  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.
