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.

翻译：在基于深度学习的密码猜测模型日益涌现之际，生成高质量密码与降低重复密码的挑战依然存在。为应对这些挑战，我们提出PagPassGPT——一种基于生成式预训练Transformer（GPT）构建的密码猜测模型。该模型通过将模式结构信息作为背景知识融入其中，实现模式引导的猜测，从而显著提升命中率。此外，我们提出D&C-GEN以降低生成密码的重复率，该方法采用分而治之的策略思想：将密码猜测的主要任务递归分解为互不重叠的子任务，每个子任务继承父任务的知识并预测后续令牌。与当前最先进的模型相比，我们的方案能够正确猜测出多12%的密码，同时减少25%的重复密码生成。