SBridge: Identifying Source-to-Binary Function Similarity via Cross-Domain Control Block Matching

We present SBridge, a precise approach for identifying functions in binaries that are similar to the given source code functions. Identifying reused code in binaries is critical for security, particularly for detecting propagated vulnerabilities. Although binary-to-binary comparison is feasible, leveraging source code as the reference is more practical because source code is easier to collect and analyze directly without compilation. However, significant gaps between source and binary representations, including function inlining, create challenges in cross-domain function detection. Existing approaches primarily rely on string literals or structural similarities between entire functions, failing to capture detailed code behavior and generating many false alarms. SBridge addresses these limitations through a key innovation: control block-based function matching, which encapsulates essential functional features by segmenting functions into meaningful units such as conditionals and loops. Leveraging control blocks as a cross-domain representation, SBridge enables precise measurement of function similarity between source and binary code, effectively overcoming challenges posed by function inlining and stripped binaries. For evaluation, we collected 3,904 real-world C/C++ binaries from BinKit. In experiments identifying binary functions identical to input source functions, despite approximately 40% of binary functions being inlined, SBridge achieved 75.13% recall@1 and 80.98% recall@5, outperforming existing approaches, which achieved up to 43.31% recall@1 and 50.2% recall@5. Our further analysis confirmed that SBridge effectively identifies propagated vulnerabilities in binaries.

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2026-06-30
DOI
https://doi.org/10.1145/3797090
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

SBridge: Identifying Source-to-Binary Function Similarity via Cross-Domain Control Block Matching

Seunghoon Woo, 윤하진, Hee‐Dong Yang, Jeongwoo Lee
Proceedings of the ACM on software engineering.
Advanced Malware Detection Techniques
article

SBridge: Identifying Source-to-Binary Function Similarity via Cross-Domain Control Block Matching

Seunghoon Woo, 윤하진, Hee‐Dong Yang, Jeongwoo Lee
article en

Abstract

We present SBridge, a precise approach for identifying functions in binaries that are similar to the given source code functions. Identifying reused code in binaries is critical for security, particularly for detecting propagated vulnerabilities. Although binary-to-binary comparison is feasible, leveraging source code as the reference is more practical because source code is easier to collect and analyze directly without compilation. However, significant gaps between source and binary representations, including function inlining, create challenges in cross-domain function detection. Existing approaches primarily rely on string literals or structural similarities between entire functions, failing to capture detailed code behavior and generating many false alarms. SBridge addresses these limitations through a key innovation: control block-based function matching, which encapsulates essential functional features by segmenting functions into meaningful units such as conditionals and loops. Leveraging control blocks as a cross-domain representation, SBridge enables precise measurement of function similarity between source and binary code, effectively overcoming challenges posed by function inlining and stripped binaries. For evaluation, we collected 3,904 real-world C/C++ binaries from BinKit. In experiments identifying binary functions identical to input source functions, despite approximately 40% of binary functions being inlined, SBridge achieved 75.13% recall@1 and 80.98% recall@5, outperforming existing approaches, which achieved up to 43.31% recall@1 and 50.2% recall@5. Our further analysis confirmed that SBridge effectively identifies propagated vulnerabilities in binaries.

Proceedings of the ACM on software engineering.Vol. 3(FSE)
Korea University (KR)
National Research Foundation of Korea
Industry, innovation and infrastructure
Openalex Percentile: Top 6%
Advanced Malware Detection Techniques
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