4 papers
On the Reliability of LLM-Based Vulnerability Patching Benchmarks
Dang K Le, Wenxuan Shi, Xinyu Xing
Large language models (LLMs) have shown strong potential for automated vulnerability patching, but current benchmarks can substantially distort reported performance. Drawing on ext…
Patch Validation in Automated Vulnerability Repair
Zheng Yu, Wenxuan Shi, Xinqian Sun +3
Automated Vulnerability Repair (AVR) systems, especially those leveraging large language models (LLMs), have demonstrated promising results in patching vulnerabilities -- that is,…
BandFuzz: An ML-powered Collaborative Fuzzing Framework
Wenxuan Shi, Hongwei Li, Jiahao Yu +3
Collaborative fuzzing combines multiple individual fuzzers and dynamically chooses appropriate combinations for different programs. Unlike individual fuzzers that rely on specific…
Harnessing Large Language Models for Seed Generation in Greybox Fuzzing
Wenxuan Shi, Yunhang Zhang, Xinyu Xing +1
Greybox fuzzing has emerged as a preferred technique for discovering software bugs, striking a balance between efficiency and depth of exploration. While research has focused on im…