I've had similar concerns with letting agents view any credentials, or logs which could include sensitive data.
Which has left me feeling torn between two worlds. I use agents to assist me in writing and reviewing code. But when I am troubleshooting a production issue, I am not using agents. Now troubleshooting to me feels slow and tedious compared to developing.
I've solved this in my homelab by building a service which does three main things:
1. exposes tools to agents via MCP (e.g. 'fetch errors and metrics in the last 15min')
2. coordinates storage/retrieval of credentials from a Vault (e.g. DataDog API Key)
3. sanitizes logs/traces returned (e.g. secrets, PII, network topology details, etc.) and passes back a tokenized substitution
This sets up a trust boundary between the agent and production data. The agent never sees credentials or other sensitive data. But from the sanitized data, an agent is still very helpful in uncovering error patterns and then root causing them from the source code. It works well!
I'm actively re-writing this as a production-grade service. If this is interesting to you or anyone else in this thread, you can sign up for updates here: https://ferrex.dev/ (marketing is not my strength, I fear!).
Generally how are others dealing with the tension between agents for development, but more 'manual' processes for troubleshooting production issues? Are folks similarly adopting strict gates around what credentials/data they let agents see, or are they adopting a more 'YOLO' disposition? I imagine the answer might have to do with your org's maturity, but I am curious!
This matches what I've seen. The .env file is one vector, but the more common pattern with AI coding tools is secrets ending up directly in source code that never touch .env at all.
The ones that come up most often:
- Hardcoded keys: const STRIPE_KEY = "sk_live_..."
- Fallback patterns: process.env.SECRET || "sk_live_abc123" (the AI helpfully provides a default)
- NEXT_PUBLIC_ prefix on server-only secrets, exposing them to the client bundle
- Secrets inside console.log or error responses that end up in production logs
These pass type-checks and look correct in review. I built a static analysis tool that catches them automatically: https://github.com/prodlint/prodlint
It checks for these patterns plus related issues like missing auth on API routes, unvalidated server actions, and hallucinated imports. No LLM, just AST parsing + pattern matching, runs in under 100ms.
gitleaks and trufflehog are great for scanning git history for leaked secrets but that's one of 52 rules. prodlint catches the structural patterns AI coding tools specifically create: hallucinated npm packages that don't exist, server actions with no auth or validation, NEXT_PUBLIC_ on server-only env vars, missing rate limiting, empty catch blocks, and more. It's closer to a vibe-coding-aware ESLint than a secrets scanner.
Can't say it's a perfect solution but one way I've tried to prevent this is by wrapping secrets in a class (Java backend) where we override the toString() method to just print "***".
I've had similar concerns with letting agents view any credentials, or logs which could include sensitive data.
Which has left me feeling torn between two worlds. I use agents to assist me in writing and reviewing code. But when I am troubleshooting a production issue, I am not using agents. Now troubleshooting to me feels slow and tedious compared to developing.
I've solved this in my homelab by building a service which does three main things: 1. exposes tools to agents via MCP (e.g. 'fetch errors and metrics in the last 15min') 2. coordinates storage/retrieval of credentials from a Vault (e.g. DataDog API Key) 3. sanitizes logs/traces returned (e.g. secrets, PII, network topology details, etc.) and passes back a tokenized substitution
This sets up a trust boundary between the agent and production data. The agent never sees credentials or other sensitive data. But from the sanitized data, an agent is still very helpful in uncovering error patterns and then root causing them from the source code. It works well!
I'm actively re-writing this as a production-grade service. If this is interesting to you or anyone else in this thread, you can sign up for updates here: https://ferrex.dev/ (marketing is not my strength, I fear!).
Generally how are others dealing with the tension between agents for development, but more 'manual' processes for troubleshooting production issues? Are folks similarly adopting strict gates around what credentials/data they let agents see, or are they adopting a more 'YOLO' disposition? I imagine the answer might have to do with your org's maturity, but I am curious!
This matches what I've seen. The .env file is one vector, but the more common pattern with AI coding tools is secrets ending up directly in source code that never touch .env at all.
The ones that come up most often:
These pass type-checks and look correct in review. I built a static analysis tool that catches them automatically: https://github.com/prodlint/prodlintIt checks for these patterns plus related issues like missing auth on API routes, unvalidated server actions, and hallucinated imports. No LLM, just AST parsing + pattern matching, runs in under 100ms.
Just use gitleaks or trufflehog?
gitleaks and trufflehog are great for scanning git history for leaked secrets but that's one of 52 rules. prodlint catches the structural patterns AI coding tools specifically create: hallucinated npm packages that don't exist, server actions with no auth or validation, NEXT_PUBLIC_ on server-only env vars, missing rate limiting, empty catch blocks, and more. It's closer to a vibe-coding-aware ESLint than a secrets scanner.
Can't say it's a perfect solution but one way I've tried to prevent this is by wrapping secrets in a class (Java backend) where we override the toString() method to just print "***".
Haha, takes me back - we used to do this for PII too, also Java