Large Language Model for Vulnerability Detection: Emerging Results and Future Directions
CoRR(2024)
摘要
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.
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