Using autoregressive character-level language model to augment wordlists?
#2
This is one of the main functions of rules. Instead of just trying previously seen passwords like "octopus", we can try "octopus1", "Octopus", "OcToPus" etc. As for your question, it's impossible to predict by how much. There are many models/applications that try to do this kind of thing, like the PRINCE processor but by far the most effective is just to simply run rules. With just flat files, we can process a few million candidates per second, with rules, we can process a few tens/hundreds of billions of candidates per second due to how GPUs can generate their own work instead of constantly having to request it from the CPU. There have been many, many planned ideas for integrating AI models into Hashcat but they're often just simply not good enough and too slow or complex to be added directly into Hashcat, especially on the directly on the GPU which would be preferable.

Related links:
https://hashcat.net/wiki/doku.php?id=rule_based_attack
https://github.com/hashcat/princeprocessor
https://github.com/hashcat/hashcat/issues/3923
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RE: Using autoregressive character-level language model to augment wordlists? - by penguinkeeper - 12-26-2023, 04:04 AM