Raptor

Authors: Gadi Evron, Daniel Cuthbert, Thomas Dullien (Halvar Flake), Michael Bargury, John Cartwright
(@gadievron, @danielcuthbert, @thomasdullien, @mbrg, @grokjc)

Licence: MIT, see LICENSE. CodeQL and other third-party components have their own licence terms; see third-party notices and dependencies.

Repository: https://github.com/gadievron/raptor

What is RAPTOR?

Built by hackers, for hackers.

RAPTOR is an autonomous security research framework built on top of Claude Code (but not tied to it -- you can plug in your own analysis layer too). It chains together static analysis, binary analysis, LLM-powered vulnerability validation, exploit generation, and patch writing into a single workflow you can run against a codebase or binary.

It is not polished software. It was built in free time, held together with enthusiasm and duct tape, and it works well enough that we can't stop using it. If you want to make it better, open a PR.

RAPTOR supports security research and human review; its reports do not certify
that a codebase is secure.

RAPTOR stands for Recursive Autonomous Penetration Testing and Observation Robot. We really wanted to call it RAPTOR.

How it's built

RAPTOR is mostly AI-generated code. The humans set direction, review
output, and make design decisions; the AI writes the implementation.
Mechanical verification (tests, static analysis, corpus calibration) keeps
the quality bar where it needs to be regardless of who — or what — wrote
the code.

Start here: Capabilities · Quick start ·
First run · Security ·
Documentation

What RAPTOR can do

- Take a codebase from first look to candidate fixes. Map its attack surface,
  investigate suspicious code, assess scanner findings, test exploitability,
  and generate PoCs and patches for review. Start with…