are only partly reassuring. Inspecting whole chains instead of single skills helps: Does chain-level inspection close the cross-skill attack blind spot? reports that ChainGuard cuts attack success to 22.5% while still passing 99.5% of benign workflows. That still leaves more than one attack in five getting through, and the notes don't explain which attacks survive. Should defence units span multiple executions and agents? argues for a bigger shift. The thing you defend should be a set of actions linked by observed transfers, task authority and response history, with membership revised as evidence accumulates. In that view, a better scanner isn't the fix. The fix is to change what gets scanned, from a single skill to the evolving story of how skills hand work to each other.
Sources 5 notes
Security scanners that inspect skills one at a time miss coordinated attacks where a malicious intent is decomposed into interdependent subpayloads packaged as separate skills. Each skill appears harmless in isolation, but their ordered execution carries out the attack through contextual dependencies and artifact passing.
ColluSkill combines chain planning with scanner-feedback refinement to reach 96% average attack success. The approach works because scanners score skills individually, allowing feedback to reduce suspicion per skill while chain-level semantics remain intact.
SafeFlow demonstrates that multi-agent systems' core strength—splitting tasks and specializing roles—creates a safety blind spot where malicious intent can be distributed across steps that each appear benign individually, with harm emerging only in composition.
ChainGuard reduces attack success to 22.5% while passing 99.5% of benign workflows, demonstrating that inspecting chains rather than individual skills narrows the vulnerability. However, residual attack success above one in five indicates the blind spot persists despite the improved defense strategy.
The operational unit of defence should be a set of actions linked by observed transfers, task authority, and response history, with membership revised as evidence accumulates. Isolated review loses relevant context that spans multiple executions.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners
- SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems
- SafeFlow: Semantic Information-Flow Control for Blocking Malicious Propagation in Multi-Agent Systems
- Reinforcement Learning with Rubric Anchors
- Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection
- Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response
- LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring
- The Troy Moment of AI: Why Some Will Cheat and Some Will Follow?