用优化后的数据集微调LLaMA 3.2，提升漏洞检测准确率。
Evaluating LLaMA 3.2 for Software Vulnerability Detection
- 基于真实项目构建并优化了DiverseVul数据集用于训练。
- 微调后模型F1分数达66%，较基线47%显著提升。
- 适合关注代码安全与大模型应用的研究者参考。
深度学习（DL）已成为漏洞检测的强大工具，常优于传统方法。但构建有效模型需大量真实数据，获取困难。为此，研究者构建了目前最大的真实项目中提取的C/C++函数漏洞数据集DiverseVul，旨在提供高质量、大规模样本。然而在预处理过程中发现原始数据存在若干不一致问题，亟需优化版本。本文提出一个经修正的DiverseVul数据集，并用于微调大型语言模型LLaMA 3.2以实现漏洞检测。实验表明，采用预处理技术后模型性能显著提升，最终达到66%的F1分数，相较基线47%有明显改进，表现具有竞争力。
原文摘要 · Abstract (English)
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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