Year: 2025
Type: conference-paper
Abstract: Statically detecting vulnerabilities at the binary level is crucial for the security of Commercial-Off-The-Shelf (COTS) software when source code is not available. However, traditional methods suffer from the inherent limitations of binary translation and static analysis, which hinder their scalability for complex real-world binaries. Recent efforts that leverage Large Language Models (LLMs) for vulnerability detection are still limited by possible hallucination, inaccurate code property retrieval, and insufficient guidance.
Authors: Xiang Chen, Anshunkang Zhou, Chengfeng Ye, Charles Zhang
Institution: Hong Kong University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4415002686