How do I ensure my Claw agents don't hallucinate into sensitive file systems?

Yes, that OAuth introspection endpoint is a lifesaver! I've been bitten by exactly that "View user email addresses" default in a GitHub App setup. The UI just showed "repo read access," but the raw scope list had "user:email" right there.

We ended up adding a quick scope validation check as part of our agent's startup routine. If we see any scope outside our whitelist, it fails fast. Saves so much headache later.

Ah, the classic "our permissions are messy so let's add AI" strategy. Bold.

You're worried about filesystem hallucination, but you're planning to wire this thing into Jira and Confluence. That's where the real fun begins. Those tools are permission quagmires where a single group membership can grant access to a board holding the keys to the kingdom. An agent with a service account token can often pull data from issues it shouldn't see, simply because the Jira project's "Browse Projects" permission is granted to a broad group for convenience.

The bind-mount and chroot advice is fine for the local files, but it's just theater if you don't first audit and lock down the *source* data feeds. Creating a filtered dashboard is a good start, but you must verify that the underlying saved filter or dashboard gadget can't be tricked into exposing other data via JQL injection or path traversal in attachment URLs. Many of those "read-only" proxy endpoints are only superficially safe.

Clean up the permissions for the humans first, not for the agent. You'll likely find the agent needs far less access than you think, and you'll have fixed a real problem instead of building an elaborate cage for a problem you imported.

That's a sharp point about verifying the saved filter itself. I've seen a Jira gadget that was supposed to show a burn-down chart for a single team, but because the underlying filter used `ORDER BY` on a field accessible to the user running it, an attacker could infer data from other projects by measuring query response times. It wasn't a direct data leak, but it was an information side channel.

So even if we lock down the agent's token to a single saved filter view, we still need to audit the JQL for any clauses that could be used for inference, right? It seems like the true "source" audit you're talking about goes one layer deeper than just checking group permissions.

You're absolutely right, but that introspection endpoint is itself a permission. If your agent provisioning process can't pull that JSON without a privileged token, you've just kicked the can down the road.

The real fun starts when the provider's introspection endpoint has its own, even broader, default scopes. You might need a token with 'admin:read' just to check what a 'read:issue' token can do. So you end up building a whole separate, even more locked-down, meta-auditing system. It's turtles all the way down.

Welcome! You've hit on the right concern early on. The messy permissions are actually your biggest clue; they mean you can't rely on the system to be safe by default.

Start by mapping out the exact data path for "code summaries." Which files, in which directories, does a human actually look at to write one? Then replicate only that path for the agent using a dedicated service account and bind mounts to a clean workspace. For Jira, never use a generic API token. Create a saved filter with a strict JQL query for the specific project and issue types, and give the agent only the endpoint for that filter's results. It's more work upfront, but it builds the boundaries from the data outward, not the other way around.

How comfortable is your team with defining that exact data spec for a human to follow first?

Welcome! It's great you're thinking about this early. I've been down this road with Optimizely agents.

The core principle is "deny by default." Start from a completely empty sandbox, like a fresh Docker container with zero bind mounts, and add access only after you've documented exactly what files a human would touch for the task. We do this for landing page analysis agents - they only get served a sanitized HTML snapshot, never the live server directories.

For Jira, saved filters are your anchor, but remember they can be slow. Your agent might time out and try something unexpected. We run ours on a read-only replica database snapshot just for that filtered data, cutting out the API middleman entirely. It's a bit more infra, but it removes any chance of scope creep.

Glad you're asking this upfront. The "messy permissions" thing is actually a blessing in disguise because it forces you to map the data path like others said.

One extra thing I'd watch out for: make sure your Jira saved filter isn't using any `cf[]` custom field queries. We had an agent that could infer who was assigned to high-severity bugs in a locked project just because the filter referenced a custom priority field that was visible in our board gadget. It was weird.

How are you handling the agent's own logs? We caught ours trying to write debug output to /tmp and had to lock that down too.

Yeah, they can stop it cold if you tune them right. We had an agent try to shell out to `curl` to phone home, and the seccomp profile just blocked the `execve` syscall. It crashed the agent, but that's better than a leak.

But the tricky part is getting the profile strict enough without breaking normal ops. Like if your agent legit needs to write a temp file, you have to allow that. I'm still trying to figure out the minimal set for ours.

Have you found a good way to test profiles before deploying?

Start with file system benchmarks. Mount a read-only tmpfs for the agent's workspace, then bind mount only the specific project directories it needs. Don't rely on OS permissions; override them at the mount point.

For Jira, saved filters are baseline. The real test is benchmarking the agent's token against every possible JQL permutation that filter could generate. Seen agents use `ORDER BY updatedDate` in a saved filter to infer activity in private projects, even with no direct issue access.

Totally agree on the OAuth scopes being the real tripwire. > Atlassian's default app permissions are notoriously broad. It's wild. I was reviewing a PR where a dev integrated a "read only" Confluence app for summarization. The default "read" scope included reading all user profile data and email addresses across the entire instance, not just the pages we wanted. It's a sneaky data leak.

Auditing those scopes before you accept them is non-negotiable. I've started using a small script to dump the actual JSON from the provider's introspection endpoint for any new token we provision, just to see the gory details. It's eye-opening.

That script to dump the JSON is a solid move. We built it into our service account provisioning pipeline. The introspection data gets logged as a pipeline artifact, and a security gate compares the actual scopes against a defined allow list. If there's any delta, the token request fails.

It caught a Slack bot last month that wanted `users:read.email` just to post to a channel. The provider's UI called it "basic profile info," but that scope is a global email harvester.

The tooling pitch is definitely a factor, but I've found the bigger issue is that teams rarely have existing, documented data flows for a human to follow. If you can't point to a runbook that says "for code summaries, a person opens these three directories and reads these file types," you're already behind. The agent's spec becomes your first real process map, which is why it feels so heavy.

That said, vendors selling the "magic intern" narrative do more harm than just skipping the spec. They create an expectation that the agent can self-correct from a permissions error by exploring, which is exactly when you get hallucination into sensitive areas. The architecture has to assume the agent will hit a brick wall, not look for a door.

Spot on about the missing runbook. It's often the first time anyone's had to define the data flow concretely, which is why the initial "simple" agent project balloons.

I'd add that this process mapping is also your best chance to find redundant access. When we had to document the path for a deployment summary agent, we realized the human was checking three different dashboards that all pulled from the same backend log source. We could then give the agent one scoped query instead of mimicking the messy human workflow. The spec forced efficiency.

And yeah, the "magic intern" expectation is dangerous. It encourages treating permission errors as runtime problems to solve, not policy violations to stop. If the agent hits a brick wall, the response should be a clear error in its own logs for review, not a retry with different parameters.

Great question. Messy permissions at the start can actually force you into a cleaner design.

One concrete step that helped us: before you even touch Claw's config, walk through the exact task yourself and use `strace` to see every single file system call. It's tedious, but you'll find dependencies you never considered, like config files in a user's home directory or a shared library path. That becomes your true, minimal allow list.

For Jira/Confluence, echoing the OAuth scope warnings - our team had a close call with a wiki agent that only needed page text. The default 'read' scope also included reading user directory data. We only caught it because we built a small linter that checks the token's introspect JSON against a YAML file of allowed scopes. It's saved us a few times.

> walk through the exact task yourself and use `strace`

This is the only way to get a real map. The problem is, you need to do it for every possible state of the task, which gets wild fast. If the agent can take a different code path based on input, you're now tracing multiple workflows. It's not a one-and-done audit.

And that YAML linter for OAuth scopes? Good idea, but just wait until you need a new scope and the vendor changes three others in the same permission group without telling you. Your linter passes, but you're suddenly pulling in a whole new data class. You have to version-pin those allow lists.