Joza: Hybrid Taint Inference for Defeating Web Application SQL Injection Attacks

Abstract

Despite years of research on taint-tracking techniques to detect SQL injection attacks, taint tracking is rarely used in practice because it suffers from high performance overhead, intrusive instrumentation, and other deployment issues. Taint inference techniques address these shortcomings by obviating the need to track the flow of data during program execution by inferring markings based on either the program's input (negative taint inference), or the program itself (positive taint inference). We show that existing taint inference techniques are insecure by developing new attacks that exploit inherent weaknesses of the inferencing process. To address these exposed weaknesses, we developed Joza, a novel hybrid taint inference approach that exploits the complementary nature of negative and positive taint inference to mitigate their respective weaknesses. Our evaluation shows that Joza prevents real-world SQL injection attacks, exhibits no false positives, incurs low performance overhead (4%), and is easy to deploy.