Context-based password cracking dictionary expansion using generative pre-trained transformers

With the rise of online criminal activity leading to the increasing importance of digital forensics, efficient and effective password-cracking tools are necessary to collect evidence in a timely manner, leading to solved crimes. Recent advances in machine learning and artificial intelligence have led to the development of context-based and large language model approaches, significantly improving the accuracy and efficiency of password cracking. This work focusses on these more modern techniques, specifically creating context-based contextual password dictionaries through training a series of PassGPTs, a large language model capable of creating password candidates from leaked password dictionary lists. This paper explores possible improvements in password cracking techniques to help law enforcement agencies in digital forensic investigations by combining PassGPT with a contextual approach.