计算机工程2026，Vol.52Issue(1)：1-21,21.DOI:10.19678/j.issn.1000-3428.0253233
大语言模型赋能区块链服务安全研究综述:现状、挑战与机遇(特邀)
Large Language Models Empowering Blockchain Service Security:A Comprehensive Survey of Status,Challenges,and Opportunities(Invited)
摘要
Abstract
Blockchain has gradually evolved into a critical infrastructure that supports the digital economy.However,its inherent characteristics such as anonymity,cross-chain interoperability,and multi-party participation have led to frequent security incidents,including fraud,money laundering,and cyberattacks,which pose serious threats to the stability and compliance of the blockchain ecosystem.Although existing analytical tools and methods have made notable progress in blockchain service security,they suffer from limited generalizability,insufficient reasoning capabilities,and poor adaptability to the evolution of complex business logic.The rapid development of generative Large Language Model(LLM)has significantly reshaped the service computing paradigm.With their strong capabilities in natural language understanding,knowledge reasoning,and multimodal integration,LLM provide new perspectives and technical pathways for research on blockchain service security.This paper systematically reviews the progress of LLM applications in three major areas:pre-event smart contract auditing,in-event anomaly detection,and post-event cross-chain behavior correlation.Further,it summarizes their advantages and limitations and highlights representative practices of LLM-enabled blockchain security.Finally,open research challenges and future directions are discussed,aiming to provide insights for building a trustworthy,interpretable,and efficient framework for blockchain service computing and governance.关键词
区块链/大语言模型/服务安全/智能合约审计/异常行为检测/多链行为关联Key words
blockchain/Large Language Model(LLM)/service security/smart contract auditing/abnormal behavior detection/multi-chain behavior association分类
信息技术与安全科学引用本文复制引用
林丹,卢顺峰,刘姿妍,张博昭,何龙,蒋子规,吴嘉婧,郑子彬..大语言模型赋能区块链服务安全研究综述:现状、挑战与机遇(特邀)[J].计算机工程,2026,52(1):1-21,21.基金项目
国家重点研发计划(2023YFB2704700) （2023YFB2704700）
国家自然科学基金(62502548,62372485,623B2102,62472457) （62502548,62372485,623B2102,62472457）
广东省自然科学基金(2023A1515011336). （2023A1515011336）
