woodpecker - Red Teaming for AI and Cloud

项目说明

<di="center">
<a href="
<img src="
</a>
<a href="
<img src="
</a>
<a href ="
<img src="
</a>
</div>
<br />
<di="center">
<h3 align="center">woodpecker</h3>
<p align="center">
Red-teaming for AI and Cloud
<br />
<a href=" the docs »</strong></a>
<br />
<br />
<a href=" Bug</a>
·
<a href=" Feature</a>
</p>
</div>

woodpecker is a modular red teaming tool focused for AI and cloud apps. The tool is designed to disco weaknesses by experimentation.

Getting Started

Installation

You can fetch the latest release [here][latest-release-url], or you can build from source.

#### Building from Source

To build from source, you'll need to have Go installed.

Usage

The design of woodpecker can be broken down into three concepts:

The woodpecker CLI mirrors this, and exposes experiment, and component commands.

#### Experiments &

To start, you need to run an experiment.

Each experiment is defined by a experiment file which allows you to tweak your experiment parameters to suit your scenarios.

For a full list of experiments a, you can run woodpecker experiment and you'll get a list and a short description of their capabilities.

To get you started you can then run woodpecker experiment snippet -e <experiment-name> and it'll output a template you can start from.

Once you're happy with your template you can run it:

Once you' run the experiment, you can if it was sucessful or not:

You can also output in formats using -o json or -o yaml

#### Components

Some experiments require additional applications installed to run or enhance their functionality.

These can be added by pro a YAML file, see the [components][components-dir-url] directory for examples.

Experiments that need a component will warn you if it's not deployed when trying to run it.

Contributing

Please read the contribution guidelines, [here][contributing-url].

License

Distributed under the [Apache License 2.0][license-url].

[latest-release-url]:
[experiments-dir-url]:
[components-dir-url]:
[contributing-url]:
[license-url]:

项目信息

开发语言：Go

Star 数：214

技术要点

该项目属于红蓝攻防方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Red Teaming for AI and Cloud展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。