You've got the gist exactly right. That file-read-to-email example is the textbook case.

For your Docker Compose setup, the starting protection is embarrassingly simple: your compose file should list zero tools. You then add exactly one, and only when you've proven to yourself the agent can't do its job without it. The default OpenClaw project templates give you a kitchen sink of tools "for convenience," which is where most new users get bitten. Delete them all first.

The other common trap is thinking you're safe because you didn't give it a network tool, but you gave it a logging tool that writes to a file. If that file is in a mounted volume another container reads, you've just created an indirect network channel. Start by assuming any data output can be exfiltrated.

Good luck parsing that audit trail when your queue middleware logs are in one system and your container logs are in another. You've just traded an opaque blob for fragmented noise.

Complexity creep is the killer. Teams end up so tangled in their own plumbing they can't see the actual data flows. The "distributed system" you're building still has a single brain making all the decisions. You just moved the levers further away.

Your example is spot on. I just set up my first agent and the "strip every tool" advice saved me. I almost used the default template with a dozen tools before reading threads here.

One thing I'm still figuring out: how do you actually test it's secure? Like, you remove the email tool, but what's to stop a clever prompt from making it *pretend* to call a tool it doesn't have? The LLM might still output a fake JSON function call in its response, right? Do we just rely on the framework to ignore that?

Also, if you're using Docker, does isolating the agent in its own container actually help if all the dangerous tools are already removed? Or is that extra complexity for later?

The principle's correct, but that validation function is an in-process allow-list, not a security boundary. It's trivially bypassed if the agent can corrupt the `user_session` state or the function's logic flow, which is often possible through prompt injection or unexpected context manipulation.

For a true permit system, the policy and enforcement must be external. A minimal sidecar that validates against a signed, immutable policy file is the baseline. Your Python snippet is a good first-step audit log, but treat it as a logging mechanism, not an enforcement mechanism.

Also, you need to consider the supply chain of that `allowed_tools` list itself. Where does it come from? Is that session data generated from a trusted, signed SBOM, or is it just another mutable runtime variable?