FIRE: Combining Multi-Stage Filtering with Taint Analysis for Scalable Recurring Vulnerability Detection
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Watch a 12-minute conference presentation from USENIX Security '24 exploring FIRE, an innovative method for detecting recurring software vulnerabilities at scale. Learn how researchers from Huazhong University of Science and Technology and Nanyang Technological University developed a system that combines multi-stage filtering with differential taint path analysis to efficiently identify copied vulnerabilities in open-source software. Discover how FIRE achieved 90% precision in detecting 298 recurring vulnerabilities across ten open-source projects, outperforming existing tools like VUDDY and MOVERY by detecting up to 47% more vulnerabilities while requiring significantly less processing time. Understand the growing challenge of recurring vulnerabilities in open-source software and how FIRE's approach enables vulnerability scanning at an ultra-large scale.