What is the difference between static scanning and sandbox detonation for AI agent skills?

Static scanning reviews manifests and source code for suspicious patterns, but it cannot see behavior that only appears at runtime. Sandbox detonation executes the skill in isolation and records what it actually does, including network calls, file writes, and credential access. For skill security, that behavioral evidence is far more reliable than code review alone.

Why the Difference Matters for AI Agent Skills

AI agent skills are not just code artefacts, they are execution paths. Static scanning can still be useful for catching obvious red flags in manifests, dependency declarations, command strings, and hardcoded endpoints, but it only sees what is written down. That means a skill can look clean on review and still behave in unsafe ways once it starts calling tools, reaching out over the network, or touching files.

Sandbox detonation changes the question from “What does this look like?” to “What does this actually do?” That matters because the most consequential failures in agent skills often come from runtime behaviour, not syntax. A skill may read secrets, invoke unexpected subprocesses, or exfiltrate data only after it receives a real input or follows a chained action. Behavioural testing is therefore the better detector for hidden trust expansion and unintended access.

In practice, teams discover the dangerous part of a skill only after it is executed in a controlled environment, not when a reviewer is reading the source tree.

How Static Review and Sandbox Detonation Complement Each Other

Static scanning is the right first pass when you want fast triage. It is cheap, repeatable, and good at surfacing obvious issues such as suspicious imports, unsafe shell usage, embedded URLs, and references to permissions or secrets. For agent skills, that helps narrow the review set before anything is run. But static review is bounded by the source material. It cannot prove whether a harmless-looking tool wrapper later opens a socket, rewrites a file, or attempts privilege-bearing actions.

Sandbox detonation is a runtime control, not just a code-control. The skill is executed in isolation while you observe what it actually emits and touches. For AI agent skills, the important observations are behavioural:

network destinations and protocol use
file writes, temp-file creation, and persistence attempts
process launches and command execution paths
credential lookup, token access, or secret-reading behaviour
unexpected outbound data flows after tool use or prompt variation

That makes detonation especially valuable for skills that assemble actions dynamically, load instructions from remote sources, or branch based on runtime context. It also helps separate a genuinely dangerous skill from one that merely contains noisy code patterns.

For the strongest result, teams usually use static scanning to rank candidates and sandbox detonation to confirm whether the skill crosses from suspicious-looking code into observable misuse. These controls tend to break down when the skill depends on external services, hidden feature flags, or human-in-the-loop steps that the sandbox cannot reproduce faithfully.

Common Variations and Edge Cases

Tighter sandboxing often increases friction, so teams have to balance confidence against speed and coverage. Some skills are safe to detonate in a generic container, while others only reveal meaningful behaviour when the sandbox includes realistic credentials, tool access, or network conditions. The operational tradeoff is that a too-restricted sandbox may miss the behaviour you are trying to measure, while an over-permissive one may create its own exposure.

There is also no universal standard for how much static evidence is “enough” before detonation. Current guidance suggests treating static review as a pre-filter, not as proof of safety. Skill packs that fetch remote instructions, invoke MCP-style integrations, or manipulate files based on user input deserve runtime observation even when the source appears tidy. By contrast, a purely declarative skill with no execution path may not justify full detonation unless the packaging itself is suspicious.

Teams should also expect evasive or delayed behaviour. Some skills only activate risky logic after a specific prompt pattern, a second-stage download, or a dependency chain. That means a single happy-path sandbox run is rarely sufficient if the skill can adapt at runtime. The practical mistake is assuming that a clean static scan plus one clean execution equals safety.

Practitioner takeaway: Use static scanning to reduce noise, but trust sandbox detonation for security decisions because AI agent skills are judged by what they execute, not just by what they declare.

Risk and Threat Considerations

AI agent skills create exposure when hidden runtime behaviour can expand access, move data, or trigger side effects outside the reviewer’s expectation. The core risk is that source code review may miss the exact path where the skill becomes dangerous, especially when behaviour is conditional, staged, or dependent on external inputs.

Failure mechanism: An attacker or malicious maintainer can bury unsafe logic behind benign-looking manifests, delayed execution, dynamic imports, or prompt-triggered branches. Once executed, the skill may contact unapproved endpoints, read local secrets, or modify files in ways that were not visible during static analysis.

Impact: The result can be data exposure, unauthorized network egress, credential theft, persistence on the host, or downstream compromise through chained agent actions.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

OWASP Agentic AI Top 10 and MITRE ATT&CK address the attack and risk surface, while CIS Controls v8 set the governance and control requirements practitioners need to meet.

Framework	Control / Reference	Relevance
OWASP Agentic AI Top 10	A3 — Tool Misuse and Excessive Agency	Agent skills can misuse tools or exceed intended runtime actions.
	A4 — Memory, Context and Prompt Injection	Runtime-triggered behaviour can change after prompt or context manipulation.
	A6 — Supply Chain and Third-Party Dependencies	Static review often misses risky dependency behaviour inside packaged skills.
Recommendation — Inspect tool-using skills in a sandbox and block any action beyond approved scope. Test skills under adversarial prompts and reject logic that changes unsafe behaviour at runtime. Scan dependencies, then detonate the packaged skill to verify what the supply chain actually does.
MITRE ATT&CK	T1105 — Ingress Tool Transfer	Sandbox detonation can reveal network fetches and staged payload retrieval.
	T1059 — Command and Scripting Interpreter	AI agent skills may launch commands only when executed, not in static review.
Recommendation — Hunt for runtime downloads and block skills that fetch code or data from untrusted sources. Detonate skills to detect hidden command execution and isolate any interpreter abuse.
CIS Controls v8	16 — Application Software Security	Application code and packaging should be verified before deployment of skills.
Recommendation — Use secure testing and review gates to block unsafe skills before release.

Practitioner Guidance

What to prioritise: Treat any skill that can reach the network, read files, or invoke tools as runtime-sensitive, even if the manifest looks ordinary. Static review should rank it for detonation rather than close the case.

What to verify: Confirm that the sandbox captures outbound connections, file system writes, subprocess launches, and any access to secrets or tokens. A detonation run that does not observe those signals is incomplete for this class of skill.

Decision rule: If the skill’s risk depends on behaviour that only appears after execution, sandbox evidence should outweigh code cleanliness. If the skill is purely declarative, static review may be sufficient.

Practitioner takeaway: The review question is not whether the skill contains suspicious text, but whether its executed behaviour stays within the permissions and trust boundary the organisation intended.