Pre-Model Representation Failures in GNN-Based Smart Contract Vulnerability Detection

[2608.15184] Pre-Model Representation Failures in GNN-Based Smart Contract Vulnerability Detection Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Cryptography and Security arXiv:2608.15184 (cs) [Submitted on 15 Aug 2026] Title: Pre-Model Representation Failures in GNN-Based Smart Contract Vulnerability Detection Authors: Birindwa Prisca Hondi , Chinoso Philip Nwishienyi , Charity Wanja Mwaura , Alia Teto , Jema David Ndibwile View a PDF of the paper titled Pre-Model Representation Failures in GNN-Based Smart Contract Vulnerability Detection, by Birindwa Prisca Hondi and 4 other authors View PDF HTML (experimental) Abstract: This paper is a failure analysis of the representation layer underlying GNN-based smart contract vulnerability detectors. These systems convert source code into graphs before any learning takes place; if the graph fails to capture the code's semantics, no model improvement can compensate. We investigate GNNSCVulDetector and identify four failures. First, structurally different contracts produce byte-for-byte identical graphs, constituting a concrete evasion attack. Second, graph construction is governed by a hardcoded 47-entry variable whitelist (including one duplicate entry), which constrains what the extractor can recognise. As a consequence, identical vulnerabilities with different variable names produce inconsistent graphs, graph quality degrades as naming diverges from the whitelist, and when no entry matches the pipeline produces structural output not grounded in source variables. Third, the C node (the graph element representing the external caller that triggers a reentrancy attack) is absent from even the most canonical vulnerable contract in the literature. Fourth, a controlled experiment confirms this as a direct misclassification: a fully exploitable contract is labelled safe because the C -> W edge is never constructed. All four failures are demonstrated experimentally. Current accuracy figures in the literature are measured under conditions that do not expose these failures. We demonstrate one confirmed case of misclassification caused directly by a representation-layer failure; the prevalence of such failures in real-world contract populations remains an open empirical question. Comments: 12 pages, 5 Tables, 4 Listings Subjects: Cryptography and Security (cs.CR) ; Computational Complexity (cs.CC) Cite as: arXiv:2608.15184 [cs.CR] (or arXiv:2608.15184v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.15184 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jema David Ndibwile Prof [ view email ] [v1] Sat, 15 Aug 2026 11:54:45 UTC (24 KB) Full-text links: Access Paper: View a PDF of the paper titled Pre-Model Representation Failures in GNN-Based Smart Contract Vulnerability Detection, by Birindwa Prisca Hondi and 4 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 cs.CC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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