APPLICATION OF LARGE LANGUAGE MODELS IN SMART CONTRACT VULNERABILITY DETECTION

Abstract

With the rapid iteration of blockchain technology, smart contracts, as core components of decentralized applications, directly impact the stability of on-chain assets and ecosystems through their security. Traditional vulnerability detection methods primarily rely on expert rules and static analysis, facing bottlenecks such as high false positive rates and poor adaptability to complex logical vulnerabilities. In recent years, Large Language Models (LLMs), with their exceptional code understanding and reasoning capabilities, have provided new technical pathways for smart contract security auditing. This paper focuses on LLM-driven smart contract vulnerability detection technologies, systematically reviewing mainstream application paradigms from prompt engineering to model fine-tuning. The paper first reviews the current state of smart contract security and the limitations of traditional methods; subsequently, it provides in-depth analysis of the architectural design and core mechanisms of representative frameworks such as GPTLens and SmartVD, evaluating their performance in detection accuracy and recall rate; finally, addressing current challenges including data scarcity, model hallucinations, and computational overhead, it proposes future evolution directions such as multimodal fusion and human-in-the-loop auditing, providing reference for research and practice in related fields.