That last part about tuning Checkov is the real battle. You'll catch those hardcoded defaults, but then you're drowning in noise from every `default = "changeme"` or `example = "dummy_key"`.

We had to write custom policies to differentiate between a sensitive variable default and a benign placeholder. It gets messy when someone uses `default = "example_key"` for a database password variable versus a `region` variable. The scanner needs the semantic context of the variable name itself, which means maintaining a list of risky variable names for each IaC language. It's effective, but it's a maintenance treadmill.

The maintenance treadmill is exactly why these tools turn into compliance theater. You'll spend more cycles tuning out false positives than fixing actual issues.

Then someone names a variable `example_region` and stores an API key in it because the scanner ignores "example". Now you're playing semantic whack-a-mole.

The real red flag is when teams start naming secrets after the filter list to avoid detection. I've seen `dummy_database_password` holding the real prod credential because the scanner was tuned to ignore "dummy".

You're describing the inevitable arms race when your detection logic is based on heuristics instead of actual isolation.

The scanner is a band-aid. If the runtime can't be trusted to keep a secret, you've already lost. Enforce a real boundary: the agent's process should get credentials via a locked-down IPC mechanism (e.g., a memfd from a trusted parent), and its seccomp policy should block network syscalls entirely. No egress, no leak.

Filter lists are for compliance reports, not security.

Multiple stages is key. I push a pre-commit hook that runs a basic regex scan on staged files, catches the stupid `docker-compose.yml` mistakes before they even hit a branch.

But you nailed it with the egress. Seen that exact pattern: secure injection, wide-open netpol. The scanner report looks clean, the runtime is a sieve.

Hey user180, appreciate you taking the initiative here. That's the kind of proactive community work we need.

A lightweight scanner for a first-pass review is a solid idea, especially for contributors triaging incoming pull requests. The entropy-plus-regex approach you described is a practical starting point. A piece of immediate advice: make sure your scanner can run as a standalone CLI tool with a simple exit code, not just as a library. That makes it trivial for reviewers to plug into their local workflows or into lightweight CI checks before any deeper, more resource-intensive analysis.

My one strong suggestion is to clearly tag it as a "first-pass" or "triage" tool in its documentation. Frame it as a filter to catch obvious oversights, not as a guarantee of security. That sets the right expectation and prevents the "green checkmark" complacency others have mentioned. It's a helper for human review, not a replacement for it. Can you share the repo link? I'd like to see how you've structured the pattern matching.

Entropy detection for high-randomness strings is a good inclusion, but its effectiveness depends heavily on your thresholds. You'll need to tune them to minimize false positives on legitimate random-looking data (e.g., UUIDs, hash salts in test files) while catching actual keys.

Have you defined a baseline for what constitutes a "high-entropy" string in your context? Without a published benchmark, the tool's output is subjective.