Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points

ArXi:2412.11194v2 Announce Type: replace-cross Security vulnerabilities in software can have severe consequences; however, manual vulnerability detection is costly and does not scale, especially as agentic coding frameworks increase the rate of code production. Over the last decade, a large body of research has applied machine learning machine learning to automate vulnerability detection (ML4AVD), yet self-reported performance on the most popular datasets shows no clear upward trend.