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gadievron/raptor

Raptor turns Claude Code into a general-purpose AI offensive/defensive security agent

装之前:没有可直接复制的命令 · 许可证 NOASSERTION · 链接可访问

第一步:这条暂时没有可执行的下一步,可先看原仓库 https://github.com/gadievron/raptor

在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
取自该仓库的安装命令
npm install -g @anthropic-ai/claude-code

解析自我们于 2026-11-04 抓取的该仓库 README;我们未执行过。

这条是仓库自己写的安装方式,不是 MCP 客户端配置,所以不生成 JSON。
配置项的键名以你的客户端为准:客户端与 CLI
★ 数取值
4,011 ★ · gadievron/raptor · 取自 GitHub 搜索 API · (2026-11-13 20:00:50 核对)
来源明细
从该仓库的 GitHub 页面直接读取 · 条目类型:开源项目
收录与活跃
收录于 2026-11-05 · 最近见到 2026-11-14 · 共见到 10 次 · 被 1 个来源列出
评分构成
综合分 111 · 4,011 ★ · 1 个来源 · repo
安装
npm install -g @anthropic-ai/claude-code
常见问题(答案都来自我们的记录)
怎么安装?
该仓库给出的安装命令是 npm install -g @anthropic-ai/claude-code。我们的记录里它是仓库自己提供的;不保证在你的客户端上直接可用。
它适合谁 / 用来做什么?
我们把它归在这些标签下: 开源项目。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 gadievron/raptor;我们收录于 2026-11-05;最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。

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) ║
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║ 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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来源
github · https://github.com/gadievron/raptor
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