  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.
