# PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer

arXiv: https://arxiv.org/abs/2404.04886 · HTML: https://mlsanity.com/paper/2404.04886/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 56 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [PassGAN: A Deep Learning Approach for Password Guessing](https://mlsanity.com/paper/1709.00440/) (2017)
- [A survey on text generation using generative adversarial networks](https://mlsanity.com/paper/2212.11119/) (2022)
- [Improving Password Guessing via Representation Learning](https://mlsanity.com/paper/1910.04232/) (2019)
- [PassFlow: Guessing Passwords with Generative Flows](https://mlsanity.com/paper/2105.06165/) (2021)
- [PassGPT: Password Modeling and (Guided) Generation with Large Language Models](https://mlsanity.com/paper/2306.01545/) (2023)
- [GNPassGAN: Improved Generative Adversarial Networks For Trawling Offline Password Guessing](https://mlsanity.com/paper/2208.06943/) (2022)
- [Generative Deep Learning Techniques for Password Generation](https://mlsanity.com/paper/2012.05685/) (2020)

## Built on by (most cited first, 2 of 2)

- [MAYA: Addressing Inconsistencies in Generative Password Guessing through a Unified Benchmark](https://mlsanity.com/paper/2504.16651/) (2025)
- [PassGPT+: Leveraging Linguistic Priors for Password Modeling](https://mlsanity.com/paper/2609.39880/) (2026)

## Abstract (arXiv)

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.