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arXiv 2403.14280cs.CR
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面向区块链安全的大语言模型:一项系统性文献综述

Large Language Models for Blockchain Security: A Systematic Literature Review

Zheyuan He, Zihao Li, Sen Yang, He Ye, Ao Qiao, Xiaosong Zhang, Xiapu Luo, Ting Chen

AI总结:

该系统性文献综述聚焦大语言模型在区块链安全领域的应用研究,梳理其作用机制与应用场景,评估相关挑战与局限,为相关研究者、从业者和政策制定者提供参考。

摘要:

大语言模型(LLMs)已成为网络安全各领域的强大工具。值得注意的是,近期研究越来越多地探索将LLMs应用于区块链安全(BS)场景。然而,目前对于LLMs在区块链安全领域的完整应用范围、影响及潜在限制,仍缺乏全面的认知。为填补这一空白,我们针对将LLMs应用于区块链安全(LLM4BS)的研究开展了文献综述。\n本研究旨在全面分析和理解现有研究,阐明LLMs如何助力提升区块链系统的安全性。通过对现有文献的全面梳理,我们深入探究了LLMs与区块链安全各方面的融合。我们探讨了LLMs强化区块链安全的作用机制,包括其在智能合约审计、交易异常检测、漏洞修复、智能合约程序分析中的应用,以及作为加密货币社区参与者的场景。此外,我们还评估了利用LLMs提升区块链安全所面临的挑战与局限,考量了可扩展性、隐私顾虑和伦理问题等因素。本次全面综述揭示了LLM4BS相关任务的机遇与潜在风险,可为研究人员、从业者和政策制定者提供有价值的参考。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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