Large Language Models (LLMs) have emerged as powerful tools across various domains within cyber security. Notably, recent studies are increasingly exploring LLMs applied to the context of blockchain security (BS). However, there remains a gap in a comprehensive understanding regarding the full scope of applications, impacts, and potential constraints of LLMs on blockchain security. To fill this gap, we undertake a literature review focusing on the studies that apply LLMs in blockchain security (LLM4BS). Our study aims to comprehensively analyze and understand existing research, and elucidate how LLMs contribute to enhancing the security of blockchain systems. Through a thorough examination of existing literature, 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, transaction anomaly detection, vulnerability repair, program analysis of smart contracts, and serving as participants in the cryptocurrency community. Furthermore, we assess the challenges and limitations associated with leveraging LLMs for enhancing blockchain security, considering factors such as scalability, privacy concerns, and ethical concerns. Our thorough review sheds light on the opportunities and potential risks of tasks on LLM4BS, providing valuable insights for researchers, practitioners, and policymakers alike.

翻译：大语言模型（LLMs）已成为网络安全领域多个子领域中的强大工具。值得注意的是，近期研究日益关注将LLMs应用于区块链安全（BS）场景。然而，关于LLMs在区块链安全领域的全部应用范围、影响及潜在约束仍缺乏全面认知。为填补这一空白，我们开展了一项聚焦于LLMs在区块链安全（LLM4BS）中应用研究的文献综述。本研究旨在系统分析与理解现有研究，阐明LLMs如何提升区块链系统的安全性。通过深入梳理现有文献，我们探究了LLMs与区块链安全各层面的融合路径，阐释了LLMs强化区块链安全的作用机制，包括在智能合约审计、交易异常检测、漏洞修复、智能合约程序分析以及作为加密货币社区参与者等领域的应用。此外，我们从可扩展性、隐私关切及伦理问题等维度，评估了利用LLMs提升区块链安全所面临的挑战与局限。本综述全面揭示了LLM4BS任务的机遇与潜在风险，为研究人员、从业者及政策制定者提供了重要洞见。