Security auditing of industry-scale software systems mandates automation. Static taint analysis enables deep and exhaustive tracking of suspicious data flows for detection of potential leakage and integrity violations, such as cross-site scripting (XSS), SQL injection (SQLi) and log forging. Research in this area has taken two directions: program slicing and type systems. Both of these approaches suffer from a high rate of false findings, which limits the usability of analysis tools based on these techniques. Attempts to reduce the number of false findings have resulted in analyses that are either (i) unsound, suffering from the dual problem of false negatives, or (ii) too expensive due to their high precision, thereby failing to scale to real-world applications.
In this paper, we investigate a novel approach for enabling precise yet scalable static taint analysis. The key observation informing our approach is that taint analysis is a demand-driven problem, which enables lazy computation of vulnerable information flows, instead of eagerly computing a complete data-flow solution, which is the reason for the traditional dichotomy between scalability and precision. We have implemented our approach in
, an analysis tool that computes data-flow propagations on demand, in an efficient and accurate manner, and additionally features incremental analysis capabilities.
on a suite of 16 production-level benchmarks shows
to achieve high accuracy and compare favorably to a state-of-the-art tool that trades soundness for precision.