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：大型语言模型（LLMs）已成为涵盖区块链安全（BS）的多个领域中的强大工具。近期有多项研究探索LLMs在BS中的应用。然而，关于LLMs对区块链安全的全方位应用、影响及潜在局限性，我们仍存在认知差距。为填补这一空白，我们针对LLM4BS展开了文献综述。作为首个关于LLMs在区块链安全领域应用的综述，本研究旨在全面分析现有研究，阐明LLMs如何助力增强区块链系统的安全性。通过对学术成果的深入检视，我们探讨了LLMs与区块链安全各层面的整合方式，并解析了LLMs强化区块链安全的机制，包括其在智能合约审计、身份验证、异常检测、漏洞修复等方面的应用。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限制，涉及可扩展性、隐私问题及对抗性攻击等因素。本综述揭示了这一交叉领域的机遇与潜在风险，为研究人员、从业人员及政策制定者提供了宝贵见解。