VulDec: Vulnerability Detection in Binaries via Neural Decompilation
VulDec pairs neural decompilation with ML-based vulnerability detection on raw binaries — promising for supply-chain and firmware audits where source code is unavailable.
Summary written by editorial AI · Source link below
arXiv:2610.33716v1 Announce Type: new Abstract: Automated vulnerability detection in binary executables is challenging due to the lack of source code and the limited semantic information available at the assembly level. Despite significant progress in machine-learning-based code analysis, the field still lacks methodologies and large, high-quality datasets that provide vulnerable code jointly at assembly- and source-level with precise vulnerability labels. To address this gap, we introduce VulD
Editorial Analysis
Automated binary vulnerability detection could transform third-party software assessments, particularly where vendors refuse to share source code under supply-chain due diligence.
Monitor VulDec's progress and consider piloting neural-decompilation-based analysis for upcoming third-party software and firmware security reviews.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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