PassGAN

PassGAN is a research implementation of a generative adversarial network trained to model human password patterns. It reproduces ideas from the paper “PassGAN: A Deep Learning Approach for Password Guessing” using a modified Wasserstein GAN implementation. The repository includes scripts for training models and generating candidate password samples from learned distributions. A pretrained model based on the RockYou dataset is provided for reproducing experiments. TensorFlow and CUDA were used by the original implementation, reflecting the deep-learning tooling available when the project was created. The maintainer also documented experimental results and questioned how the approach compared with established RNN and Markov methods. Because generated candidates can be used in password-recovery attacks, the software is most appropriate for authorized security research and password-strength analysis.