PassGAN: A Deep Learning Approach for Password Guessing

TL;DR

PassGAN introduces a deep learning-based password guessing model using GANs to autonomously learn password distributions from leaks, outperforming traditional rule-based tools without prior password knowledge.

Contribution

This paper presents PassGAN, the first GAN-based password guessing tool that learns password patterns directly from data, eliminating the need for manual rule creation.

Findings

PassGAN surpasses rule-based and other machine learning password guessing tools.

Combining PassGAN with HashCat increases password recovery by 51%-73%.

PassGAN can autonomously learn password properties without prior knowledge.

Abstract

State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., "password123456") and leet speak (e.g., "password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode.