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arXiv · 2503.07770

Evaluating LLaMA 3.2 for Software Vulnerability Detection

José Gonçalves·Miguel Silva·Bernardo Cabral·Tiago Dias·Eva Maia·Isabel Praça·Ricardo Severino·Luís Lino Ferreira
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Abstract

Deep Learning (DL) has emerged as a powerful tool for vulnerability detection, often outperforming traditional solutions. However, developing effective DL models requires large amounts of real-world data, which can be difficult to obtain in sufficient quantities. To address this challenge, DiverseVul dataset has been curated as the largest dataset of vulnerable and non-vulnerable C/C++ functions extracted exclusively from real-world projects. Its goal is to provide high-quality, large-scale samples for training DL models. However, during our study several inconsistencies were identified in the raw dataset while applying pre-processing techniques, highlighting the need for a refined version. In this work, we present a refined version of DiverseVul dataset, which is used to fine-tune a large language model, LLaMA 3.2, for vulnerability detection. Experimental results show that the use of pre-processing techniques led to an improvement in performance, with the model achieving an F1-Score of 66%, a competitive result when compared to our baseline, which achieved a 47% F1-Score in software vulnerability detection.

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José Gonçalves, Miguel Silva, Bernardo Cabral, Tiago Dias, Eva Maia, Isabel Praça, Ricardo Severino, Luís Lino Ferreira. 2025-03-10. Evaluating LLaMA 3.2 for Software Vulnerability Detection. https://arxiv.org/abs/2503.07770

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