Enhancing Static Analyzers with Graph-Based Vulnerability Discovery

Amazon security engineer Tom Ganz discusses how graph-based techniques, particularly through the use of Memgraph, can significantly enhance static analyzers for vulnerability discovery, by addressing their inherent limitations such as high false positives and lack of contextual awareness. Traditional static analyzers often misinterpret complex programming constructs due to a surface-level understanding of code, and theoretical constraints like Rice’s theorem and the halting problem further limit their capabilities. By integrating machine learning with graph-based analysis, using tools like graph neural networks, it's possible to capture nuanced code dependencies, providing a more context-aware and accurate vulnerability detection. Ganz introduces Pavudi, a patch-focused methodology that enhances detection precision and efficiency by targeting recent code changes rather than entire codebases, resulting in a 50% increase in accuracy and reduced false positives. Memgraph was chosen for its superior speed and scalability in processing large-scale graph data, crucial for complex graph traversals in vulnerability detection, and its user-friendly interface facilitated rapid experimentation. Despite some challenges in achieving production-level performance, the research sets a foundation for more effective machine learning-based static analysis by emphasizing patches and better contextual learning.