Large Language Models- Powered Identification and Analysis of Kubernetes Misconfigurations

Abstract

Kubernetes configuration files (KCFs) present a significant challenge due to their inherent complexity and proneness to errors, often leading to security vulnerabilities and operational disruptions. Traditional rule-based (RB) tools designed for detecting KCF misconfigurations rely on predefined, static rule sets, which limit their adaptability and ability to identify newly emerging misconfigurations. Moreover, RB tools frequently suffer from inaccuracies caused by errors in coding detection rules. Current approaches for identifying and analysing KCF misconfigurations are often constrained by limited scalability, narrow detection coverage, or high expertise demands, and they typically lack automated remediation alongside detection capabilities. Recent innovations utilizing large language models (LLMs) in this domain often depend on API-driven, generalpurpose, and primarily commercial models, which introduce security concerns, inconsistent classification results, and elevated costs. In this paper, we present GenKubeSec, a robust and adaptive LLM-powered framework that not only detects a broad spectrum of KCF misconfigurations but also pinpoints their precise locations, provides detailed reasoning. Empirical evaluations reveal that GenKubeSec achieves comparable precision compared to three leading industry-standard RB tools. Additionally, an independent review of a random sample of KCFs by a Kubernetes security expert validated that GenKubeSec's explanations for Identification and Analysis were 100% accurate, insightful, and highly beneficial.