Artificial Intelligence (AI) is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are very few studies that analyze the suitability of Large Language Models (LLMs) in network security. To fill this gap, we examine the suitability of LLMs in network security, particularly with the case study of STRIDE threat modeling. We utilize four prompting techniques with five LLMs to perform STRIDE classification of 5G threats. From our evaluation results, we point out key findings and detailed insights along with the explanation of the possible underlying factors influencing the behavior of LLMs in the modeling of certain threats. The numerical results and the insights support the necessity for adjusting and fine-tuning LLMs for network security use cases.

翻译：人工智能（AI）预计将成为下一代AI原生6G网络不可或缺的组成部分。随着AI的普及，研究人员已识别出AI在网络安全领域的众多应用场景。然而，目前鲜有研究分析大型语言模型（LLMs）在网络安全领域的适用性。为填补这一空白，本文以STRIDE威胁建模为案例，探讨LLMs在网络安全中的适用性。我们采用四种提示技术，结合五种LLMs对5G威胁进行STRIDE分类。基于评估结果，我们指出关键发现与详细见解，并阐释可能影响LLMs在特定威胁建模中行为的内在因素。数值结果与深入分析表明，针对网络安全应用场景调整和微调LLMs具有必要性。