Pwnagotchi is an A2C-based "AI" leveraging bettercap that learns from its surrounding WiFi environment to maximize the crackable WPA key material it captures (either passively, or by performing authentication and association attacks). This material is collected as PCAP files containing any form of handshake supported by hashcat, including PMKIDs, full and half WPA handshakes.
Instead of merely playing Super Mario or Atari games like most reinforcement learning-based "AI" (yawn), Pwnagotchi tunes its parameters over time to get better at pwning WiFi things to in the environments you expose it to.
More specifically, Pwnagotchi is using an LSTM with MLP feature extractor as its policy network for the A2C agent. If you're unfamiliar with A2C, here is a very good introductory explanation (in comic form!) of the basic principles behind how Pwnagotchi learns. (You can read more about how Pwnagotchi learns in the Usage doc.)
Keep in mind: Unlike the usual RL simulations, Pwnagotchi learns over time. Time for a Pwnagotchi is measured in epochs; a single epoch can last from a few seconds to minutes, depending on how many access points and client stations are visible. Do not expect your Pwnagotchi to perform amazingly well at the very beginning, as it will be exploring several combinations of key parameters to determine ideal adjustments for pwning the particular environment you are exposing it to during its beginning epochs ... but ** listen to your Pwnagotchi when it tells you it's boring!** Bring it into novel WiFi environments with you and have it observe new networks and capture new handshakes—and you'll see. :)
Multiple units within close physical proximity can "talk" to each other, advertising their presence to each other by broadcasting custom information elements using a parasite protocol I've built on top of the existing dot11 standard. Over time, two or more units trained together will learn to cooperate upon detecting each other's presence by dividing the available channels among them for optimal pwnage.
Fikolmij's/Dal's changes: Changes made to stop bootloops on Banana Pi and Orange Pi. Removed all calls to brcm and led as we don't install nexmon (yet) and the led isn't compatible on most boards. Also removed the numpy requirement to make the install instructions easier. Added correct jinja2, itsdangerous and Werkzeug to requirements.
A utility tool has been added to easily switch between Pwnagotchi operational modes on Banana Pi and Orange Pi devices. The switcher is located in the /scripts/ directory and provides a simple command-line interface to manage your Pwnagotchi's mode.
- AUTO: Automatic mode for passive WiFi handshake collection
- AI: AI-powered mode with reinforcement learning
- MANU: Manual mode for management and maintenance
# Check current mode and status
sudo python3 scripts/pwnagotchi-switcher.py status
# Switch to AUTO mode
sudo python3 scripts/pwnagotchi-switcher.py auto
# Switch to AI mode
sudo python3 scripts/pwnagotchi-switcher.py ai
# Restart the pwnagotchi service
sudo python3 scripts/pwnagotchi-switcher.py restartFor detailed usage instructions, configuration options, and troubleshooting, see USAGE.md.
To install the switcher as a system-wide command, run the installation script:
cd scripts
sudo bash install.shAfter installation, you can use pwn-switch command instead of the full path.
| Official Links | |
|---|---|
| Website | pwnagotchi.ai |
| Forum | community.pwnagotchi.ai |
| Slack | pwnagotchi.slack.com |
| Subreddit | r/pwnagotchi |
| @pwnagotchi |
pwnagotchi is made with ♥ by @evilsocket and the amazing dev team. It is released under the GPL3 license.