Dynamic Taint Tracking for Modern Java Virtual Machines

Dynamic taint tracking is a program analysis that traces the flow of information through a program. In the Java virtual machine (JVM), there are two prominent approaches for dynamic taint tracking: "shadowing" and "mirroring". Shadowing is able to precisely track information flows, but is also prone to disrupting the semantics of the program under analysis. Mirroring is better able to preserve program semantics, but often inaccurate. The limitations of these approaches are further exacerbated by features introduced in the latest Java versions. In this paper, we propose Galette, an approach for dynamic taint tracking in the JVM that combines aspects of both shadowing and mirroring to provide precise, robust taint tag propagation in modern JVMs. On a benchmark suite of 3,451 synthetic Java programs, we found that Galette was able to propagate taint tags with perfect accuracy while preserving program semantics on all four active long-term support versions of Java. We also found that Galette's runtime and memory overheads were competitive with that of two state-of-the-art dynamic taint tracking systems on a benchmark suite of twenty real-world Java programs.

CCS Concepts: • Software and its engineering → Dynamic analysis; • Security and privacy → Information flow control.