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Analyzing and Mitigating (with LLMs) the Security Misconfigurations of Helm Charts from Artifact Hub

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arxiv 2403.09537 v1 pith:BROU7J4N submitted 2024-03-14 cs.SE

classification cs.SE
keywords helmchartsmisconfigurationstoolwillchartllmstools
verification ladder T0 review T1 audit T2 compute T3 formal
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Background: Helm is a package manager that allows defining, installing, and upgrading applications with Kubernetes (K8s), a popular container orchestration platform. A Helm chart is a collection of files describing all dependencies, resources, and parameters required for deploying an application within a K8s cluster. Objective: The goal of this study is to mine and empirically evaluate the security of Helm charts, comparing the performance of existing tools in terms of misconfigurations reported by policies available by default, and measure to what extent LLMs could be used for removing misconfiguration. We also want to investigate whether there are false positives in both the LLM refactorings and the tool outputs. Method: We propose a pipeline to mine Helm charts from Artifact Hub, a popular centralized repository, and analyze them using state-of-the-art open-source tools, such as Checkov and KICS. First, such a pipeline will run several chart analyzers and identify the common and unique misconfigurations reported by each tool. Secondly, it will use LLMs to suggest mitigation for each misconfiguration. Finally, the chart refactoring previously generated will be analyzed again by the same tools to see whether it satisfies the tool's policies. At the same time, we will also perform a manual analysis on a subset of charts to evaluate whether there are false positive misconfigurations from the tool's reporting and in the LLM refactoring.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How Good LLM-Generated Password Policies Are?

    cs.CR 2025-06 conditional novelty 6.0 of 10

    LLM-generated pwquality.conf password policies are frequently inconsistent, hallucinated, and incorrect, so they require validation before deployment in Linux PAM systems.

  2. A Survey of AIOps in the Era of Large Language Models

    cs.SE 2025-06 conditional novelty 3.0 of 10

    A systematic survey that categorizes LLM-based AIOps research into four dimensions: data sources, tasks, methods, and evaluation, claiming to be the first comprehensive such overview.

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