Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?

• URL: http://arxiv.org/abs/2412.07538v1
• Date: Tue, 10 Dec 2024 14:17:14 GMT
• Title: Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?
• Authors: D. Cotroneo, F. C. Grasso, R. Natella, V. Orbinato,
• Abstract summary: This paper presents an experimental study to predict vulnerabilities in binary code without source code or complex representations of the binary. The results outperform the state-of-the-art in both neural decompilation and vulnerability prediction.
• Score: 0.0
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• Abstract: Vulnerability prediction is valuable in identifying security issues more efficiently, even though it requires the source code of the target software system, which is a restrictive hypothesis. This paper presents an experimental study to predict vulnerabilities in binary code without source code or complex representations of the binary, leveraging the pivotal idea of decompiling the binary file through neural decompilation and predicting vulnerabilities through deep learning on the decompiled source code. The results outperform the state-of-the-art in both neural decompilation and vulnerability prediction, showing that it is possible to identify vulnerable programs with this approach concerning bi-class (vulnerable/non-vulnerable) and multi-class (type of vulnerability) analysis.

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