FlowStAP

FlowStAP is a software framework and research prototype designed to perform flow-sensitive taint analysis on programs. The goal is to detect how sensitive data can flow from sources such as user input, device identifiers, or network data to sinks such as logs, network transmissions, or file writes within a software system. By accounting for the influence of program control flow, FlowStAP aims to distinguish different execution paths and to propagate taint across function calls, across modules, and through common data structures.

Its typical approach is interprocedural analysis that constructs a control-flow graph and a taint lattice, applying propagation rules to model how tainted values influence subsequent computations. It often employs context- and path-sensitivity to reduce false positives and to handle features such as aliasing, object fields, and library or native calls. Implementations may operate on source code or intermediate representations such as bytecode, and they produce reports listing tainted sources, potential sinks, and the path conditions that connect them.

FlowStAP has been described in academic literature and has informed the development of several taint-analysis tools for mobile and web applications. It is used by researchers and security engineers to audit privacy risks and to evaluate the effectiveness of taint-analysis techniques. Real-world deployments typically require integration with development workflows and may include sampling or modular analysis to scale to large codebases.

Limitations include the risk of false positives and negatives, particularly in the presence of dynamic features, reflection, obfuscation, or external code. Performance and scalability remain ongoing concerns for flow-sensitive analyses, and results typically require manual validation by developers or security analysts.