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

2026-08-15

AI Summary

This study addresses semantic loss and misclassification in GNN-based smart contract vulnerability detectors caused by failures in graph representation layers. It presents the first systematic analysis of pre-model representation layer failure mechanisms. Through code-to-graph conversion and controlled experiments, four categories of graph construction defects are identified: structural isomorphism issues, variable whitelisting constraints, critical node omission, and erroneous edge construction. The research confirms that representation layer failures constitute the root cause of security misjudgments and reveals coverage gaps in existing evaluation frameworks regarding these defects. These findings provide a crucial theoretical foundation and actionable directions for enhancing detector robustness against representation-level vulnerabilities in smart contract analysis.

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.

Problem

• Smart Contract Vulnerability Detection
• Graph Neural Networks
• Pre-Model Representation Failures
• Failure Analysis
• Misclassification

Innovation

• Representation Failure Analysis
• GNN Smart Contract Vulnerability Detection
• Graph Construction Flaws
• Pre-model Representation
• Evasion Attack