Essay Journal Article Report
STEM, Research, The University of Hong Kong, Leadership & Research Laidlaw Scholars, Laidlaw Conference 2025

Fuzzing with Hopper++: Applying LLM to Program Generation and Software Security

This research focuses on leveraging the power of Generative AI and applying Large Language Models to fuzz testing. Based on the previous interpretative fuzzing tool Hopper, this work introduces Hopper++, an enhanced version that incorporates an LLM module to automate fuzzing.
Go to the profile of Wang Hanyu
Wang Hanyu
student, The University of Hong Kong
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Go to the profile of Wang Hanyu
Wang Hanyu (She/Her)
student, The University of Hong Kong

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