Security scanners often stop at reporting suspicious code; the harder question is whether a finding is real and whether the fix actually closes it. This project treats those steps as one workflow, combining source-based investigation with validation and follow-up checks. That makes it useful for turning an AI-generated alert into evidence a developer can review—not a substitute for that review.
What Sets It Apart
- Findings are meant to be investigated, not merely counted. Reports include source evidence and coverage limits, helping reviewers distinguish a supported issue from a plausible but unconfirmed candidate.
- Remediation is part of the loop. It can propose patches and verify existing fixes, reducing the gap between identifying a vulnerability and testing a response.
- It fits different review scopes. Teams can examine a whole repository, selected paths, or a Git diff, and automate work through a CLI, TypeScript SDK, or GitHub Actions.
- It can work alongside other scanners. Imported findings can be reviewed and managed with Codex Security results, which helps avoid treating any single scanner as the complete security picture.
Who It’s For—and the Trade-Off
Great fit if engineering or security teams want AI-assisted investigation and remediation integrated into repository workflows, with reports they can inspect and export. Look elsewhere if you need a fully deterministic scanner, guaranteed runtime reproduction, or an automatic security sign-off: findings still require human review, scan coverage can have exclusions, and a proposed patch is not proof that a system is secure.
