1 citations · 1 across the 4 of their papers we have counts for
5 papers
Narratives Mislead: An Empirical Study of How LLMs Misjudge CVSS Severity in Bug Bounty Reports
Ruitao Feng, Yuekang Li, Yao Zhang
Bug bounty platforms receive vulnerability reports faster than vendors can triage them, and Large Language Models (LLMs) are increasingly considered for assigning the Common Vulner…
Decoupling is a Necessity: Transformation-Agnostic Decompiled Code Recovery under Optimization and Obfuscation
Zhiping Zhou, Xiaohong Li, Ruitao Feng +3
Reverse engineering is essential for software security analysis and vulnerability detection. Decompilation, the process of lifting binaries to high-level pseudocode, is central to…
Variation is the Key: A Variation-Based Framework for LLM-Generated Text Detection
Xuecong Li, Xiaohong Li, Qiang Hu +2
Detecting text generated by large language models (LLMs) is crucial but challenging. Existing detectors depend on impractical assumptions, such as white-box settings, or solely rel…
FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation
Zhiping Zhou, Xiaohong Li, Ruitao Feng +5
Decompilation converts machine code into human-readable form, enabling analysis and debugging without source code. However, fidelity issues often degrade the readability and semant…
It Only Gets Worse: Revisiting DL-Based Vulnerability Detectors from a Practical Perspective
Yunqian Wang, Xiaohong Li, Yao Zhang +3
With the growing threat of software vulnerabilities, deep learning (DL)-based detectors have gained popularity for vulnerability detection. However, doubts remain regarding their c…