VulnFusion: Exploiting Multimodal Representations for Advanced Smart Contract Vulnerability Detection

In the fast-evolving domain of blockchain technology, smart contracts have become instrumental, facilitating billions of dollars in transactions daily. However, the increasing prevalence of smart contract vulnerabilities poses significant risks, potentially undermining trust in this innovative technology. To address these security challenges, our research introduces a novel approach for detecting and mitigating vulnerabilities through a comprehensive analysis of four principal modalities: Solidity source code, bytecode, opcode, and intermediate representations. This multimodal analysis employs a Transformer architecture, VulnFusion, which uniquely integrates these modalities to provide a deeper understanding of both the semantic and syntactical nuances of smart contracts. We propose an interleaving-based data fusion technique that outperforms traditional fusion methods such as concatenation and cross-attention. Experimental evaluations demonstrate that our interleavingbased fusion technique achieves an impressive average F1 score of 89% across source code, opcode, and intermediate representations, demonstrating the effectiveness of our approach.