# On Deep Learning in Password Guessing, a Survey

arXiv: https://arxiv.org/abs/2208.10413 · HTML: https://mlsanity.com/paper/2208.10413/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [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)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://mlsanity.com/paper/2006.05132/) (2020)
- [Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect](https://mlsanity.com/paper/1803.01541/) (2018)
- [Adversarial Lipschitz Regularization](https://mlsanity.com/paper/1907.05681/) (2019)
- [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)
- [Wasserstein Divergence for GANs](https://mlsanity.com/paper/1712.01026/) (2017)
- [Improving Password Guessing via Representation Learning](https://mlsanity.com/paper/1910.04232/) (2019)

## Built on by (most cited first, 1 of 1)

- [When Intelligence Fails: An Empirical Study on Why LLMs Struggle with Password Cracking](https://mlsanity.com/paper/2510.17884/) (2025)

## Abstract (arXiv)

The security of passwords is dependent 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 be representative of the actual threat. This approach, however, needs domain-specific knowledge and expertise that are difficult to duplicate. This paper compares various deep learning-based password guessing approaches that do not require domain knowledge or assumptions about users' password structures and combinations. The involved model categories are Recurrent Neural Networks, Generative Adversarial Networks, Autoencoder, and Attention mechanisms. Additionally, we proposed a promising research experimental design on using variations of IWGAN on password guessing under non-targeted offline attacks. Using these advanced strategies, we can enhance password security and create more accurate and efficient Password Strength Meters.