SAEFUZZ: Smart Contract Vulnerability Detection through Statically Guided Evolutionary Fuzzing

[2608.15151] SAEFUZZ: Smart Contract Vulnerability Detection through Statically Guided Evolutionary Fuzzing Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Cryptography and Security arXiv:2608.15151 (cs) [Submitted on 15 Aug 2026] Title: SAEFUZZ: Smart Contract Vulnerability Detection through Statically Guided Evolutionary Fuzzing Authors: Shiting Yu , Rundong Wei , Xiaoqi Li View a PDF of the paper titled SAEFUZZ: Smart Contract Vulnerability Detection through Statically Guided Evolutionary Fuzzing, by Shiting Yu and 2 other authors View PDF HTML (experimental) Abstract: The effectiveness of smart contract fuzzing depends strongly on whether generated transactions reach deep, state-dependent execution paths. Existing fuzzers often generate highly random call sequences, wasting executions on semantically invalid or low-value states and leaving vulnerabilities that require specific invocation orders unexplored. We present a lightweight method for generating fuzz test cases under bytecode-level static guidance. We construct an Ethereum virtual machine control-flow graph, extract paths containing vulnerability-relevant instructions, recover function selectors, and order externally callable functions according to storage read-write dependencies. A coverage-guided evolutionary strategy then generates, evaluates, recombines, and mutates executable seeds. Five dedicated runtime oracles target reentrancy, integer overflow or underflow, block-state dependence, unsafe delegate calls, and frozen Ether. The evaluation uses deployed Ethereum contracts, including labelled vulnerable contracts. SAEFUZZ detects most labelled vulnerable contracts, yielding 98.50% accuracy, 90.00% precision, and 81.82% recall. It also achieves 84.07% mean instruction coverage, with valid test cases accounting for 93.48% of generated cases. Ablation results indicate that static guidance, directed seed generation, and vulnerability-specific oracles each contribute to the final performance. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.15151 [cs.CR] (or arXiv:2608.15151v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.15151 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rundong Wei [ view email ] [v1] Sat, 15 Aug 2026 10:06:12 UTC (63 KB) Full-text links: Access Paper: View a PDF of the paper titled SAEFUZZ: Smart Contract Vulnerability Detection through Statically Guided Evolutionary Fuzzing, by Shiting Yu and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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