Passwords still remain a dominant authentication method, yet their security is routinely subverted by predictable user choices and large-scale credential leaks. Automated password guessing is a key tool for stress-testing password policies and modeling attacker behavior. This paper applies LLM-driven evolutionary computation to automatically optimize prompts for the LLM password guessing framework. Using OpenEvolve, an open-source system combining MAP-Elites quality-diversity search with an island population model we evolve prompts that maximize cracking rate on a RockYou-derived test set. We evaluate three configurations: a local setup with Qwen3 8B, a single compact cloud model Gemini-2.5 Flash, and a two-model ensemble of frontier LLMs. The approach raises the cracking rates from 2.02\% to 8.48\%. Character distribution analysis further confirms how evolved prompts produce statistically more realistic passwords. Automated prompt evolution is a low-barrier yet effective way to strengthen LLM-based password auditing and underlining how attack pipelines show tendency via automated improvements.

翻译：密码仍然是一种主要的身份验证方法，但其安全性经常因用户的可预测选择和大规模凭证泄露而受到削弱。自动化密码猜测是压力测试密码策略和建模攻击者行为的关键工具。本文应用LLM驱动的进化计算来自动优化LLM密码猜测框架中的提示词。通过使用OpenEvolve（一个结合MAP-Elites质量多样性搜索与岛屿种群模型的开源系统），我们演化出能够在RockYou衍生测试集上最大化破解率的提示词。我们评估了三种配置：本地设置使用Qwen3 8B、单一紧凑云模型Gemini-2.5 Flash，以及一个由前沿LLM组成的双模型集成。该方法将破解率从2.02%提升至8.48%。字符分布分析进一步证实了演化后的提示词能够生成统计上更逼真的密码。自动化提示演化是一种低门槛但有效的方法，能够增强基于LLM的密码审计，并凸显了攻击流程如何通过自动化改进展现出增强趋势。