Dynamic taint analysis assigns taint labels to sensitive data and tracks the propagation of such tainted data during program execution. This program analysis technique has been implemented in various analysis platforms targeting specific programming languages or program representations and has been applied to diverse fields such as software security and debugging. While some of these platforms support customization of their taint analysis, such customization is typically limited to certain analysis properties or to predefined options. This limitation can require analysis developers to modify the analysis platform in order to adapt other analysis properties or to implement new taint analysis applications.

Dynamic taint analysis is a popular analysis technique which tracks the propagation of specific values while a program executes. To this end, a taint label is attached to these values and is dynamically propagated to any values derived from them. Frequent application of this analysis technique in many fields has led to the development of general-purpose analysis platforms with taint propagation capabilities. However, these platforms generally limit analysis developers to a specific implementation language, to specific propagation semantics or to specific taint label representations.

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.

Dynamic taint analysis is a popular program analysis technique in which sensitive data is marked as tainted and the propagation of tainted data is tracked in order to determine whether that data reaches critical program locations. This analysis technique has been successfully applied to software vulnerability detection, malware analysis, testing and debugging, and many other fields. However, existing approaches of dynamic taint analysis are either language-specific or they target native code. Neither is suitable for analyzing applications in which high-level dynamic languages such as JavaScript and low-level languages such as C interact.In these approaches, the language boundary forms an opaque barrier that prevents a sound analysis of data flow in the other language and can thus lead to the analysis being evaded.

Dynamic taint analysis is a program analysis technique in which data is marked and its propagation is tracked while the program is executing. It is applied to solve problems in many fields, especially in software security. Current taint analysis platforms are limited to a single programming language, and therefore cannot support programs which, as is common today, are implemented in multiple programming languages. Current implementations of dynamic taint analysis also incur a significant performance overhead.