Multimodal Fusion for Smart Contract Vulnerability Detection: An Experimental Dive

Abstract

Smart contract vulnerabilities pose serious risks in blockchain ecosystems, yet existing detection methods often rely on either source code or opcode analysis in isolation, missing complementary information across modalities. This paper presents a multimodal learning framework that combines semantic features extracted from source code using CodeBERT with Structure-Based Traversal (SBT) encoding and behavioral patterns derived from opcode sequences using a gMLP(gated Multi-Layer Perceptron) model applied to TF-IDF vectors. The framework systematically evaluates various fusion strategies, including concatenation, self-attention, cross-attention, and a hybrid attention mechanism, all within a unified architecture and dataset. Extensive experiments on the SmartBugs benchmark demonstrate two key findings: (1) the pairing of CodeBERT(SBT) and gMLP(opcode) achieves superior modality synergy (F1-score: 0.84), and (2) our hybrid attention fusion mechanism further improves performance to 0.87 F1, outperforming other fusion strategies by up to 3.6%. Compared to the best unimodal baselines, our approach yields a 12.8% F1 gain. To the best of our knowledge, this is the first study to provide a systematic benchmark of these fusion strategies under a unified framework for smart contract vulnerability detection. These results underscore the importance of informed modality selection and intelligent fusion design in building robust AI-driven vulnerability detection tools.