How do you assess agent skill safety beyond single-package scanning?

Why composition-aware review is safer than single-package scanning

Single-package scanning answers only one question: is this skill or dependency obviously malicious on its own? Agent skill safety is a workflow property, so the real issue is whether otherwise ordinary components become unsafe when they are combined, ordered, or allowed to inherit each other’s authority. That includes prompts, tool calls, data access, and side effects that only become dangerous in sequence.

A composition-aware review therefore treats the agent path as the unit of analysis. If one skill can trigger another, pass data into a more privileged tool, or change the agent’s state in a way that affects later actions, the risk emerges from the chain, not the package in isolation. That is why Agent Skills Security Guide is useful here: it frames skill vetting as a whole-workflow exercise, not a file-by-file check.

The practical question is not only “does this package contain malicious code?”, but “what can this skill make the agent do once it is trusted?” If a benign-looking skill can reach sensitive data, broaden tool access, or alter downstream prompts, then the combined behavior may be unsafe even when each input looks acceptable on its own. That is the core failure mode composition-aware review is designed to catch.

What to map across the full agent workflow

Start by tracing each skill from entry to exit: what it reads, what it can call, what it can pass forward, and what state it can change. A useful map includes tool permissions, prompt boundaries, data sources, output destinations, and any implicit trust the agent gives to content produced by one skill and consumed by another. The review should also note whether a skill can introduce new instructions, new retrieval paths, or new external calls that were not visible in the original package metadata.

The same mapping should identify where privilege is inherited or amplified. A low-risk skill can become dangerous if the agent reuses its outputs in a higher-privilege context, or if one skill can steer the agent into actions that another skill was never intended to perform. OWASP Agentic Top 10 ASI04 Guide is a good reference point for this kind of supply-chain and privilege scoping work because it focuses on how skills, prompts, and agents interact across the chain.

This is also where composition uncovers hidden data paths. A skill that seems harmless in isolation may become unsafe if it can exfiltrate context through logs, side channels, shared memory, or output formatting that another component later transmits. Likewise, a skill that merely transforms input can still be a problem if it creates unexpected actions when chained with a tool-wielding agent.

What strong review should catch before release

Strong review looks for emergent behavior, not just known-bad artifacts. The red flags are unsafe privilege escalation, hidden movement of sensitive data, and unexpected actions that appear only after two or more benign components are combined. A skill can be clean in static inspection yet still be unsafe if it changes the agent’s decision path, expands its reach, or creates a trust boundary the reviewer did not model.

That is why the test should include representative end-to-end scenarios, not only package-level analysis. Reviewers should see whether the composed workflow can reach tools it should not, whether it can access data outside its stated purpose, and whether one skill can silently influence another through shared state or generated instructions. If the workflow only behaves safely when every component is used exactly as intended, then the composition is fragile and should be treated as such.

For broader context on how agent autonomy changes risk as tool access grows, AI Agents vs Agentic AI is helpful because it explains why the security review must track authority, not just content. And because skill chains sit inside a wider supply chain, OWASP Agentic Skills Top 10 (AST10) provides the external skill-layer framing that matches this exact review problem.

Risk and Threat Considerations

Composition risk matters because attackers rarely need a single obviously malicious package when they can exploit trust across multiple ordinary ones. A skill chain can hide privilege expansion, move data through unexpected paths, or turn a routine agent action into an unauthorized external request. The danger is often cumulative: each component looks acceptable alone, but the combination crosses a security boundary the reviewer did not explicitly test.

Failure mechanism: A benign skill can inherit context, data, or authority from a previous step, then use that trust to trigger a tool call, expose sensitive content, or steer the agent into a higher-impact action that single-package scanning would not reveal.

Impact: The agent may disclose data, perform unintended actions, or reach systems and permissions outside the intended scope, creating a workflow-level compromise even when no single package is overtly malicious.

Practitioner Guidance

What to prioritise: Review the composition first where skills can touch sensitive tools, data, or actions. If a workflow includes prompt chaining, retrieval, code execution, or outbound connectivity, treat the interaction surface as the real asset, not the package list.

What to verify: Confirm that each skill has a narrow purpose, that downstream steps do not inherit broader authority than intended, and that no skill can silently convert read-only context into an action. A good review can explain why every tool call, data path, and side effect is necessary.

Practitioner takeaway: The safest agent is not the one built from individually clean parts, but the one whose combined behavior has been tested for authority creep, unexpected data flow, and emergent side effects.

Related resources from NHI Mgmt Group

• Package Scanning
• What is the difference between human identity governance and AI agent governance?
• When does AI agent access create more risk than it reduces?
• What is the difference between governing human access and governing AI agent access?