# Towards Secure Cloud-Native Computing: Unveiling Kubernetes Misconfigurations with Large Language Models

arXiv: https://arxiv.org/abs/2609.20834 · HTML: https://mlsanity.com/paper/2609.20834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 25 Jul 2026.

## Builds on (most cited first, 19 of 19)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Evaluating Commonsense in Pre-trained Language Models](https://mlsanity.com/paper/1911.11931/) (2019)
- [LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report](https://mlsanity.com/paper/2405.00732/) (2024)
- [Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices](https://mlsanity.com/paper/2406.03777/) (2024)
- [GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation](https://mlsanity.com/paper/2405.19954/) (2024)
- [Analyzing and Mitigating (with LLMs) the Security Misconfigurations of Helm Charts from Artifact Hub](https://mlsanity.com/paper/2403.09537/) (2024)
- [DeVAIC: A Tool for Security Assessment of AI-generated Code](https://mlsanity.com/paper/2404.07548/) (2024)
- [Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation](https://mlsanity.com/paper/2411.06221/) (2024)
- [LLM-Based Misconfiguration Detection for AWS Serverless Computing](https://mlsanity.com/paper/2411.00642/) (2024)
- [The Effects of In-domain Corpus Size on pre-training BERT](https://mlsanity.com/paper/2212.07914/) (2022)
- [VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model](https://mlsanity.com/paper/2309.08474/) (2023)
- [XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection](https://mlsanity.com/paper/2309.14677/) (2023)
- [Centralized Defense: Logging and Mitigation of Kubernetes Misconfigurations with Open Source Tools](https://mlsanity.com/paper/2408.03714/) (2024)
- [CAIP: Detecting Router Misconfigurations with Context-Aware Iterative Prompting of LLMs](https://mlsanity.com/paper/2411.14283/) (2024)
- [Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments](https://mlsanity.com/paper/2405.01888/) (2024)
- [Detect Llama -- Finding Vulnerabilities in Smart Contracts using Large Language Models](https://mlsanity.com/paper/2407.08969/) (2024)
- [Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities](https://mlsanity.com/paper/2409.10574/) (2024)
- [Why Not Mitigate Vulnerabilities in Helm Charts?](https://mlsanity.com/paper/2312.15350/) (2023)
- [Qubernetes: Towards a Unified Cloud-Native Execution Platform for Hybrid Classic-Quantum Computing](https://mlsanity.com/paper/2408.01436/) (2024)

## Abstract (arXiv)

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.