PRO · AI REPAIR

From finding to verified PR — in 10 seconds.

Our AI engine reads the vulnerable code, drafts a patch, runs your tests in a sandbox, re-scans for safety, and opens a GitHub PR. You review and merge.

AI Repair runs after a scan finds a vulnerability — pick a scan to start.
Run Repo Scan first →Run Free Scan →
10s
average resolution
95%
AI fix success rate
25
languages supported
0
data retained

How it works after a scan

AI Repair takes a finding from any of your scans and turns it into a reviewable Pull Request.

A
Run a scan
Free Scan, URL Scan, or Repo Scan — any one produces findings AI Repair can act on.
B
Pick a finding
Click any high-confidence finding and choose 'Repair'. The engine has the full context.
C
Review the PR
A verified, sandbox-tested patch arrives as a GitHub PR with reasoning attached.

Five stages, fully automated

  1. 1
    Context
    Vulnerable code slice + similar CVE patches retrieved from RAG.
  2. 2
    Generate
    Our AI engine generates a fix with a step-by-step reasoning trace.
  3. 3
    Sandbox
    Docker (--network none --read-only --cap-drop ALL) runs your tests.
  4. 4
    Re-scan
    Confirms the patch doesn't introduce new vulnerabilities.
  5. 5
    PR
    Confidence score (0–100) plus a fully-formed GitHub Pull Request.

Built for production

Sandboxed by default

Generated patches never touch your infra. Tests run inside a network-isolated, read-only Docker container with all Linux capabilities dropped.

Confidence-scored

Every PR carries a 0–100 score combining test pass rate, re-scan safety, breaking-change risk, and CVE patch reference quality. Review what matters.

Reasoning trace, exposed

We show you why the engine made each change. The full step-by-step reasoning is attached to the PR — no black boxes.

Ready to ship clean code?

Pick a scan — AI Repair takes it from there.

Run Free Scan →Run URL Scan →Run Repo Scan →