Strengthening Supply Chain Security with Fine-grained Safe Patch Identification
Enhancing supply chain security is crucial, often involving the detection and porting of patches from upstream to downstream. However, current security patch analysis works yield relatively low recall rates (i.e., many security patches are missed). In this work, we offer a new solution to fix a substantial number of vulnerabilities in outdated dependency code. We develop SPatch to comprehensively detect fine-grained safe patches. It leverages fine-grained patch analysis and a new differential symbolic execution technique to analyze the functional impacts of code changes.
We evaluated SPatch on various software, including the Linux kernel and OpenSSL, and demonstrated that it outperformed existing methods in detecting safe patches, resulting in observable security benefits. In our case studies, we updated hundreds of functions in modern software using safe patches detected by SPatch without causing any regression issues. Our detected safe security patches have been merged into the latest version of downstream software like Redis.
Wed 17 AprDisplayed time zone: Lisbon change
16:00 - 17:30 | |||
16:00 15mTalk | RUNNER: Responsible UNfair NEuron Repair for Enhancing Deep Neural Network Fairness Research Track Li Tianlin Nanyang Technological University, Yue Cao Nanyang Technological University, Jian Zhang Nanyang Technological University, Shiqian Zhao Nanyang Technological University, Yihao Huang East China Normal University, Aishan Liu Beihang University; Institute of Dataspace, Qing Guo IHPC and CFAR at A*STAR, Singapore, Yang Liu Nanyang Technological University | ||
16:15 15mTalk | ITER: Iterative Neural Repair for Multi-Location Patches Research Track | ||
16:30 15mTalk | Out of Context: How important is Local Context in Neural Program Repair? Research Track | ||
16:45 15mTalk | Out of Sight, Out of Mind: Better Automatic Vulnerability Repair by Broadening Input Ranges and Sources Research Track Xin Zhou Singapore Management University, Singapore, Kisub Kim Singapore Management University, Singapore, Bowen Xu North Carolina State University, DongGyun Han Royal Holloway, University of London, David Lo Singapore Management University | ||
17:00 15mTalk | Strengthening Supply Chain Security with Fine-grained Safe Patch Identification Research Track Luo Changhua The Chinese University of Hong Kong, Wei Meng Chinese University of Hong Kong, Shuai Wang The Hong Kong University of Science and Technology |