On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities
Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.
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 | ||