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Field-Sensitive Over-Tainting Reduction in IFDS Taint Analysis via CFL-Reachability

Authors Yujiang Gui , Yonggang Tao , Jingling Xue

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Subject Classification

ACM Subject Classification
  • Theory of computation → Program analysis
Keywords
  • Taint Analysis
  • CFL-Reachability
  • Access Path
  • Field Sensitivity
  • Pointer Analysis

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Abstract

IFDS taint analysis is inherently context- and flow-sensitive, allowing precise encoding of field sensitivity in access-path generation. However, preserving this level of precision in practice is difficult, leading to over-tainting - marking more data facts as tainted than necessary. The root cause is the undecidability of solving two context-free language reachability (CFL-reachability) problems along the same dataflow path, which forces k-limiting as an over-approximation of field sensitivity. Consequently, spurious access paths are introduced, increasing analysis time, memory usage, and false positives, especially in large-scale applications.
To address this challenge, we present TnFix, a CFL-reachability-based technique for mitigating over-tainting in IFDS taint analysis. The key insight is that the field sequence of any candidate tainted access path can be checked by a deterministic finite automaton (DFA) that accepts feasible sequences of field accesses. TnFix builds these DFAs by first solving a lightweight field-sensitive CFL-reachability problem to construct a Field Points-to Graph (FPG) that integrates data flows from taint sources and library summaries, and then converting the FPG into per-object DFAs. During taint analysis, TnFix queries these DFAs to prune access paths whose field sequences are rejected, eliminating the spurious paths introduced by k-limiting and improving precision without sacrificing scalability.
In a comparative evaluation against FlowDroid on a set of 36 widely used Android apps for taint analysis, TnFix successfully analyzes 7 apps that FlowDroid cannot complete within a three-hour time budget. For the remaining 29 apps, it improves analysis speed by an average of 2.5× and reduces false positives by an average of 12.2%. TnFix thus establishes the first CFL-based optimization framework for reducing over-tainting in IFDS taint analysis, delivering substantial gains in both efficiency and precision for practical use.

Cite As Get BibTex

Yujiang Gui, Yonggang Tao, and Jingling Xue. Field-Sensitive Over-Tainting Reduction in IFDS Taint Analysis via CFL-Reachability. In 40th European Conference on Object-Oriented Programming (ECOOP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 372, pp. 10:1-10:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.ECOOP.2026.10

Author Details

Yujiang Gui
  • University of New South Wales, Sydney, Australia
Yonggang Tao
  • University of New South Wales, Sydney, Australia
Jingling Xue
  • University of New South Wales, Sydney, Australia

Funding

Australian Research Council Grants DP240103194.

Acknowledgements

{We thank the anonymous reviewers for their constructive comments.}

Supplementary Materials

References

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