Tired of Claude Code Leaking Your Secrets? I Built Guardrails for It
If you use Claude Code a lot, you've probably seen this:
“Let me ...
For further actions, you may consider blocking this person and/or reporting abuse
Secret exposure is not just a model problem; it is a workspace design problem. The safer setup is least-privilege files, explicit secret boundaries, scrubbed logs, and tests that fail when sensitive values appear in prompts or tool output.
Totally agree - good workspace design should be the first line of defense.
My only addition is that I wouldn't rely on discipline alone. Once an autonomous agent is making hundreds of tool calls, sooner or later someone forgets a boundary, a script exposes something unexpected, or the agent finds a path nobody anticipated.
That's how I see guardrails: not a replacement for least privilege and proper secret handling, but an additional safety net when those controls inevitably aren't perfect.
Defense in depth applies to AI agents too.
Agreed. Discipline is not a control; it is a hope that nobody forgets the control. The useful guardrail is the one that still fires when the workspace is messy, the script is surprising, or the agent finds a path the designer did not anticipate.
Exactly. I think we've converged on the same principle: prevent as early as possible, make the remaining paths visible, and never pretend the system is complete.
That is the whole difference. Discipline depends on everyone remembering the rule under pressure. A control makes the wrong path harder or impossible. For agent work, I trust boring boundaries a lot more than polished instructions.
Exactly. Prompts guide behavior. Boundaries enforce it.
Imo, the better approach would be middleware between the agent and the MCP it uses to execute. Essentially a private/public key encryption, so it sees gibberish, it returns gibberish, it parses with your private key and it executes as if it had real credentials
I like that model for established credentials, and I use a similar approach in my own agent architecture: the agent gets a capability/reference, while the real credential stays behind the execution layer.
The problem I'm trying to cover is a bit earlier in the lifecycle though. During active development, .env often contains many secrets and new credentials are constantly being added before they have a proper broker/MCP abstraction around them. One accidental Read of the file can expose all of them at once.
So I see the two approaches as complementary: hide mature credentials behind capabilities whenever possible, and still guard raw secret files because development is exactly where those boundaries are incomplete.
Yeah it's a bit difficult running a linter to detect it, you'd need to tag everything as secret before you start using AI, or you'd need to run an airgapped local model to tag it... Hm. An embedding model might work for that? I thought before to maybe look into setting up something like it, you use a tiny, fast model locally to run pre-processing, akin to how a draft model works for outputs, but for inputs, so if user gives credentials, it gives the cloud model an alias, if the tool pulls IPs, or secrets, it aliases them. You send apple@apple.com it sees it, flags it, filters it, the cloud model gets pear@peach.com so credentials are kept safe. If trained specifically for the task, it's not too impossible for a model to also act as a gatekeeper for any security risk code, etc. where it just flags 'common mistakes' like unrestricted endpoints, plaintext credentials, etc. and recommends the cloud model fixes it. That way it's never actually 'doing' anything, other than detection and aliasing, with such a small scope, even a 250m model train for that exact purpose can do the job. Unfortunately, I dont have the resources to train 1 from scratch, but I think that's the next evolution of agentic coding safety, a localized filter middleman, which if trained appropriately, can also improve code quality, by being a standards checker.