CBGF: Callback Coverage Guided Fuzzing

Callback functions are widely used across programming languages, libraries, and operating systems. While offering flexible software design, these mechanisms introduce inherently complex execution flows that can serve as potential attack vectors for security vulnerabilities. Despite the continuous reporting and active exploitation of callback-related vulnerabilities in real-world environments, traditional coverage-guided greybox fuzzing approaches typically instrument the entire codebase uniformly without prioritizing callback functions, thereby insufficiently capturing callback-specific execution flows and limiting effectiveness in detecting these vulnerabilities. We propose Callback Coverage Guided Fuzzing (CBGF), a novel approach that leverages user-defined callback functions to strategically insert instrumentation code, enabling precise monitoring of callback execution flows. The proposed instrumentation approach facilitates comprehensive identification of callback execution paths while maintaining seamless compatibility with existing fuzzing frameworks. Our evaluation using CPython demonstrated the effectiveness of CBGF, discovering seven bugs, including five callback-related vulnerabilities.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3561135
Primary Topic
Artificial Intelligence in Games
Type
article
Field-Weighted Citation Impact
0.00
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article

CBGF: Callback Coverage Guided Fuzzing

Daesung Moon, Ho Young Hwang
IEEE Access
Artificial Intelligence in Games
article

CBGF: Callback Coverage Guided Fuzzing

Daesung Moon, Ho Young Hwang
article en

Abstract

Callback functions are widely used across programming languages, libraries, and operating systems. While offering flexible software design, these mechanisms introduce inherently complex execution flows that can serve as potential attack vectors for security vulnerabilities. Despite the continuous reporting and active exploitation of callback-related vulnerabilities in real-world environments, traditional coverage-guided greybox fuzzing approaches typically instrument the entire codebase uniformly without prioritizing callback functions, thereby insufficiently capturing callback-specific execution flows and limiting effectiveness in detecting these vulnerabilities. We propose Callback Coverage Guided Fuzzing (CBGF), a novel approach that leverages user-defined callback functions to strategically insert instrumentation code, enabling precise monitoring of callback execution flows. The proposed instrumentation approach facilitates comprehensive identification of callback execution paths while maintaining seamless compatibility with existing fuzzing frameworks. Our evaluation using CPython demonstrated the effectiveness of CBGF, discovering seven bugs, including five callback-related vulnerabilities.

IEEE AccessVol. 13
Electronics and Telecommunications Research Institute (KR), Korea University of Science and Technology (KR)
Openalex Percentile: Top 97%
Artificial Intelligence in Games
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CBGF: Callback Coverage Guided Fuzzing — Daesung Moon, Ho Young Hwang · IEEE Access (2025) | TGRS Research Map | TGRS