SemFinder: A Semantics-Based Approach to Enhance Vulnerability Analysis in Web Applications

Modern web applications are becoming increasingly complex. They include multiple dynamic runtime constructs that are difficult to analyze by static application security testing (SAST) tools. These tools often use a graph representation of the code for their analysis. However, built statically, such graphs may miss important data and control flows dependent on runtime information. In addition, the presence of difficult-to-analyze code patterns in modern web applications, referred to as testability tarpits, further reduces the accuracy of statically built graphs. As a result, current SAST tools have several false negatives because of 'hidden' paths, which are not present in the graphs. In this paper, we present SemFinder, an approach designed to automatically detect such hidden paths. SemFinder uses natural language semantics to hypothesize connections between different locations in the code based on the meaning and similarity of the variables in those locations and test those hypotheses dynamically. We evaluate SemFinder on 30 PHP applications and discover 215 new exploitable hidden paths with respect to existing SAST tools, leading to the submission of 31 new CVEs.