大语言模型驱动的口令管理系统优化与实践

As the number of internet services continues to grow, password management has become a significant chal-lenge. Although password management system (PMS) provide secure solutions, their usability is limited by design flaws in password strength meters (PSM) and non-random password generators (NRPG), leading to inaccurate password as-sessments, insufficient password strength, and poor memorability. To address these issues, this paper proposes an optimization scheme for PMS based on large language model (LLM). The proposed approach combines fine-tuning techniques with retrieval-augmented generation, creating a specialized LLM model for password security that can effect-ively identify weak passwords and extract deep semantic features. Meanwhile, an innovative NRPG framework enhances both password strength and memorability. The accuracy of the PSM is optimized through an improved Zxcvbn algorithm and password guessing model. This solution significantly enhances the usability of PMS and promotes its widespread adoption in practical applications.