arXiv Machine Learning October 28, 2026 By Francesco Crescenzo Grasso, Pietro Liguori, Roberto Natella, Domenico Cotroneo

VulDec: Vulnerability Detection in Binaries via Neural Decompilation

Read the original on arXiv Machine Learning →

VulDec is a new framework for detecting vulnerabilities in binary executables. It introduces a large, curated dataset that pairs real-world assembly functions with source code and detailed CWE annotations across multiple architectures. Using LLM-based neural decompilers trained on this dataset, the system achieves strong source-level recovery and a macro F1-score of 0.61 on realistic test data, demonstrating that neural decompilation can effectively replace traditional low-level binary representations for vulnerability detection.

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