Pwnagotchi
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About
Pwnagotchi is an open-source, AI-powered tool designed for WiFi security auditing and research. Built on the A2C (Advantage Actor-Critic) deep reinforcement learning framework, it runs primarily on a Raspberry Pi Zero W. Unlike static tools, Pwnagotchi learns from its environment to maximize the collection of WPA key material, specifically capturing handshakes and PMKIDs that can later be cracked with tools like hashcat. It functions as a digital pet that interacts with its user through an e-ink display, changing its mood and personality based on the successful acquisition of network data. The technical core of the system is powered by bettercap, which handles the low-level network interactions. The AI component allows the device to tune its own parameters over time, such as channel hopping frequency and deauthentication timing, rather than following a fixed algorithm. Users can operate the device in two primary modes: MANU for manual data transfer and configuration, and AUTO or AI for autonomous scanning. It supports both passive sniffing and active association attacks, storing all captured data as PCAP files on its local storage for easy export and analysis. This tool is primarily intended for cybersecurity professionals, penetration testers, and hobbyists interested in the practical application of reinforcement learning and wireless networking. It provides a tangible way to observe how AI agents interact with real-world environments. The hardware requirements are specific but affordable, making it accessible for educational purposes and wardriving experiments. It is a highly customizable platform, supporting a robust plugin architecture that can extend its functionality to include features like GPS logging, cloud data synchronization, and custom UI elements. What distinguishes Pwnagotchi from other WiFi auditing tools is its gamified approach and the use of reinforcement learning. By personifying the hacking process with a Tamagotchi-inspired interface, it creates an engaging experience for the user while performing complex security tasks. Additionally, the system features a unique collaborative element where multiple Pwnagotchis can communicate and coordinate their efforts when they are in close proximity. This cooperative behavior, combined with the adaptive nature of its learning algorithm, makes it a sophisticated yet whimsical tool for exploring modern network security.
Pros & Cons
Uses adaptive reinforcement learning to optimize capture rates based on environmental factors.
Highly portable and cost-effective using standard Raspberry Pi hardware components.
Extremely customizable through a robust plugin system and localized language support.
Provides a gamified, interactive experience that makes network security auditing engaging.
Supports community-driven cooperative behavior for improved efficiency when units are near each other.
The AI component requires a significant warm-up time of roughly 30 minutes on every boot.
Lacks a hardware clock on the Pi Zero W, leading to unreliable log timestamps without external fixes.
Requires manual hardware assembly and firmware flashing which may be difficult for beginners.
The physical appearance of the assembled device can be mistaken for suspicious electronics in public spaces.
Use Cases
Security researchers can automate the collection of WPA handshakes to study network vulnerabilities using adaptive AI agents.
Penetration testers can use the device for passive reconnaissance during physical security assessments to gather network credentials.
Computer science students can learn the practical implementation of Advantage Actor-Critic reinforcement learning in a real-world setting.
WiFi enthusiasts can gamify the process of wardriving while contributing data to community-driven network maps.
System administrators can audit their own wireless infrastructure resilience against automated deauthentication and association attacks.
Platform
Task
Features
• web-based dashboard
• white-listing capabilities
• cooperative multi-unit communication
• passive and active attack modes
• plugin-based extensibility
• e-ink display support
• bettercap integration
• a2c deep reinforcement learning
FAQs
What is the primary function of Pwnagotchi?
It is an AI that uses reinforcement learning to capture WPA handshakes and PMKIDs from surrounding WiFi networks for security auditing purposes.
What hardware do I need to build one?
The standard build requires a Raspberry Pi Zero W, a high-quality SD card, and a compatible e-ink display like the WaveShare v2 HAT.
Can I use Pwnagotchi without a physical screen?
Yes, you can access the device's interface and monitor its status through the built-in web UI when connected to a computer or mobile device.
How does the AI improve over time?
Using the A2C framework, the AI tunes its parameters for channel hopping and attack frequency based on a reward function to maximize handshake captures.
Is 5GHz WiFi supported?
The software supports it, but the default Raspberry Pi Zero W hardware only sees 2.4GHz; you would need external hardware for 5GHz support.
How can I prevent it from attacking my own home network?
You can add your specific SSIDs or BSSIDs to a whitelist in the configuration file to prevent the device from performing deauthentication attacks on them.
Why does the AI take a long time to start?
On boot, the device loads several large machine learning libraries into memory, which can take approximately 30 minutes on the Raspberry Pi Zero W hardware.
What happens when two Pwnagotchis meet in the wild?
They exchange information such as their names and number of pwned networks, then coordinate their channels to work together more effectively.
Where are the captured handshakes stored?
All captured WPA handshakes are stored as PCAP files within the /root/handshakes/ directory on the device's internal storage.
Pricing Plans
Open Source
Free Plan• GPL3 Licensed
• Deep Reinforcement Learning
• Community Discord access
• Plugin architecture
• Web management UI
• Multi-language support
• PCAP data logging
• Hardware-agnostic software core
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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