VulDec: Vulnerability Detection in Binaries via Neural Decompilation

Reported by arXiv cs.CR (Francesco Crescenzo Grasso, Pietro Liguori, Roberto Natella, Domenico Cotroneo)

Summary by Cyber Security News27 Oct 2026, 05:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.33716

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.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

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