Optimized DeBERTa for Efficient Smart Contract Vulnerability Detection

Abstract

Smart contracts are integral to blockchain applications; yet, their immutability creates security vulnerabilities, such as reentrancy and overflow, which can be critically damaging. While detection tools exist, many rely on symbolic execution, graph preprocessing, or binary classification, limiting their efficiency and practicality. This study presents an optimized DeBERTa V3-based transformer model for multilabel detection of smart contract vulnerabilities. The proposed approach operates directly on tokenized Solidity code, leveraging disentangled attention and position embeddings to model semantic patterns. Evaluation on three public datasets achieves up to 100% F1-scores on key vulnerabilities and maintains an average inference latency below 58 ms per smart contract. These results demonstrate the feasibility of integrating high-accuracy, low-latency vulnerability detection into real-time auditing tools, thereby enhancing contract security before deployment.