PassGAN

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PassGAN is an open-source deep learning tool for password guessing, based on a research paper. It provides a command-line interface and a pretrained model for generating password samples.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is PassGAN?

PassGAN is an open-source deep learning tool for password guessing, implementing the approach described in the paper "PassGAN: A Deep Learning Approach for Password Guessing." This repository provides a modified TensorFlow implementation of Improved Training of Wasserstein GANs, making it easy to train and sample from the model. It includes a command-line interface for generating password samples and training custom models. A pretrained PassGAN model, trained on the RockYou dataset, is also provided. Users can train their own models using various password leaks and datasets, with instructions for downloading common datasets like the LinkedIn leak. The tool is released under an MIT License, acknowledging the original authors of the PassGAN paper and the underlying WGAN training code.

Best used for

Ideal for security researchers and data scientists who need to analyze password strength, generate large sets of synthetic passwords, and train deep learning models for password guessing. Especially valuable for academic research and penetration testing scenarios.

Common actions

guess passwords

train deep learning models

generate password samples

research password security

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Capabilities

Key features

Deep learning password guessing
Generative Adversarial Network (GAN)
Command-line interface
Pretrained RockYou model
Custom model training
Password sample generation

Target Audience

data scientistprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary purpose of PassGAN?

PassGAN is designed to apply deep learning, specifically Generative Adversarial Networks (GANs), to the problem of password guessing. It allows users to generate large numbers of potential passwords based on learned patterns from existing datasets, aiding in security research and analysis.

Can I train PassGAN with my own password datasets?

Yes, PassGAN is designed to allow users to train their own models using custom password leaks and datasets. The repository provides instructions and examples for how to prepare and use your own training data with the provided scripts.

What are the technical requirements to run PassGAN?

To run PassGAN, you will need CUDA 8 pre-installed for GPU acceleration, as well as the Python dependencies listed in the requirements.txt file. Training on large datasets can be computationally intensive, often requiring powerful GPUs like a GTX 1080 for several hours.

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Also listed in

This tool also appears in

Research & Education › Academic Research Data & Analytics › Predictive Analytics

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