Amidst the surge in deep learning-based password guessing models, challenges of generating high-quality passwords and reducing duplicate passwords persist. To address these challenges, we present PagPassGPT, a password guessing model constructed on Generative Pretrained Transformer (GPT). It can perform pattern guided guessing by incorporating pattern structure information as background knowledge, resulting in a significant increase in the hit rate. Furthermore, we propose D&C-GEN to reduce the repeat rate of generated passwords, which adopts the concept of a divide-and-conquer approach. The primary task of guessing passwords is recursively divided into non-overlapping subtasks. Each subtask inherits the knowledge from the parent task and predicts succeeding tokens. In comparison to the state-of-the-art model, our proposed scheme exhibits the capability to correctly guess 12% more passwords while producing 25% fewer duplicates.
@article{arxiv.2404.04886,
title = {PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer},
author = {Xingyu Su and Xiaojie Zhu and Yang Li and Yong Li and Chi Chen and Paulo Esteves-Veríssimo},
journal= {arXiv preprint arXiv:2404.04886},
year = {2024}
}