Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the effectiveness of LLMs in detecting software vulnerabilities is largely unexplored. This paper aims to bridge this gap by exploring how LLMs perform with various prompts, particularly focusing on two state-of-the-art LLMs: GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves competitive performance with the prior state-of-the-art vulnerability detection approach and GPT-4 consistently outperformed the state-of-the-art.
@article{arxiv.2401.15468,
title = {Large Language Model for Vulnerability Detection: Emerging Results and Future Directions},
author = {Xin Zhou and Ting Zhang and David Lo},
journal= {arXiv preprint arXiv:2401.15468},
year = {2024}
}
Comments
Accepted in the New Ideas and Emerging Results Track at ICSE 2024