Hey everyone! 👋 I was just doing a security review of our PRs and noticed that Snyk Code flagged a few potential SQL injection issues that our previous static analysis tool missed. That got me thinking...
We’re all trying to balance security with developer velocity, right? So I wanted to ask: does Snyk Code actually catch *real* injection flaws in practice, or does it just add noise to the review queue?
From my own testing on a Node.js codebase:
- It caught a classic concatenated query in a `db.query()` call pretty reliably.
- It flagged some template literals in a dynamic MongoDB query builder, which was interesting.
- But I also saw a few warnings on what looked like safe parameterized queries—false positives that the team had to triage.
What’s your experience been? Specifically:
- **Precision:** How many of its alerts for injection (SQL, NoSQL, command, etc.) turned out to be true positives?
- **Noise level:** Did it overwhelm your PRs, or was it manageable?
- **Integration:** Did it work smoothly in your GitHub/GitLab/Bitbucket pipelines?
I’m putting together a little comparison table for our team, and real-world feedback would be super helpful. If you’ve run it on a live project, what was the signal-to-noise ratio like for security flaws?
— Dan
spreadsheet ninja
Totally feel you on balancing security with velocity. Our team's been using Snyk Code for about six months on a mixed Python/JS codebase.
On your point about false positives on safe queries, we saw that too, especially with Django's ORM. It flagged some queryset extra() usage that we'd already properly escaped. We ended up adding a few targeted suppressions in the Snyk config file, which cut down the noise significantly.
Integration-wise, it's been pretty smooth in GitHub Actions. The key for us was tweaking the severity thresholds so only high-confidence issues block the PR, and the rest go into the Snyk dashboard for weekly review. That kept the PR queue clean. What's your policy on letting lower-severity findings through?
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