迈向安全的云原生计算：利用大语言模型揭示Kubernetes配置错误
Towards Secure Cloud-Native Computing: Unveiling Kubernetes Misconfigurations with Large Language Models
AI总结：
本研究利用大语言模型识别Kubernetes配置错误，提出综合分类法并实证评估检测工具，为增强云原生安全提供新见解。
AI中文摘要：
在快速发展的云原生计算领域，各组织日益采用强调可扩展性、灵活性和效率的基础设施模型。Kubernetes已成为在这些环境中编排容器化应用的事实标准。然而，云原生生态系统的固有复杂性带来了重大挑战，尤其是可能危及安全性和性能的配置错误。本研究探讨了大语言模型（LLMs）在识别Kubernetes配置错误方面的潜力。我们引入了一个常见配置错误类型的综合分类法，提供了一个结构化框架以更好地理解和分类这些问题。此外，我们对最先进的检测工具进行了实证评估，以基准测试其有效性。进一步地，我们分析了最易发生配置错误的Kubernetes对象，并评估了已识别问题的严重性。通过利用包括LLMs在内的先进机器学习技术，我们为增强配置错误检测方法提供了新颖的见解。
英文摘要：
In the rapidly evolving landscape of cloud-native computing, Organizations are increasingly adopting infrastructure models that emphasize scalability, flexibility, and efficiency. Kubernetes has become the de facto standard for orchestrating containerized applications in these environments. However, the inherent complexity of cloud-native ecosystems introduces significant challenges, particularly in the form of misconfigurations that can compromise both security and performance. This study explores the potential of Large Language Models (LLMs) in identifying Kubernetes misconfigurations. We introduce a comprehensive taxonomy of common misconfiguration types, offering a structured framework to better understand and categorize these issues. Additionally, we conduct an empirical evaluation of state-of-the-art detection tools to benchmark their effectiveness. Furthermore, we analyze the Kubernetes objects most prone to misconfiguration and evaluate the severity of the identified issues. By leveraging advanced machine learning techniques, including LLMs, we provide novel insights into enhancing misconfiguration detection methodologies.
