VulDec: Vulnerability Detection in Binaries via Neural Decompilation

Overview & Synopsis

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 VulDec, consisting of (i) a new large-scale, carefully curated dataset aligning real-world Assembly-C function pairs with detailed CWE annotations across multiple architectures, and (ii) the first end-to-end framework that leverages neural decompilation for binary vulnerability detection. Tuned LLM-based neural decompilers trained on our dataset achieve strong source-level recovery across architectures (Edit Similarity of approximately 56%, BLEU-4 of approximately 43%), while our tuned LLM-based detectors reach a macro F1-score of 0.61 on an in-distribution realistic test set (3% vulnerability rate). Our findings provide evidence that neural decompilation offers a powerful alternative to conventional low-level binary representations, facilitating robust binary vulnerability detection.