This paper presents a systematic literature review on the application of Large Language Models (LLMs) in blockchain security (LLM4BS). The study aims to analyze existing research and elucidate how LLMs contribute to enhancing blockchain security. It explores the integration of LLMs into various aspects of blockchain security, including smart contract auditing, transaction anomaly detection, vulnerability repair, program analysis of smart contracts, and serving as participants in the cryptocurrency community. The review also assesses the challenges and limitations associated with leveraging LLMs for blockchain security, considering factors such as scalability, privacy concerns, and ethical concerns. The paper highlights the opportunities and potential risks of LLM4BS tasks, providing valuable insights for researchers, practitioners, and policymakers. The review outlines the current landscape of LLM applications across diverse domains and the security threats associated with blockchain technology. It elaborates on the incorporation and progression of LLM4BS tasks, including smart contract auditing, block transaction detection, contract dynamic analysis, smart contract development, and cryptocurrency community contributors. The study also presents three case studies: LLM4FUZZ, SMARTINV, and BLOCKGPT, which demonstrate the state-of-the-art LLM4BS tasks. The paper discusses the challenges and future directions of LLM4BS, emphasizing the need for interdisciplinary collaboration, regulatory compliance, dynamic security threats, ethical governance, energy considerations, and data quality. The conclusion highlights the transformative potential of LLMs in blockchain security, while emphasizing the importance of ethical practices, regulatory alignment, and informed community engagement to ensure a resilient and equitable security future.This paper presents a systematic literature review on the application of Large Language Models (LLMs) in blockchain security (LLM4BS). The study aims to analyze existing research and elucidate how LLMs contribute to enhancing blockchain security. It explores the integration of LLMs into various aspects of blockchain security, including smart contract auditing, transaction anomaly detection, vulnerability repair, program analysis of smart contracts, and serving as participants in the cryptocurrency community. The review also assesses the challenges and limitations associated with leveraging LLMs for blockchain security, considering factors such as scalability, privacy concerns, and ethical concerns. The paper highlights the opportunities and potential risks of LLM4BS tasks, providing valuable insights for researchers, practitioners, and policymakers. The review outlines the current landscape of LLM applications across diverse domains and the security threats associated with blockchain technology. It elaborates on the incorporation and progression of LLM4BS tasks, including smart contract auditing, block transaction detection, contract dynamic analysis, smart contract development, and cryptocurrency community contributors. The study also presents three case studies: LLM4FUZZ, SMARTINV, and BLOCKGPT, which demonstrate the state-of-the-art LLM4BS tasks. The paper discusses the challenges and future directions of LLM4BS, emphasizing the need for interdisciplinary collaboration, regulatory compliance, dynamic security threats, ethical governance, energy considerations, and data quality. The conclusion highlights the transformative potential of LLMs in blockchain security, while emphasizing the importance of ethical practices, regulatory alignment, and informed community engagement to ensure a resilient and equitable security future.