3 papers
cs.SE2026
LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer
Kunpeng Zhang, Dongwei Xiao, Daoyuan Wu +5
Deep learning (DL) libraries are widely used in critical applications, where even subtle silent bugs can lead to serious consequences. While existing DL fuzzing techniques have mad…
cs.SE2025
Low-Cost and Comprehensive Non-textual Input Fuzzing with LLM-Synthesized Input Generators
Kunpeng Zhang, Zongjie Li, Daoyuan Wu +2
Modern software often accepts inputs with highly complex grammars. Recent advances in large language models (LLMs) have shown that they can be used to synthesize high-quality natur…
cs.SE2025
Your Fix Is My Exploit: Enabling Comprehensive DL Library API Fuzzing with Large Language Models
Kunpeng Zhang, Shuai Wang, Jitao Han +4
Deep learning (DL) libraries, widely used in AI applications, often contain vulnerabilities like buffer overflows and use-after-free errors. Traditional fuzzing struggles with the…