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)

doi:10.19678/j.issn.1000-3428.0253233

计算机工程 ›› 2026, Vol. 52 ›› Issue (1): 1-21. doi: 10.19678/j.issn.1000-3428.0253233

• 大模型时代的服务计算 • 上一篇下一篇

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)

林丹¹^,², 卢顺峰¹, 刘姿妍¹, 张博昭¹, 何龙¹, 蒋子规¹, 吴嘉婧¹^,²^,*(), 郑子彬¹^,²

1. 中山大学软件工程学院, 广东珠海 519082
2. 广东省区块链工程技术研究中心, 广东珠海 519082

收稿日期:2025-11-03 修回日期:2025-12-09 出版日期:2026-01-15 发布日期:2026-01-15
通讯作者: 吴嘉婧
作者简介:
林丹(CCF专业会员), 女, 助理研究员, 主研方向为区块链金融监管技术
卢顺峰, 硕士研究生
刘姿妍, 本科生
张博昭, 本科生
何龙, 博士研究生
蒋子规(CCF高级会员), 副教授
吴嘉婧(通信作者), 教授
郑子彬, 教授
基金资助:
国家重点研发计划(2023YFB2704700); 国家自然科学基金(62502548); 国家自然科学基金(62372485); 国家自然科学基金(623B2102); 国家自然科学基金(62472457); 广东省自然科学基金(2023A1515011336)

Large Language Models Empowering Blockchain Service Security: A Comprehensive Survey of Status, Challenges, and Opportunities (Invited)

LIN Dan¹^,², LU Shunfeng¹, LIU Ziyan¹, ZHANG Bozhao¹, HE Long¹, JIANG Zigui¹, WU Jiajing¹^,²^,*(), ZHENG Zibin¹^,²

1. School of Software Engineering, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
2. Guangdong Blockchain Engineering Technology Research Center, Zhuhai 519082, Guangdong, China

Received:2025-11-03 Revised:2025-12-09 Online:2026-01-15 Published:2026-01-15
Contact: WU Jiajing

摘要/Abstract

摘要：

区块链已逐渐发展成支撑数字经济的重要基础设施, 但其匿名性、跨链互操作性、多方参与等特征, 导致诈骗、洗钱与攻击等安全事件频发, 严重威胁生态系统的稳定与合规。尽管现有分析工具与方法在区块链服务安全领域取得了一定进展, 但仍普遍存在泛化能力不足、推理能力有限、难以适应复杂业务逻辑演化等问题。与此同时, 生成式大语言模型(LLM)的快速发展正在深刻重塑服务计算范式, 其在自然语言理解、知识推理与多模态融合等方面的优势, 为区块链服务安全研究提供了新的思路与技术路径。系统梳理LLM在事前智能合约审计、事中异常行为检测、事后多链行为关联任务中的应用进展, 归纳其优势与局限, 总结LLM赋能区块链服务安全的典型实践。最后, 展望LLM赋能区块链服务安全面临的开放科学问题与未来研究方向, 为构建可信、可解释、高效的区块链服务计算与治理体系提供参考。

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

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.

Key words: blockchain, Large Language Model (LLM), service security, smart contract auditing, abnormal behavior detection, multi-chain behavior association

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.

LIN Dan, LU Shunfeng, LIU Ziyan, ZHANG Bozhao, HE Long, JIANG Zigui, WU Jiajing, ZHENG Zibin. Large Language Models Empowering Blockchain Service Security: A Comprehensive Survey of Status, Challenges, and Opportunities (Invited)[J]. Computer Engineering, 2026, 52(1): 1-21.

https://www.ecice06.com/CN/Y2026/V52/I1/1

图/表 15

图1 区块链服务安全存在的现实问题

Fig.1 The practical problems of blockchain service security

图2 知识驱动和数据驱动的区块链安全防护模式

Fig.2 Knowledge-driven and data-driven blockchain security protection models

图3 LLM为区块链服务安全提供新的解决方案

Fig.3 LLM provides new solutions for blockchain service security

图4 LLM赋能的区块链服务安全全流程防护架构

Fig.4 LLM-empowered full-process protection architecture for blockchain service security

图5 区块链服务生态架构与本文研究的定位

Fig.5 Blockchain service ecological architecture and the positioning of this research

图6 基于LLM的事前智能合约审计的三阶段演进过程

Fig.6 The three-stage evolutionary process of LLM-based pre-deployment smart contract auditing

图7 从模型优化到智能决策的演化脉络

Fig.7 The evolutionary trajectory from model optimization to intelligent decision-making

图8 技术演进时间线图谱

Fig.8 Graph of the technology evolution timeline

图9 比特币和Solana的数据结构及交易模式

Fig.9 The data structure and transaction mode of Bitcoin and Solana

图10 大模型时代下的未来发展新范式

Fig.10 The new paradigm of future development in the era of large models

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