Static Application Security Testing

Find the flaws in your own code — ranked by what's actually exploitable.

BreachLens scans your first-party source across major languages, then ranks the findings by reachability and proof and uses AI to flag the false positives. Your team fixes the handful that matter instead of triaging thousands that don't.

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Major languagesAI cuts false positivesRuns on your infrastructure

Signal, not a wall of alerts

A SAST report nobody can work through.

Pattern-matching scanners flood you with thousands of findings and no priority — so the report gets ignored. BreachLens ranks by reachability and proof, uses AI to cut the likely false positives, and correlates each finding with the rest of your stack.

Scan

Your code, every language

Source across Python, JavaScript/TypeScript, Java, Go, Ruby, PHP, C#, and more — scanned on every pull request, each finding tagged to its CWE.

Rank

Exploitable first, noise cut

Findings float up by reachability and proof, and an AI pass flags the likely false positives with its reasoning — so the top of the list is real.

Fix

Drafted as a pull request

For the findings that matter, AI drafts the patch and opens a PR against the repo. A human reviews and merges — nothing changes without approval.

What you get

First-party code security your team will actually use.

Major languages, one scan

No per-language tool zoo.

· Python, JavaScript/TypeScript, Java, Go, Ruby, PHP, C#, and more.
· Each finding tagged to its CWE, with the offending code.
· One engine, one view, across every repo.

AI cuts the false positives

Triage the real ones.

· An AI pass reviews findings and flags the likely false positives.
· With its reasoning, so you can agree or overrule it.
· Your own model — Anthropic, OpenAI, Gemini, or fully local.

Ranked by exploitability

Reachability and proof, not just severity.

· Findings float up by whether the code is reachable and proven.
· A reachable, proven flaw outranks a wall of unproven highs.
· So effort goes where an attacker would actually go.

Correlated into attack paths

The chain, not the alert.

· A code flaw links to the image it ships in and the app it's exposed on.
· One story across code, containers, and the running app.
· Click any node to open the underlying finding.

Caught where you work

In the PR, and in the editor.

· Findings post inline on the pull request, on the diff.
· Or surfaced in the IDE extension as you write.
· Security in the tools your team already uses.

breachlens · findings

A BreachLens SAST finding — a command injection in first-party application source, shown with the offending lines, its severity and confidence, a plain-language explanation, and whether it sits in an attack-path chain.

A finding on real application code — the vulnerable lines, a plain-language explanation, and an honest note when it isn't part of an attack path.

Straight answers

What an AppSec team asks about SAST.

Which languages does it cover?

The major ones — Python, JavaScript/TypeScript, Java, Go, Ruby, PHP, C#, and more — in a single scan, with each finding tagged to its CWE. No separate tool per language.

Won't it drown us in false positives?

That's the point of the AI pass. Beyond ranking by reachability and proof, an AI review flags the likely false positives — with its reasoning — so your team triages the real findings instead of a wall of noise.

Is it just pattern matching, or does it prioritise?

It prioritises. Findings are ranked by reachability and proof of exploit and correlated across tiers — a code flaw shows up alongside the container it ships in and the app it's exposed on, as one attack path rather than an isolated alert.

Where does it run?

On your infrastructure. Your source and the findings never leave your network — it can run fully air-gapped, with your own AI model for triage and fixes.