Can skill scanners miss attacks hidden across multiple skills?
Current security scanners check each skill individually for malicious behavior. This explores whether attackers can split a harmful objective across multiple benign-looking skills that pass inspection separately but form a dangerous chain when composed together.
The paper starts from an empirical study of existing skill scanners and reports what they look at: current defenses "primarily inspect individual skills, including their instructions, permissions, dependencies, and code behaviors," which leaves "risks arising from cross-skill composition insufficiently examined." The blind spot it draws from that is practical: "multiple locally plausible skills may independently pass security scanning while collectively forming a harmful workflow during agent execution."
ColluSkill is the attack built to sit in that gap. It decomposes "a complete malicious intent into several interdependent subpayloads" and embeds each in an independently packaged skill, so the attack "does not rely on any single malicious skill, but on the ordered composition of locally plausible behaviors through contextual dependencies, artifact passing, and execution handoffs." My reading is that "interdependent" carries the weight: a subpayload that is only one step of a chain looks like an ordinary utility when scanned alone, so its incompleteness is its camouflage. The excerpt names the three coupling routes and does not define them.
This is the SafeFlow shape moved to a different artifact. There the fragments are subtasks handed between role-specialized agents at runtime (Can task decomposition hide harmful intent across agents?); here they are packaged skills, and the check that fails is a scan of the file. The two also differ in time, which is my inference: a scanner judges a skill when it is packaged or installed, while the chain exists only when an agent composes the skills during a run. That is why sharpening the per-skill check does not help, the point Can individual components pass safety checks if the system still fails? makes for other components. The attack also rides on something agents are built to do: How should agents route across thousands of skills? argues real tasks need several skills chained, so the dependencies and hand-offs the attack relies on look like what a legitimate plan produces. By the SoK's four-way test for what interaction did to a failure, this is the composition role (Does a multi-agent setting automatically signal a security effect?), with skills as the composing parts and not agents: the excerpt describes an agent composing them and mentions no second agent. My reading is that the failure here needs several skills and not several agents, which is not the controlled single-versus-multi comparison the fragmentation notes still lack.
The obvious objection is to scan combinations. But the set of chains is not fixed at scan time: the agent assembles it per task from a library that can hold thousands of skills (a vault inference from the routing note). The paper's own answer is a chain-level defense, not pairwise scanning; see Does chain-level inspection close the cross-skill attack blind spot?.
What the excerpt does not give. The six scanners are unnamed. The empirical study's scope and measures are not described, and the excerpt does not say whether it used the same six scanners as the evaluation. There is no worked example of an intent split into subpayloads.
Inquiring lines that read this note 11
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
Can defenses detect attacks composed across multiple skills?- Why do workflow-level defenses catch attacks that single-skill inspection cannot detect?
- Why does scanning skill pairs not fully prevent cross-skill attacks?
- Can defenses check skill chains at execution time instead of scan time?
- How do chain-level defenses differ from per-skill scanner detection approaches?
- Why do skill scanners fail when evaluating composed behaviors instead of isolated skills?
- How much does attack success depend on tuning to specific scanners versus general robustness?
- Can skill scanners detect attacks spanning multiple skills in a chain?
Related concepts in this collection 7
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Can task decomposition hide harmful intent across agents?
Explores whether splitting a harmful objective into specialized subtasks allows malicious intent to evade detection at each individual step, since no single agent sees the full malicious picture.
the same fragmentation in runtime multi-agent roles; here the fragments are packaged skills and the failed check is a pre-run scan
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Can individual components pass safety checks if the system still fails?
Explores whether local validation at each step—alignment checks, protocol compliance, plausibility tests—can guarantee safety when components are composed into larger workflows. Why the gap between component-level assurance and system-level outcomes matters for AI safety.
the pattern this instantiates; here it comes with a measured attack-versus-scanner result, not only an argument
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Can step-by-step approval miss harmful behavior patterns?
If each action an agent takes passes its individual safety check, can the overall sequence still violate system constraints? This matters because per-action inspection may miss harms that emerge only across time or composition.
the general thesis, with a skill-scanner case
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Does a multi-agent setting automatically signal a security effect?
Explores whether observing a failure in multi-agent systems proves the failure is genuinely multi-agent in nature. The distinction matters for correctly interpreting security research and avoiding false attributions.
the SoK's four-way test; this case is the composition role among skills, with no second agent in the excerpt
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How should agents route across thousands of skills?
As skill libraries grow, should routing focus on selecting one skill or composing many? This explores whether decomposition and chaining creates better task execution than single-skill selection.
composition is by design, and the attack rides on it
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Can attackers evade skill scanners by refining individual skills?
Explores whether feedback from per-skill scanners can be weaponized to make malicious multi-skill chains undetectable. Matters because it tests a core assumption of skill-level defense mechanisms.
how the chain is tuned until the scanners pass it
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Does bundling code with skills create hidden security risks?
Agent skills combine instructions with executable code and system access. This packaging enables reuse but may also enable attacks—especially when skills are composed together or shared across platforms without adequate inspection.
why a skill is worth attacking at all
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners
- Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection
- ChannelGuard: Safe Models Do Not Compose into Safe Multi-Agent Systems
- Reinforcement Learning with Rubric Anchors
- LLMs Corrupt Your Documents When You Delegate
- SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems
- Natural Emergent Misalignment From Reward Hacking In Production RL
- SafeFlow: Semantic Information-Flow Control for Blocking Malicious Propagation in Multi-Agent Systems
Original note title
adversarial cross-skill composition evades skill scanners that inspect one skill at a time — no single skill is malicious and the ordered chain carries the attack