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How do I evaluate the security of custom plugins for a Claw runtime?

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(@pipeline_wizard)
Eminent Member
Joined: 5 months ago
Posts: 12
Topic starter   [#191]

I'm looking at moving some of our pipeline logic to custom plugins for a Claw runtime. The productivity gain is obvious, but I'm stuck on the security review. Vendor docs are all "our sandbox is secure" with zero useful detail.

I need a concrete evaluation rubric, not marketing fluff. What are you all actually checking when you approve a third-party or internally developed plugin for a critical pipeline?

My starting point is asking for these artifacts from the plugin developer, but I'm sure I'm missing key items:

* Full dependency list (direct and transitive), with pinned versions and CVE scan results for each.
* The exact permission manifest the plugin requires within the runtime (e.g., can it `exec`? can it write to `/`? network access?).
* A breakdown of all external system credentials the plugin needs, and the justification for each.
* The data lineage: what pipeline context/secrets does it read, and what does it output? A diagram is mandatory.

Beyond that, what's the right process? Static analysis? Dynamic analysis in a staging runtime? I assume you're not just running `go build` and hoping for the best.

Also, how are you handling runtime behavior? If a plugin is supposed to only call our internal artifact registry, but starts trying to resolve external domains, that's a major red flag. Is anyone using something like eBPF or seccomp profiles inside the runtime to enforce this?

I'd take a look at any actual checklists or automation scripts you're willing to share. Bonus points if you have a Terraform module or Kubernetes setup for a secure plugin sandbox for testing.

--- pw


pipelines are code


   
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(@marketing_ops_priya)
Trusted Member
Joined: 3 months ago
Posts: 41
 

Your artifact list is a solid start, but you're missing the explicit data schema. A plugin that reads "contact data" could be pulling three fields or the entire customer PII table. Demand the exact input and output JSON schema or protobuf definition.

For runtime behavior, static analysis is baseline. You need a dynamic profiler in your staging environment that monitors for abnormal patterns, like a plugin designed to read from S3 suddenly attempting network calls to unfamiliar IPs. We run plugins under eBPF to trace all syscalls against the declared manifest; any deviation fails the review.

Also, audit the developer's CI/CD pipeline. If they're not scanning dependencies on every commit, your pinned version list is stale the moment they rebuild.


Show me the data


   
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