Should organisations allow AI skills to ship with compiled caches?

No, not when the skill can influence code execution or tool use. Compile at deploy time from source, strip caches from distribution packages, and validate the runtime artefact before the agent inherits any privileges. Otherwise, a small hidden payload can survive ordinary review and become an execution path.

Why This Matters for Security Teams

Compiled caches are not just performance artefacts when an AI skill can trigger code execution, call tools, or inherit identity and privilege. In those cases, the cache becomes part of the trusted supply chain, which means it can carry logic that ordinary source review may never inspect. That creates a governance problem as much as a technical one, because the organisation may believe it is shipping reviewed behaviour while actually distributing prebuilt execution paths. NIST’s control baseline in NIST SP 800-53 Rev 5 Security and Privacy Controls is useful here because it reinforces configuration control, integrity, and least privilege as operational requirements, not optional hygiene.

The practical risk is that caches can preserve stale imports, embedded prompts, serialised bytecode, or helper logic that no longer matches the source tree. If that artefact is later loaded by an agent with access to secrets, APIs, or workflow actions, the cache can become a covert bridge between development and runtime. Security teams often miss this because build systems treat caches as harmless acceleration data, while threat actors treat them as a persistence opportunity. In practice, many security teams encounter cache abuse only after a deployment pipeline or agent tool chain has already been trusted end to end, rather than through intentional artefact governance.

How It Works in Practice

The safest pattern is to compile from source at deploy time, not at package publish time, and to treat every shipped artefact as something that must be reconstructed and verified in the target environment. For AI skills, that means separating human-reviewed source, reproducible build outputs, and runtime packaging. It also means deciding whether the cache is an internal build accelerator or a distributable artefact. If it is distributable, it should be treated as code, not as a convenience file.

Operationally, teams usually need four controls working together:

• Build pipelines that generate caches in controlled environments and never copy them into release bundles unless there is a documented reason.
• Integrity checks that compare the runtime artefact against the expected source commit, dependency set, and build configuration.
• Artefact scanning for embedded logic, serialised objects, hidden imports, and unexpected execution hooks before release.
• Privilege gating so the skill cannot inherit tool access until verification completes and the release is signed.

This is especially important in agentic workflows, where a skill may be able to read files, invoke tools, or chain into other services. In that environment, the cache is not just an optimisation layer, it can become an execution layer. Guidance from the OWASP Top 10 for Large Language Model Applications and the NIST AI Risk Management Framework both support a defence-in-depth approach that validates inputs, provenance, and runtime behaviour before trust is extended. These controls tend to break down when releases are assembled from mixed language stacks and container layers because caches, wheels, and image layers can reintroduce code paths after source review has already finished.

Common Variations and Edge Cases

Tighter artefact control often increases build time and release friction, requiring organisations to balance deterministic delivery against developer convenience. That tradeoff is real, especially in large monorepos or fast-moving AI product teams where caching can materially speed tests and packaging. Best practice is evolving, but the current guidance suggests that the more a skill can influence execution or tool use, the less acceptable it is to ship opaque precompiled content.

There are edge cases where a cache may be acceptable, but only with strong constraints. For example, a cache used purely for local inference acceleration inside a sealed, read-only runtime is lower risk than a cache distributed to third parties. Even then, the security question is whether the cache can change behaviour in ways the source review did not cover. If the answer is yes, it should be treated as a controlled build output with provenance, signing, and rollback requirements.

This is where agent governance and NHI controls intersect. If a skill is granted access through a non-human identity, the cached artefact must not be able to bypass that identity policy or smuggle in privileges the agent was never meant to hold. For AI systems with supply-chain exposure, OWASP Agentic AI Top 10 is a useful complement to build integrity controls. The policy breaks down in environments that rely on third-party plugin bundles or self-updating agents because those models blur the line between release artefact and live execution state.

Related resources from NHI Mgmt Group

• Should organisations allow AI agents to use hardcoded secrets?
• Should organisations allow AI systems to execute response actions directly?
• Should organisations allow AI agents to register clients dynamically?
• Should organisations allow AI tools that can generate attack code?