G
gadievron/raptor
Raptor turns Claude Code into a general-purpose AI offensive/defensive security agent
View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
Install & config
Install command from the repository
npm install -g @anthropic-ai/claude-code
Parsed from the README we fetched on 2026-11-04; we have not executed it.
This is the install method the repository states, not an MCP client config, so no JSON is generated.
Key names differ by client: Clients & CLI
- Star count
- 4,011 ★ · gadievron/raptor · read from the GitHub search API · (checked 2026-11-13 20:00:50)
- Source
- read directly from its GitHub page · type: open-source project
- Harvest history
- first seen 2026-11-05 · last seen 2026-11-14 · seen 10× · listed by 1 sources
- Score
- score 111 · 4,011 ★ · 1 sources · repo
- Install
npm install -g @anthropic-ai/claude-code
FAQ (answers come from our records)
How do I install it?
The repository provides this install command:
npm install -g @anthropic-ai/claude-code. It comes from the repository itself; we cannot guarantee it runs as-is in your client.Who is it for?
We filed it under these tags: 开源项目. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository
gadievron/raptor;first catalogued 2026-11-05;last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.text
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
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║ Autonomous Offensive/Defensive Research Framework ║
║ Based on Claude Code (v3.1.0) ║
║ ║
║ Gadi Evron, Daniel Cuthbert, Thomas Dullien (Halvar Flake) ║
║ Michael Bargury, John Cartwright ║
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⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠻⠿⣿⣧⠀ Get them bugs.....⠀⠀⠀⠀⠀
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 /agentic; use
/understand or standalone /audit when you need a focused investigation.
Check what your software ships with. Inspect application dependencies,
container packages, and workflow components; identify advisory and supply-chain
risks, produce an SBOM, and plan fixes or upgrades with /sca.
Investigate an unfamiliar binary or a crash. Recover entry points and
parser boundaries, collect runtime evidence, fuzz a target, and investigate
the failure with /binary, /fuzz, and /crash-analysis.
Test running applications. Explore web endpoints with /web and inspect
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║ ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝ ╚═════╝ ╚═╝ ╚═╝ ║
║ ║
║ Autonomous Offensive/Defensive Research Framework ║
║ Based on Claude Code (v3.1.0) ║
║ ║
║ Gadi Evron, Daniel Cuthbert, Thomas Dullien (Halvar Flake) ║
║ Michael Bargury, John Cartwright ║
║ ║
╚═══════════════════════════════════════════════════════════════════════════╝
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣤⣤⣀⣀
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⣀⠤⠴⠒⠒⠛⠛⠛⠛⠛⠿⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠟⠁⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠛⣿⣿⣿⡟⠻⢿⡀⠀⠀⠀⠀⠀
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⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⡇⣿⡁⠀⠀⠀⠉⠁⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠻⠿⣿⣧⠀ Get them bugs.....⠀⠀⠀⠀⠀
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 /agentic; use
/understand or standalone /audit when you need a focused investigation.
Check what your software ships with. Inspect application dependencies,
container packages, and workflow components; identify advisory and supply-chain
risks, produce an SBOM, and plan fixes or upgrades with /sca.
Investigate an unfamiliar binary or a crash. Recover entry points and
parser boundaries, collect runtime evidence, fuzz a target, and investigate
the failure with /binary, /fuzz, and /crash-analysis.
Test running applications. Explore web endpoints with /web and inspect
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