In the rapidly evolving landscape of cloud-native computing
Organizations are increasingly adopting infrastructure models that emphasize scalability, flexibility, and efficiency.
Kubernetes
Kubernetes has become the de facto standard for orchestrating containerized applications in these environments.
Challenges
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
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.
Empirical Evaluation
Additionally, we conduct an empirical evaluation of state-of-the-art detection tools to benchmark their effectiveness.
Analysis
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.