Run focused AI vulnerability scans with open-kritt

open-kritt is a self-hosted security research platform for developers and bug-bounty teams that want AI agents to inspect code in smaller, repeatable workflows instead of asking one model to audit an entire repository at once. It lets you build prompt-based scan workflows, run them with Codex or Claude Code, validate findings with post-scripts, rank severity, and de-duplicate results before review.

Why it is useful:

• It is aimed at practical code-security work: scoped scans, reusable workflows, findings, ranking, and proof-building rather than generic chat.
• It supports model access through Codex, OpenAI, Anthropic, or OpenRouter, so teams can choose the provider path that fits their setup.
• The project is open source under AGPL-3.0 and includes a local Docker Compose stack plus a repository-local ./kritt CLI.

Use it carefully. The project documentation states that tool-enabled scan agents run as root inside disposable containers with writable repository copies and direct internet access. The backend is unauthenticated by default. That makes open-kritt best suited for a dedicated VM or isolated Docker host, with scoped credentials and network controls, especially when scanning untrusted or private repositories.

A good first evaluation path is to read the threat model, clone the repository on an isolated machine, run ./kritt setup, configure one model-access option, and scan a small non-sensitive repository before connecting private code.