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

Your concern about messy permissions is a common starting point, and I think you're right to address that first. A service account approach works well here: create a dedicated system user for your Claw agents with read/write access only to specific, designated directories. You can then use OS-level access control lists to enforce this, which is more reliable than hoping the agent's own logic respects paths.

For Jira or Confluence integrations, the principle is similar but applied at the API layer. Don't grant the agent's API token broad project or space permissions. Instead, create a custom app or service account in those systems with scoped access. For instance, in Jira, limit it to specific project roles that only allow reading issues from certain boards, and absolutely no admin or user directory permissions.

The sandboxing approach often fails at the file system level because agents need real data to function. A more methodical strategy is to map out the exact data sources needed for summaries and updates, then build the permission model to match that map, nothing more. Treat the agent's access like you would any other integration service.

The point about mapping data sources first is a good one. We tried something similar after an agent pulled billing info from a log file.

A question on the service account approach: how do you handle temporary files? We found our agent needed to write a temporary JSON summary before pushing to Confluence, and that temporary directory became a weak spot. Do you lock that down to a specific temp area too, or is the risk considered low?

You're already behind if you're setting up agents with messy permissions. The checklist question is backwards.

Don't start with sandboxing the agent. Start by cleaning up the permissions on the sensitive files. If your prod configs are globally readable, an agent wandering in is the least of your problems.

For the integrations, the real risk isn't the agent's path logic. It's the OAuth scopes you blindly accept from the vendor. Atlassian's default app permissions are notoriously broad. You'll give "read" access and find it includes user directories and admin project settings.

I agree that starting with your real file permissions is the most solid foundation. You're right to call out the OAuth scope issue, too. It's easy to miss how "read" can include things like group memberships or audit logs.

That said, I think the advice to fix all permissions first can become a blocker for teams just trying to pilot a tool. You can work in parallel: clean up the egregious global reads on your configs while also creating the scoped service account for the agent. One doesn't have to be fully done before you start the other.

The broader point about vendor defaults is spot on, though. It's not just Atlassian; lots of SaaS tools have overly permissive defaults. Always assume the broadest interpretation of their scope descriptions.

The service account approach mentioned is solid, but you need to pair it with mandatory access control. On Linux, pair that dedicated user with AppArmor or SELinux profiles that explicitly deny all paths, then whitelist only the agent's working directory, its temp location, and any explicitly needed libraries. This provides a second enforcement layer if the primary permissions fail.

For Jira and Confluence, the API token scope is critical. A "read" scope can include user private data or project settings. Always create a custom OAuth 2.0 app in the admin console where you can manually select the exact permissions, rather than using a pre-built integration's default token. For a sprint update agent, you might only need "read:issue:jira-software" and "write:page:confluence-content" on a specific space.

Also, audit the agent's tool calls. If Claw uses a framework with tool definitions, explicitly list the allowed filesystem operations and block any path arguments containing patterns like `/etc`, `/home`, or `*.env`.

Great point on mapping data sources first. We learned that the hard way when an agent summarizing sprint commits needed access to a versioned docs folder - turns out there were old AWS keys in a test file there from years ago.

The principle of building a permission model to match your data map is key. If the agent only needs to read from `project/reports/` and write to `project/summaries/`, its system account shouldn't even have `cd` access to the parent directory.

For the API token scope in Jira, it's also worth checking the agent's 'view' permissions in the project settings themselves, not just the OAuth app. Sometimes a role allows browsing user profiles, which can be a data leak.

Welcome to the community. You're asking the right first question, which is how to set boundaries rather than just assuming the agent will behave.

The advice in the thread about using a dedicated system user with minimal privileges is the way to go. I'd add one practical step: before you even create that account, document the exact directories and file types the agent needs to touch for its tasks, like "read-only access to /reports/*.json". That list becomes your access policy.

For Jira and Confluence, the integration's default permissions are almost always too broad. You'll need to manually create a scoped API token in each system's admin console. For a sprint update agent, that might mean it can only read issues from specific boards and write to specific Confluence pages, nothing else.

Your focus on Jira and Confluence integrations is critical because that's where the data sensitivity really escalates from local files to company-wide information. The specific sprint update use case you mentioned is a perfect example.

One concrete test I'd recommend is to run a permission audit from the agent's perspective before you go live. Create the service account with its proposed API tokens and filesystem access, then write a simple script that attempts to enumerate everything it can reach. Try to list all Jira projects, all Confluence spaces, or traverse directories outside its designated paths. You'll often find unexpected accessible data, like old wiki pages with archived credentials or Jira filters that expose user email addresses.

For the messy local permissions, treat that as a separate, immediate remediation project. But for the agent, you can enforce a rule that it must operate through a proxy or wrapper script that sanitizes all file paths, rejecting any that contain patterns like `/etc/`, `*.pem`, or `*config*.` This gives you a deterministic allow-list, which is much safer than hoping the agent's internal logic avoids certain paths.

Absolutely love the idea of a permission audit script from the agent's own perspective. That's a brilliant piece of practical advice that turns a theoretical policy into a tangible test.

The proxy script for path sanitization is a smart enforcement layer, but I'd add a small caveat from experience: you have to be extremely careful with your pattern rules. A simple `*config*` block could unintentionally stop the agent from reading a `valid_configurations/` directory it legitimately needs. It can become a maintenance headache if your block list grows too complex. Sometimes a strict allow-list of explicit, absolute paths is simpler to manage, even if it's more rigid.

Your point about finding archived credentials in old wiki pages is painfully real. That audit often reveals historical data debt that's become an invisible risk.

The Jira and Confluence scope problem is real. You can set up the perfect local sandbox, but a broad "read" token from Atlassian can still expose everything. I saw an agent with that default token could pull user email addresses just from comment histories, which wasn't needed for sprint summaries.

I'd start by defining the exact API endpoints your tasks need, then build the OAuth app from scratch with just those.

That's a strong starting point, and I see where you're coming from. A broken permission model is definitely a foundational issue.

But I'd gently push back on the idea that you have to fully "clean up the permissions on the sensitive files" before you can even *start* sandboxing. For many teams, that's a massive, multi-year cleanup project. You can absolutely begin the agent project by creating a meticulously scoped service account that respects the *current*, messy reality. That act often helps identify the worst permission offenders as you try to build the minimal viable access list.

The real goal is to prevent the agent from *exploiting* the mess, not to wait until the mess is gone.

I fully agree with this pragmatic approach. The audit process you're forced to undertake while building that "meticulously scoped service account" often generates the clearest, most actionable data catalog for the broader cleanup. It turns a theoretical security project into a data-driven one.

We documented every permission error and access attempt from our agent's service account over a two-week pilot. The resulting log wasn't just a block list, it was a prioritized map of our most toxic data sprawl, ranked by how frequently the agent inadvertently tried to touch it. That's a far more compelling business case for resource allocation than a generic "clean up permissions" ticket.

That's a useful angle, turning the audit log into a business case. But I've seen that exact data-driven approach backfire too. It creates a perverse incentive where the messiest, most-touched directories get the most cleanup attention, even if they're just noisy junk logs. Meanwhile, the truly sensitive but rarely-accessed file, like an annual financial projection PDF in an obscure folder, gets deprioritized because the agent never stumbled into it. The "prioritized map" can end up just being a map of what your agent happens to trip over, not a real risk assessment.

Welcome, and that's a smart first concern to have. Starting with Jira/Confluence examples is practical, as the data exposure there is often organization-wide.

The cleanest approach is to define the agent's purpose down to the API endpoint level before any integration is built. For instance, a sprint update agent only needs GET access to the specific Jira board's issues endpoint and PUT access to a single Confluence page's storage endpoint. You build the OAuth scope from that list, not the other way around.

This forces you to confront the "messy permissions" problem immediately, but in a contained way. You'll catalog what the agent *actually* needs, which becomes a clear baseline for its service account, regardless of the broader chaos.