密码
计算机科学
杠杆(统计)
对抗制
熵(时间箭头)
匹配(统计)
计算机安全
密码强度
密码策略
人工智能
一次性密码
统计
数学
物理
量子力学
作者
Javier Rando,Fernando Pérez‐Cruz,Briland Hitaj
标识
DOI:10.1007/978-3-031-51482-1_9
摘要
Large language models (LLMs) successfully model natural language from vast amounts of text without the need for explicit supervision. In this paper, we investigate the efficacy of LLMs in modeling passwords. We present PassGPT, an LLM trained on password leaks for password generation. PassGPT outperforms existing methods based on generative adversarial networks (GAN) by guessing twice as many previously unseen passwords. Furthermore, we introduce the concept of guided password generation, where we leverage PassGPT sampling procedure to generate passwords matching arbitrary constraints, a feat lacking in current GAN-based strategies. Lastly, we conduct an in-depth analysis of the entropy and probability distribution that PassGPT defines over passwords and discuss their use in enhancing existing password strength estimators.
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