Kubernetes Misconfigurations in the Wild: Taxonomy, Evolution, and Automated Repair with Large Language Models

• Some operational Kubernetes issues decline as projects mature from incubator to stable, but the study finds critical security misconfigurations often persist or reappear.
• Adding context improved LLM remediation, with the best standalone model correcting 89.06% of misconfigurations.
• Kubecurity, a validation layer built on the official Kubernetes specifications, pushed correction accuracy to 98.50% when combined with LLM reasoning and substantially cut newly introduced misconfigurations.

• grounding — Tying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.
• context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.

Gives practitioners a map of which Kubernetes misconfiguration classes persist through project maturity and how well LLMs can actually fix them with added context.

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