Large Language Model for Vulnerability Detection: Emerging Results and Future Directions
Learning-based approaches for identifying software vulnerabilities have been a topic of enduring interest in the software security domain. Previous vulnerability detection methods relying on machine learning have predominantly utilized medium-sized pre-trained models such as CodeBERT or trained smaller neural networks from scratch. Recently, rapid advancements in Large Pre-Trained Language Models (LLMs) have garnered attention for their remarkable few-shot learning capabilities, with notable attention directed toward ChatGPT, which has amassed over 100 million active users within just two months of its release. Despite the widespread adoption of ChatGPT, its effectiveness and potential in detecting software vulnerabilities remain largely unexplored. This paper aims to bridge this gap by investigating the efficacy of ChatGPT (built on GPT-3.5 and GPT-4) with diverse prompts. Experimental results demonstrate that, with the incorporation of our designed prompts, ChatGPT (GPT-3.5) exhibits a significant improvement of 25.4% in terms of Accuracy. With the utilization of prompts, ChatGPT (GPT-3.5) achieves competitive performance with the state-of-the-art vulnerability detection approach and ChatGPT (GPT-4) outperformed the state-of-the-art by 34.8% in terms of Accuracy.
Thu 18 AprDisplayed time zone: Lisbon change
11:00 - 12:30 | AI & Security 2Research Track / New Ideas and Emerging Results at Sophia de Mello Breyner Andresen Chair(s): Gabriele Bavota Software Institute @ Università della Svizzera Italiana | ||
11:00 15mTalk | Towards Causal Deep Learning for Vulnerability Detection Research Track Md Mahbubur Rahman Iowa State University, Ira Ceka Columbia University, Chengzhi Mao Columbia University, Saikat Chakraborty Microsoft Research, Baishakhi Ray AWS AI Labs, Wei Le Iowa State University | ||
11:15 15mTalk | MetaLog: Generalizable Cross-System Anomaly Detection from Logs with Meta-Learning Research Track Chenyangguang Zhang Tsinghua University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Guopeng Shen Linkedsee Technology (China) Limited, Pinyan Zhu Linkedsee Technology (China) Limited, Ying Li School of Software and Microelectronics, Peking University, Beijing, China | ||
11:30 15mTalk | Coca: Improving and Explaining Graph Neural Network-Based Vulnerability Detection Systems Research Track Sicong Cao Yangzhou University, Xiaobing Sun Yangzhou University, Xiaoxue Wu Yangzhou University, David Lo Singapore Management University, Lili Bo Yangzhou University, Bin Li Yangzhou University, Wei Liu Nanjing University Media Attached File Attached | ||
11:45 15mTalk | Improving Smart Contract Security with Contrastive Learning-based Vulnerability Detection Research Track Yizhou Chen Peking University, Zeyu Sun Institute of Software, Chinese Academy of Sciences, Zhihao Gong Peking University, Dan Hao Peking University | ||
12:00 15mTalk | On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities Research Track Zhen Li Huazhong University of Science and Technology, Ning Wang Huazhong University of Science and Technology, Deqing Zou Huazhong University of Science and Technology, Yating Li Huazhong University of Science and Technology, Ruqian Zhang Huazhong University of Science and Technology, Shouhuai Xu University of Colorado Colorado Springs, Chao Zhang Tsinghua University, Hai Jin Huazhong University of Science and Technology Pre-print | ||
12:15 7mTalk | Large Language Model for Vulnerability Detection: Emerging Results and Future Directions New Ideas and Emerging Results Xin Zhou Singapore Management University, Singapore, Ting Zhang Singapore Management University, David Lo Singapore Management University | ||
12:22 7mTalk | Re(gEx|DoS)Eval: Evaluating Generated Regular Expressions and their Proneness to DoS Attacks New Ideas and Emerging Results Mohammed Latif Siddiq University of Notre Dame, Jiahao Zhang , Lindsay Roney University of Notre Dame, Joanna C. S. Santos University of Notre Dame DOI Pre-print Media Attached | ||