Low-overhead multi-language dynamic taint analysis on managed runtimes through speculative optimization
Jacob Kreindl, Daniele Bonetta, Lukas Stadler, David Leopoldseder, Hanspeter Mössenböck · 2021
Dynamic taint analysis (DTA) is a popular program analysis technique with applications to diverse fields such as software vulnerability detection and reverse engineering. It consists of marking sensitive data as tainted and tracking its propagation at runtime. While DTA has been implemented on top of many different analysis platforms, these implementations generally incur significant slowdown from taint propagation. Since a purely dynamic analysis cannot predict which instructions will operate on tainted values at runtime, programs have to be fully instrumented for taint propagation even when they never actually observe tainted values. We propose leveraging speculative optimizations to reduce slowdown on the peak performance of programs instrumented for DTA on a managed runtime capable of dynamic compilation.