PassGPT
PassGPT is a causal language model trained on password leaks. It was first introduced in this paper. This version of the model was trained on passwords from the RockYou leak, after filtering those that were at most 10 characters long. If you need access to PassGPT trained on passwords up to 16 characters long, you can apply here.
This is a curated version of the model reported in the paper. Vocabulary size was reduced to the most meaningful characters and training was slightly optimized. Results are slightly better with these architectures.
Usage and License Notices
Model description
The model inherits the GPT2LMHeadModel architecture and implements a custom BertTokenizer that encodes each character in a password as a single token, avoiding merges. It was trained from a random initialization, and the code for training can be found in the official repository.
Password Generation
Passwords can be sampled from the model using the built-in generation methods provided by HuggingFace and using the "start of password token" as seed (i.e. <s>). This code can be used to generate one password with PassGPT.
from transformers import GPT2LMHeadModel
from transformers import RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained("javirandor/passgpt-10characters",
max_len=12,
padding="max_length",
truncation=True,
do_lower_case=False,
strip_accents=False,
mask_token="<mask>",
unk_token="<unk>",
pad_token="<pad>",
truncation_side="right")
model = GPT2LMHeadModel.from_pretrained("javirandor/passgpt-10characters").eval()
NUM_GENERATIONS = 1
# Generate passwords sampling from the beginning of password token
g = model.generate(torch.tensor([[tokenizer.bos_token_id]]),
do_sample=True,
num_return_sequences=NUM_GENERATIONS,
max_length=12,
pad_token_id=tokenizer.pad_token_id,
bad_words_ids=[[tokenizer.bos_token_id]])
# Remove start of sentence token
g = g[:, 1:]
decoded = tokenizer.batch_decode(g.tolist())
decoded_clean = [i.split("</s>")[0] for i in decoded] # Get content before end of password token
# Print your sampled passwords!
print(decoded_clean)
You can find a more flexible script for sampling here.
Cite our work
@article{rando2023passgpt,
title={PassGPT: Password Modeling and (Guided) Generation with Large Language Models},
author={Rando, Javier and Perez-Cruz, Fernando and Hitaj, Briland},
journal={arXiv preprint arXiv:2306.01545},
year={2023}
}