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Multi-target Coverage-based Greybox Fuzzing

arXiv Security Archived Mar 27, 2026 ✓ Full text saved

arXiv:2603.25354v1 Announce Type: new Abstract: In recent years, fuzzing has been widely applied not only to application software but also to system software, including the Linux kernel and firmware, and has become a powerful technique for vulnerability discovery. Among these approaches, Coverage-based grey-box fuzzing, which utilizes runtime code coverage information, has become the dominant methodology. Conventional fuzzing techniques primarily target a single software component and have paid

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    Computer Science > Cryptography and Security [Submitted on 26 Mar 2026] Multi-target Coverage-based Greybox Fuzzing Masami Ichikawa In recent years, fuzzing has been widely applied not only to application software but also to system software, including the Linux kernel and firmware, and has become a powerful technique for vulnerability discovery. Among these approaches, Coverage-based grey-box fuzzing, which utilizes runtime code coverage information, has become the dominant methodology. Conventional fuzzing techniques primarily target a single software component and have paid little attention to cooperative execution with other software. However, modern system software architectures commonly consist of firmware and an operating system that operate cooperatively through well-defined interfaces, such as OpenSBI in the RISC-V architecture and OP-TEE in the ARM architecture. In this study, we investigate fuzzing techniques for architectures in which an operating system and firmware operate cooperatively. In particular, we propose a fuzzing method that enables deeper exploration of the system by leveraging the code coverage of each cooperating software component as feedback, compared to conventional Single-target fuzzing. To observe the execution of the operating system and firmware in a unified manner, our method adopts QEMU as a virtualization environment and executes fuzzing by booting the system within a virtual machine. This enables the measurement of code coverage across software boundaries. Furthermore, we implemented the proposed method as a Multi-target Coverage-based Greybox Fuzzer called MTCFuzz and evaluated its effectiveness. Comments: Master's thesis Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.25354 [cs.CR]   (or arXiv:2603.25354v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.25354 Focus to learn more Submission history From: Masami Ichikawa [view email] [v1] Thu, 26 Mar 2026 11:59:21 UTC (590 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Mar 27, 2026
    Archived
    Mar 27, 2026
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