The paper "Large Language Models for Blockchain Security: A Systematic Literature Review" by Zheyuan He, Zihao Li, Sen Yang, He Ye, Ao Qiao, Xiaosong Zhang, Ting Chen, and Xiapu Luo explores the application of Large Language Models (LLMs) in enhancing blockchain security. The authors conduct a comprehensive literature review to understand the current state and future potential of LLMs in blockchain security, focusing on tasks such as smart contract auditing, transaction anomaly detection, vulnerability repair, program analysis, and community engagement. They highlight the benefits of LLMs, including their ability to handle complex patterns, generate contextually relevant text, and adapt to specific tasks through fine-tuning. However, they also address challenges such as scalability, privacy, and ethical concerns. The paper provides a detailed taxonomy of LLM4BS tasks and presents case studies of LLM4FUZZ, SMARTINV, and BLOCKGPT to illustrate their practical applications. Finally, the authors discuss future directions, emphasizing the need for interdisciplinary collaboration, regulatory alignment, ethical governance, and sustainable practices to fully realize the potential of LLMs in blockchain security.The paper "Large Language Models for Blockchain Security: A Systematic Literature Review" by Zheyuan He, Zihao Li, Sen Yang, He Ye, Ao Qiao, Xiaosong Zhang, Ting Chen, and Xiapu Luo explores the application of Large Language Models (LLMs) in enhancing blockchain security. The authors conduct a comprehensive literature review to understand the current state and future potential of LLMs in blockchain security, focusing on tasks such as smart contract auditing, transaction anomaly detection, vulnerability repair, program analysis, and community engagement. They highlight the benefits of LLMs, including their ability to handle complex patterns, generate contextually relevant text, and adapt to specific tasks through fine-tuning. However, they also address challenges such as scalability, privacy, and ethical concerns. The paper provides a detailed taxonomy of LLM4BS tasks and presents case studies of LLM4FUZZ, SMARTINV, and BLOCKGPT to illustrate their practical applications. Finally, the authors discuss future directions, emphasizing the need for interdisciplinary collaboration, regulatory alignment, ethical governance, and sustainable practices to fully realize the potential of LLMs in blockchain security.