LLM-Guided Fuzzing for Pathological Input Generation
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Presented our research on leveraging Large Language Models to guide fuzzing processes in identifying inputs that trigger worst-case resource consumption. This work explores the intersection of generative AI and software testing to improve system robustness. Recognized with the Best Paper Award.