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Investigating Metamorphic Fuzz Oracle Enhancement via Large Language Models

arXiv Security Archived Jun 15, 2026 ✓ Full text saved

arXiv:2606.14164v1 Announce Type: cross Abstract: Fuzz drivers are essential components of greybox fuzzing, as they encapsulate target interfaces, define test spaces, and largely determine fuzzing effectiveness. Existing fuzz drivers typically rely on crash-based oracles for security testing, overlooking library functionality and limiting bug detection capability. In this paper, we present the first study on metamorphic-based fuzz oracle enhancement (MFOE), which augments existing fuzz drivers w

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    Computer Science > Software Engineering [Submitted on 12 Jun 2026] Investigating Metamorphic Fuzz Oracle Enhancement via Large Language Models Ruixiang Qian, Ding Yang, Zengxu Chen, Yuxuan Gao, Chunrong Fang, Chao Zhang, Zhenyu Chen Fuzz drivers are essential components of greybox fuzzing, as they encapsulate target interfaces, define test spaces, and largely determine fuzzing effectiveness. Existing fuzz drivers typically rely on crash-based oracles for security testing, overlooking library functionality and limiting bug detection capability. In this paper, we present the first study on metamorphic-based fuzz oracle enhancement (MFOE), which augments existing fuzz drivers with metamorphic-based oracles derived from metamorphic relations (MRs). Since constructing and integrating such oracles requires substantial domain knowledge, automating MFOE is challenging. To address this challenge, we propose MetaFOE, an LLM-based framework that automatically generates and integrates metamorphic-based oracles. We evaluate MetaFOE on OSS-Fuzz drivers using three modern LLMs and five prompt strategies. MetaFOE generates 3,475 MRs, of which 77.3% are applicable, and implements 12,351 meta drivers, with 6,228 being valid. After three hours of fuzzing, the valid meta drivers improve edge coverage by an average of 18.7% and trigger 1,528 unique crashes. Our results demonstrate both the effectiveness of metamorphic-based oracle enhancement and the feasibility of using LLMs to automate MFOE, providing valuable insights for advancing greybox fuzzing. Comments: 28 pages Subjects: Software Engineering (cs.SE); Cryptography and Security (cs.CR) Cite as: arXiv:2606.14164 [cs.SE]   (or arXiv:2606.14164v1 [cs.SE] for this version)   https://doi.org/10.48550/arXiv.2606.14164 Focus to learn more Submission history From: Ruixiang Qian [view email] [v1] Fri, 12 Jun 2026 06:43:35 UTC (687 KB) Access Paper: HTML (experimental) view license Current browse context: cs.SE < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.CR 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
    Jun 15, 2026
    Archived
    Jun 15, 2026
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