Improving Real-world Password Guessing Attacks via Bi-directional Transformers

Password guessing attacks, prevalent issues in the real world, can be conceptualized as efforts to approximate the probability distribution of text tokens. Techniques in the natural language processing (NLP) field naturally lend themselves to password guessing. Among them, bi-directional transformers stand out with their ability to utilize bi-directional contexts to capture the nuances in texts.

To further improve password guessing attacks, we propose a bi-directional-transformer-based guessing framework, referred to as PassBERT, which applies the pre-training / fine-tuning paradigm to password guessing attacks. We first prepare a pre-trained password model, which contains the knowledge of the general password distribution. Then, we design three attack-specific fine-tuning approaches to tailor the pre-trained password model to the following real-world attack scenarios: (1) conditional password guessing, which recovers the complete password given a partial password; (2) targeted password guessing, which compromises the password(s) of a specific user using their personal information; (3) adaptive rule-based password guessing, which selects adaptive mangling rules for a word (i.e., base password) to generate rule-transformed password candidates. The experimental results show that our fine-tuned models can outperform the state-of-the-art models by 14.53%, 21.82% and 4.86% in the three attacks, respectively, demonstrating the effectiveness of bi-directional transformers on downstream guessing attacks. Finally, we propose a hybrid password strength meter to mitigate the risks from the three attacks.