LLM-Guided Prompt Evolution for Password Guessing
This work addresses the limitations of traditional password guessing approaches, which poorly emulate real-world attacker behavior, and existing large language model (LLM)-based methods that rely heavily on handcrafted prompts. The authors propose OpenEvolve, a novel system that introduces LLM-guided prompt evolution into password guessing for the first time, integrating MAP-Elites quality-diversity search with island-based population evolution to enable fully automated, human-intervention-free prompt optimization. Evaluated on RockYou-derived test sets, OpenEvolve significantly improves cracking rates from 2.02% to 8.48% and generates passwords whose character distributions more closely mirror those of real users. Experiments employing Qwen3-8B (local), Gemini-2.5 Flash (cloud), and an ensemble configuration demonstrate consistent attack performance gains across models, substantially enhancing the effectiveness of LLM-driven password auditing.