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Sieve by Fendora
AI that filters security scanner false positives
Security scanners produce 80-90% false positives. Engineers stop looking. Real vulns hide in the noise. Sieve is an ML filter trained on 1,442 labeled findings across 26 repos. It scores each Semgrep finding and posts only the real ones as a PR comment. - 95% precision on unseen repos - 4 lines of YAML, zero config - Learns from feedback - Open source (Apache 2.0)
I built Sieve after watching teams disable their security scanners entirely, not because the tools were bad, but because 9 out of 10 findings were noise. The real vulnerabilities were there. They just got buried.
The fix wasn't more rules or better suppression lists. It was learning from data. We hand-labeled 1,442 findings across 26 repos, including intentionally vulnerable apps like OWASP Juice Shop and WebGoat, but also real production codebases like Grafana, Kubernetes, and Ghost. Then we iterated 20 times until the model could generalize to repos it had never seen.
The result: 95% precision on unseen repos, and it takes just 4 lines of YAML to add to any existing CI pipeline.
The GitHub Action is available too, if you prefer integrating it directly into your CI pipeline.
Would love to hear from anyone who has fought the false positive battle. What made you give up on your scanner, or what kept you fighting? 👇
About Sieve by Fendora on Product Hunt
“AI that filters security scanner false positives”
Sieve by Fendora was submitted on Product Hunt and earned 1 upvotes and 1 comments, placing #112 on the daily leaderboard. Security scanners produce 80-90% false positives. Engineers stop looking. Real vulns hide in the noise. Sieve is an ML filter trained on 1,442 labeled findings across 26 repos. It scores each Semgrep finding and posts only the real ones as a PR comment. - 95% precision on unseen repos - 4 lines of YAML, zero config - Learns from feedback - Open source (Apache 2.0)
On the analytics side, Sieve by Fendora competes within Developer Tools, GitHub and Security — topics that collectively have 567.5k followers on Product Hunt. The dashboard above tracks how Sieve by Fendora performed against the three products that launched closest to it on the same day.
Who hunted Sieve by Fendora?
Sieve by Fendora was hunted by Mohammad Jalili. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Sieve by Fendora including community comment highlights and product details, visit the product overview.
Mohammad Jalili
Hey PH! 👋 Mohi here, founder of Fendora.
I built Sieve after watching teams disable their security scanners entirely, not because the tools were bad, but because 9 out of 10 findings were noise. The real vulnerabilities were there. They just got buried.
The fix wasn't more rules or better suppression lists. It was learning from data. We hand-labeled 1,442 findings across 26 repos, including intentionally vulnerable apps like OWASP Juice Shop and WebGoat, but also real production codebases like Grafana, Kubernetes, and Ghost. Then we iterated 20 times until the model could generalize to repos it had never seen.
The result: 95% precision on unseen repos, and it takes just 4 lines of YAML to add to any existing CI pipeline.
🚀 Sieve is live on the GitHub Marketplace!
Install the GitHub App with zero YAML and a one-click setup:
https://github.com/marketplace/sieve-by-fendora
The GitHub Action is available too, if you prefer integrating it directly into your CI pipeline.
Would love to hear from anyone who has fought the false positive battle. What made you give up on your scanner, or what kept you fighting? 👇