# Improving Password Guessing via Representation Learning

arXiv: https://arxiv.org/abs/1910.04232 · HTML: https://mlsanity.com/paper/1910.04232/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 14 of 14)

- [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)
- [SE#PCFG: Semantically Enhanced PCFG for Password Analysis and Cracking](https://mlsanity.com/paper/2306.06824/) (2023)
- [GNPassGAN: Improved Generative Adversarial Networks For Trawling Offline Password Guessing](https://mlsanity.com/paper/2208.06943/) (2022)
- [Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem](https://mlsanity.com/paper/2403.03593/) (2024)
- [Targeted Honeyword Generation with Language Models](https://mlsanity.com/paper/2208.06946/) (2022)
- [Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data](https://mlsanity.com/paper/2301.07628/) (2023)
- [PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer](https://mlsanity.com/paper/2404.04886/) (2024)
- [On Deep Learning in Password Guessing, a Survey](https://mlsanity.com/paper/2208.10413/) (2022)
- [PassTSL: Modeling Human-Created Passwords through Two-Stage Learning](https://mlsanity.com/paper/2407.14145/) (2024)
- [MAYA: Addressing Inconsistencies in Generative Password Guessing through a Unified Benchmark](https://mlsanity.com/paper/2504.16651/) (2025)
- [PassREfinder-FL: Privacy-Preserving Credential Stuffing Risk Prediction via Graph-Based Federated Learning for Representing Password Reuse between Websites](https://mlsanity.com/paper/2510.16083/) (2025)
- [Multilingual AI-Driven Password Strength Estimation with Similarity-Based Detection](https://mlsanity.com/paper/2603.10217/) (2026)
- [Red-Teaming the Agentic Red-Team](https://mlsanity.com/paper/2606.24496/) (2026)

## Abstract (arXiv)

Learning useful representations from unstructured data is one of the core challenges, as well as a driving force, of modern data-driven approaches. Deep learning has demonstrated the broad advantages of learning and harnessing such representations. In this paper, we introduce a deep generative model representation learning approach for password guessing. We show that an abstract password representation naturally offers compelling and versatile properties that can be used to open new directions in the extensively studied, and yet presently active, password guessing field. These properties can establish novel password generation techniques that are neither feasible nor practical with the existing probabilistic and non-probabilistic approaches. Based on these properties, we introduce:(1) A general framework for conditional password guessing that can generate passwords with arbitrary biases; and (2) an Expectation Maximization-inspired framework that can dynamically adapt the estimated password distribution to match the distribution of the attacked password set.