Large Language Models (LLMs) have emerged as powerful tools in various domains involving blockchain security (BS). Several recent studies are exploring LLMs applied to BS. However, there remains a gap in our understanding regarding the full scope of applications, impacts, and potential constraints of LLMs on blockchain security. To fill this gap, we conduct a literature review on LLM4BS. As the first review of LLM's application on blockchain security, our study aims to comprehensively analyze existing research and elucidate how LLMs contribute to enhancing the security of blockchain systems. Through a thorough examination of scholarly works, we delve into the integration of LLMs into various aspects of blockchain security. We explore the mechanisms through which LLMs can bolster blockchain security, including their applications in smart contract auditing, identity verification, anomaly detection, vulnerable repair, and so on. Furthermore, we critically assess the challenges and limitations associated with leveraging LLMs for blockchain security, considering factors such as scalability, privacy concerns, and adversarial attacks. Our review sheds light on the opportunities and potential risks inherent in this convergence, providing valuable insights for researchers, practitioners, and policymakers alike.

翻译：大语言模型（LLMs）已成为区块链安全（BS）相关各领域中的重要工具。近期有多项研究探索了LLMs在区块链安全中的应用。然而，关于LLMs在区块链安全中的完整应用范围、影响及潜在约束，我们仍存在认知空白。为填补这一空白，我们对LLM4BS进行了文献综述。作为首篇关于大语言模型在区块链安全中应用的综述，本研究旨在全面分析现有研究，阐明LLMs如何为增强区块链系统安全性做出贡献。通过对学术文献的深入审查，我们探究了LLMs与区块链安全各环节的集成方式。我们剖析了LLMs强化区块链安全的内在机制，包括其在智能合约审计、身份验证、异常检测、漏洞修复等领域的应用。此外，我们批判性地评估了利用LLMs保障区块链安全所面临的挑战与局限性，涉及可扩展性、隐私顾虑及对抗性攻击等因素。本综述揭示了二者融合过程中的机遇与潜在风险，为研究人员、从业者及政策制定者提供了重要参考。