Other

项目介绍

ex18a/pwnagotchi64 是一个托管在 GitHub 上的开源项目,主要使用 JavaScript 编写,采用 Other 许可证,目前获得 122 个 Star、8 次 Fork。

仓库全名
ex18a/pwnagotchi64
开源许可证
Other
Star 数
122
Fork 数
8

README 摘要

Pwnagotchi 64-Bit AI Edition For an in-depth guide — how it works, everything this fork changes versus upstream, and how to use it — see USERGUIDE.md. What is Pwnagotchi? Pwnagotchi is a WiFi-auditing tool that carries a face and a personality, running on a small piece of hardware (usually a Raspberry Pi) you take with you. The actual attack work — deauthing, associating, capturing handshakes — is all done by…

完整内容见 GitHub 原始 README 或本页 。

常见问题

pwnagotchi64 用什么语言开发?

主要使用 JavaScript 编写,采用 Other 许可证,目前有 122 个 Star。

pwnagotchi64 可以免费商用吗?

仓库标注的许可证为 Other,商用前请先阅读仓库 LICENSE 文件确认授权范围。

如何获取 pwnagotchi64 的源码?

可以直接克隆仓库:git clone https://github.com/ex18a/pwnagotchi64.git,默认分支为 main;安装与运行步骤见仓库 README。

pwnagotchi64 最近还在维护吗?

仓库最近一次更新为 9月19日,当前没有未关闭的 Issue;Star 数为 122,可作为社区活跃度的参考。

关于

(⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

语言占比

CSS
HTML
JavaScript
Makefile
Python
Shell

Release

贡献者

README

Pwnagotchi 64-Bit AI Edition

For an in-depth guide — how it works, everything this fork changes versus upstream, and how to use it — see USER_GUIDE.md.

What is Pwnagotchi?

Pwnagotchi is a WiFi-auditing tool that carries a face and a personality, running on a small piece of hardware (usually a Raspberry Pi) you take with you. The actual attack work — deauthing, associating, capturing handshakes — is all done by bettercap; a lot of people assume the “AI” is what’s hacking the WiFi, but it isn’t. What the AI does is learn how to run bettercap well: how long to linger on a channel before hopping, how aggressively to hop between channels, how long to wait for a handshake before giving up on a target, how weak a signal is even worth bothering with, and a dozen other timing/behavior knobs. Every epoch, it sees what those settings produced — handshakes captured, time spent blind, targets missed — and that becomes a reward signal nudging its next set of settings. Over time it learns which behavior actually works in whatever environment it’s really operating in, instead of running forever on one fixed configuration that might be great sitting still on a desk and useless out on a walk.

Credit for the original concept and the reinforcement-learning core goes to evilsocket, who created Pwnagotchi — see Acknowledgments below.

About This Fork

This build keeps that same reinforcement-learning core but rebuilds the platform underneath it:

  • 64-bit, PyTorch/stable-baselines3 instead of the original’s older 32-bit, TensorFlow-based A2C implementation — faster epoch processing and better training stability.
  • Kali Linux base instead of the legacy 32-bit image, for native Nexmon firmware support — reliable monitor mode and packet injection without extra driver work.

Hardware Support: specifically optimized for the Raspberry Pi Zero 2 W with waveshare eink 2.13 v4

*Note: this image may run on other ARM64 devices with different setups.


Configuration / Usage

ssh login is user: pwn , password: raspberry

default ip for gadgetmode is 10.42.0.2

User configuration file is at /etc/pwnagotchi/config.toml.

Created/edited by running sudo pwnagotchi --setup.

Do not edit /etc/pwnagotchi/default.toml — it is overwritten on every restart.


Building from Source

This project uses Docker to create a clean, reproducible build environment. This ensures your system stays clean and the build succeeds regardless of your local Linux distribution.

Requirements:

  • Docker installed and configured.

Instructions:

  1. Clone the repository.
  2. Run:
make

The build process will automatically:

  • Package the source code.
  • Launch an isolated Debian container.
  • Download the official Kali base image and apply all security patches, Bluetooth drivers, and custom UI plugins.
  • Output the final, ready-to-flash image to the pwnagotchi64/dist/ folder called pwnagotchi64-0.0.0.0.img

Flashing SD Card

The easiest way is to use Raspberry PI Imager. dont use any pre-setup features like wifi, you will break it.


Acknowledgments & Credits

This 64-bit build would not be possible without the foundational work and continuous community efforts of the following developers. My fork builds directly upon their heavy lifting:

  • evilsocket - The original creator and architect of the Pwnagotchi project.
  • aluminum-ice - For crucial contributions to the core codebase.
  • jayofelony - For crucial contributions to the core codebase.

If I have inadvertently used your code, script, or concept without proper attribution, thank you for your indirect help! Please open an issue so I can ensure you are properly credited here.

This project is open-source and inherits the original GPL-3.0 License.

评论

暂无评论