# GNPassGAN: Improved Generative Adversarial Networks For Trawling Offline Password Guessing

arXiv: https://arxiv.org/abs/2208.06943 · HTML: https://mlsanity.com/paper/2208.06943/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 14 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [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)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [PassGAN: A Deep Learning Approach for Password Guessing](https://mlsanity.com/paper/1709.00440/) (2017)
- [Gradient Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/2109.02235/) (2021)
- [Improving Password Guessing via Representation Learning](https://mlsanity.com/paper/1910.04232/) (2019)
- [Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries](https://mlsanity.com/paper/2010.12269/) (2020)

## Built on by (most cited first, 3 of 3)

- [Targeted Honeyword Generation with Language Models](https://mlsanity.com/paper/2208.06946/) (2022)
- [PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer](https://mlsanity.com/paper/2404.04886/) (2024)
- [Multilingual AI-Driven Password Strength Estimation with Similarity-Based Detection](https://mlsanity.com/paper/2603.10217/) (2026)

## Abstract (arXiv)

The security of passwords depends on a thorough understanding of the strategies used by attackers. Unfortunately, real-world adversaries use pragmatic guessing tactics like dictionary attacks, which are difficult to simulate in password security research. Dictionary attacks must be carefully configured and modified to represent an actual threat. This approach, however, needs domain-specific knowledge and expertise that are difficult to duplicate. This paper reviews various deep learning-based password guessing approaches that do not require domain knowledge or assumptions about users' password structures and combinations. It also introduces GNPassGAN, a password guessing tool built on generative adversarial networks for trawling offline attacks. In comparison to the state-of-the-art PassGAN model, GNPassGAN is capable of guessing 88.03% more passwords and generating 31.69% fewer duplicates.