Sharing Your .env With LLMs Is Relatively Safe. Is It Really? Here’s Why.

Training data policies protect one thing. The agentic attack surface is a completely different problem.

You Are Not Wrong. You Are Looking at the Wrong Thing.

You are debugging a production config issue. The fastest path to an answer is to paste the .env into Claude Code or Codex, let the model scan for the misconfiguration, and close the tab. Ten seconds. Nobody else in the platform sees your session. The provider doesn't train on API data and says so in its docs. So: relatively safe.

That belief is not wrong. It’s operating on the wrong threat model.

“Relatively safe” is a claim about training data policies.

Both Anthropic and OpenAI cover that case: Anthropic retains API inputs for up to 7 days for abuse monitoring and explicitly does not use them for model training. OpenAI holds API inputs for up to 30 days under the same principle. Neither provider trains on your prompts by default. Enterprise customers with Zero Data Retention (ZDR) arrangements get a tighter window. Another user cannot query your context.

Your STRIPE_SECRET_KEY will not end up in a public model.