2 citations · 2 across the 6 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
cs.CR2026
Triggering and Detecting Exploitable Library Vulnerability from the Client by Directed Greybox Fuzzing
Yukai Zhao, Menghan Wu, Xing Hu +3
Developers utilize third-party libraries to improve productivity, which also introduces potential security risks. Existing approaches generate tests for public functions to trigger…
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…