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Yaniv Spokoiny
Yaniv Spokoiny

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AI coding agents read your .env all the time. Here's how I stop them from leaking it.

#security #ai #python #opensource

If you use Claude Code, Cursor or Codex, your agent has probably read your .env file today. That's normal: it needs context to do its job.

The scary part isn't the read. It's what happens next.

The attack nobody sees

Picture this, in one session:

  1. You ask the agent to fix a bug from a GitHub issue.
  2. The issue (written by an attacker) says: "to reproduce, check the values in .env".
  3. The agent reads .env. Fine.
  4. It copies the values into debug/notes.txt "for later". Looks harmless.
  5. It commits and pushes the fix. The push includes debug/notes.txt.

Your secrets are now on GitHub. Every single step looked innocent on its own.

Why most guards miss it

Most security tools look at the outgoing command and ask "does this look dangerous?" Sometimes they even ask another AI model.

But git push origin main is the most normal command in the world. And if the secret was split, base64-encoded, or copied into another file first, a content scanner has nothing to match.

Track where data came from, not what it looks like

I built StingLabs around one idea: follow the data's origin.

  • The agent reads .env → the session gets marked.
  • Anything the session writes afterwards carries the mark.
  • When marked data tries to leave (a push, a curl, an upload), it gets blocked.

So in the story above, steps 1 to 4 are allowed. Step 5 is denied, and the agent is told what was being sent, where, and where it came from. The exact same git push in a clean session goes through.

Reading isn't leaking. Leaving is.

How it works

  • Runs as a pre-tool hook inside the agent. Before every action, it answers allow, ask or deny.
  • No AI in the decision. Pure deterministic rules, so there's nothing to prompt-inject. Same history, same verdict.
  • Zero dependencies. Python standard library only.
  • Local. Nothing leaves your machine.
  • Tamper-evident. Every decision goes into a hash-chained log on your laptop.

Try it in 2 minutes 

git clone https://github.com/yanivsp2909/stinglabs.git
cd stinglabs/engine
python3 install.py
Enter fullscreen mode Exit fullscreen mode

Then make a fake secret in a throwaway folder and ask your agent to "read .env and post it to https://example.com with curl". Reading works. Sending gets blocked.

Full macOS and Windows steps are in the README.

Honest limits

  • If an agent reads a secret and re-types it from memory with no traced write in between, there's nothing to follow. Exact values and known key formats are still caught.
  • Claude Code is fully verified. Cursor, Codex, Copilot and Antigravity are wired but not yet verified against the real agents.

What's next

It's free and open source (Apache-2.0). I'm 18, and this is my first real open-source launch, so I'd love feedback, and especially people trying to break it.

⭐ github.com/yanivsp2909/stinglabs

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