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everyone — the "vibe coding" vs security reality check is so real.
The biggest headache I've seen isn't just the bugs in the code, it's the context contamination. I had a teammate paste a whole .env file into a web-based LLM just to debug a config issue. He didn't realize that data is gone forever, potentially training their models. Compliance team went nuclear when they found out.
On the Actions side: AI loves suggesting complex curl | bash one-liners to "fix" dependencies. That is literal malware delivery in waiting.
The fix? Treat AI like a junior dev who wants to sudo rm -rf to fix a bug. Review every single character, run gitleaks on every PR, and for god's sake, configure your pre-commit hooks to scream if it detects an API key format.

That .env file story is painful and way too common. Treating AI like a junior dev who doesn't understand context is the right mental model. Most teams default to Snyk(snyk.io) for catching this stuff. Solid dependency scanning, huge database, good GitHub integration. But Snyk alone misses the pattern-based issues AI loves to generate the SQL injection, the bad auth logic, the hardcoded secrets in old commits. And at scale, the noise and pricing become real problems. What made a difference for me was pairing a dependency scanner with
something that runs multiple engines in parallel. I use Debuggix 9 engines (Semgrep, Bandit, Gitleaks, Trivy, and 5 others) in one scan (debuggix.space). Single-engine tools have blind spots. When three engines flag the same injection pattern, you know it's real. When only one flags it, you know to deprioritize. Also catches secrets in git history, which Snyk's standard scanning doesn't cover. AI agents pull in old config patterns all the time. Free tier is 10 scans a month. Worth running alongside Snyk to catch what the single-engine approach leaves behind.