Indirect Function Call Target Identification within Software

By employing explicit data dependency analysis and a graph neural network to identify similar functions, the method enhances the precision of indirect function call target identification, addressing the inaccuracies in current methods and improving program security analysis.

JP7893575B2Active Publication Date: 2026-07-22INTERNATIONAL BUSINESS MACHINE CORPORATION
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Patent Information

Authority / Receiving Office
JP · JP
Patent Type
Patents
Current Assignee / Owner
INTERNATIONAL BUSINESS MACHINE CORPORATION
Filing Date
2022-11-03
Publication Date
2026-07-22

AI Technical Summary

Technical Problem

Current methods for identifying indirect function call targets in software are not precise enough for large programs, leading to high false-positive and false-negative rates, which hinders effective program analysis such as fuzz testing and vulnerability detection.

Method used

A method using explicit data dependency analysis and a graph neural network to generate feature embeddings, identifying similar functions, and expanding the ground truth dataset of indirect function call targets to improve accuracy.

Benefits of technology

Reduces false-positive and false-negative rates, enabling precise identification of indirect function call targets for improved program security analysis.

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Patent Text Reader

Abstract

Indirect function call target identification within the software is provided. A set of explicit data flows that pass function addresses between software modules of the program is determined using explicit data dependency analysis. A set of indirect function call targets is generated from results of the explicit data dependency analysis and dynamic execution analysis of the program. The set of indirect function call targets is expanded by identifying similar target functions based on feature embeddings generated by the graph neural network.
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