# PassFlow: Guessing Passwords with Generative Flows

arXiv: https://arxiv.org/abs/2105.06165 · HTML: https://mlsanity.com/paper/2105.06165/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 49 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Invertible Residual Networks](https://mlsanity.com/paper/1811.00995/) (2018)
- [Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design](https://mlsanity.com/paper/1902.00275/) (2019)
- [Residual Flows for Invertible Generative Modeling](https://mlsanity.com/paper/1906.02735/) (2019)
- [Learning Likelihoods with Conditional Normalizing Flows](https://mlsanity.com/paper/1912.00042/) (2019)
- [i-RevNet: Deep Invertible Networks](https://mlsanity.com/paper/1802.07088/) (2018)
- [Learning in Implicit Generative Models](https://mlsanity.com/paper/1610.03483/) (2016)
- [Latent Normalizing Flows for Discrete Sequences](https://mlsanity.com/paper/1901.10548/) (2019)
- [UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats](https://mlsanity.com/paper/2001.01525/) (2020)
- [WaveFlow: A Compact Flow-based Model for Raw Audio](https://mlsanity.com/paper/1912.01219/) (2019)
- [Digital Watermarking for Deep Neural Networks](https://mlsanity.com/paper/1802.02601/) (2018)
- [VFlow: More Expressive Generative Flows with Variational Data Augmentation](https://mlsanity.com/paper/2002.09741/) (2020)
- [MaCow: Masked Convolutional Generative Flow](https://mlsanity.com/paper/1902.04208/) (2019)
- [PassGAN: A Deep Learning Approach for Password Guessing](https://mlsanity.com/paper/1709.00440/) (2017)
- [Improving Password Guessing via Representation Learning](https://mlsanity.com/paper/1910.04232/) (2019)
- [Have You Stolen My Model? Evasion Attacks Against Deep Neural Network Watermarking Techniques](https://mlsanity.com/paper/1809.00615/) (2018)
- [The Naked Sun: Malicious Cooperation Between Benign-Looking Processes](https://mlsanity.com/paper/1911.02423/) (2019)
- [Reliable Detection of Compressed and Encrypted Data](https://mlsanity.com/paper/2103.17059/) (2021)
- [EnCoD: Distinguishing Compressed and Encrypted File Fragments](https://mlsanity.com/paper/2010.07754/) (2020)

## Built on by (most cited first, 10 of 10)

- [PassGPT: Password Modeling and (Guided) Generation with Large Language Models](https://mlsanity.com/paper/2306.01545/) (2023)
- [SoK: On the Offensive Potential of AI](https://mlsanity.com/paper/2412.18442/) (2024)
- [FedComm: Federated Learning as a Medium for Covert Communication](https://mlsanity.com/paper/2201.08786/) (2022)
- [TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding](https://mlsanity.com/paper/2202.06091/) (2022)
- [Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem](https://mlsanity.com/paper/2403.03593/) (2024)
- [PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer](https://mlsanity.com/paper/2404.04886/) (2024)
- [OliVaR: Improving Olive Variety Recognition using Deep Neural Networks](https://mlsanity.com/paper/2303.00431/) (2023)
- [MAYA: Addressing Inconsistencies in Generative Password Guessing through a Unified Benchmark](https://mlsanity.com/paper/2504.16651/) (2025)
- [When Intelligence Fails: An Empirical Study on Why LLMs Struggle with Password Cracking](https://mlsanity.com/paper/2510.17884/) (2025)
- [PassGPT+: Leveraging Linguistic Priors for Password Modeling](https://mlsanity.com/paper/2609.39880/) (2026)

## Abstract (arXiv)

Recent advances in generative machine learning models rekindled research interest in the area of password guessing. Data-driven password guessing approaches based on GANs, language models and deep latent variable models have shown impressive generalization performance and offer compelling properties for the task of password guessing. In this paper, we propose PassFlow, a flow-based generative model approach to password guessing. Flow-based models allow for precise log-likelihood computation and optimization, which enables exact latent variable inference. Additionally, flow-based models provide meaningful latent space representation, which enables operations such as exploration of specific subspaces of the latent space and interpolation. We demonstrate the applicability of generative flows to the context of password guessing, departing from previous applications of flow-networks which are mainly limited to the continuous space of image generation. We show that PassFlow is able to outperform prior state-of-the-art GAN-based approaches in the password guessing task while using a training set that is orders of magnitudes smaller than that of previous art. Furthermore, a qualitative analysis of the generated samples shows that PassFlow can accurately model the distribution of the original passwords, with even non-matched samples closely resembling human-like passwords.