PyFlow: An Inter-procedural Static Analysis Framework for Python

Abstract

Static program analysis infers program properties automatically. Yet precise interprocedural analysis remains challenging, and dynamically typed languages amplify the difficulty. Python is particularly problematic: dynamic dispatch, first-class functions, metaprogramming, pervasive exceptions, and an object model based on descriptors and attribute-driven lookup collectively impede precise reasoning. We present PyFlow, a generic IFDS-based static-analysis framework for Python. PyFlow provides a multi-stage intermediate-representation pipeline and a generic IFDS solver parameterized by abstract domains. Analysis developers implement only the dataflow semantics; the framework constructs the supergraph, performs fixed-point iteration, and caches summaries. We implement a taint analysis in \pyflow and evaluate it against eight Python SAST tools (DevSkim, Dlint, Bandit, Bearer, CodeQL, Pysa, Semgrep, and Snyk) on the synthetic and real-world benchmarks from a recent ICSE~'26 study. On the synthetic benchmark, PyFlow achieves the best aggregate recall and F1 score among all nine tools. On the real-world benchmark, it attains the highest recall and F1 score while maintaining precision competitive with taint-based engines. We conclude with lessons learned from building IFDS analyses for Python.