Analyzing and mitigating (with LLMs) the security misconfigurations of Helm charts from Artifact Hub.

IF 3.6 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Empirical Software Engineering Pub Date : 2025-01-01 Epub Date: 2025-07-04 DOI:10.1007/s10664-025-10688-0
Francesco Minna, Fabio Massacci, Katja Tuma
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引用次数: 0

Abstract

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. This study aimed 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 measuring to what extent LLMs could be used for removing misconfigurations. For these reasons, we proposed a pipeline to mine Helm charts from Artifact Hub, a popular centralized repository, and analyze them using state-of-the-art open-source tools like Checkov and KICS. First, the pipeline runs several chart analyzers and identifies the common and unique misconfigurations reported by each tool. Secondly, it uses LLMs to suggest a mitigation for each misconfiguration. Finally, the LLM refactored chart previously generated is analyzed again by the same tools to see whether it satisfies the tool's policies. We also performed 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. We found that (i) there is a significant difference between LLMs, (ii) providing a snippet of the YAML template as input might be insufficient compared to all resources, and (iii) even though LLMs can generate correct fixes, they may also delete other irrelevant configurations that break the application.

分析和减轻(使用llm)来自Artifact Hub的Helm图表的安全错误配置。
Helm是一个包管理器,它允许使用Kubernetes (k8)定义、安装和升级应用程序,Kubernetes是一个流行的容器编排平台。Helm图是描述在K8s集群中部署应用程序所需的所有依赖项、资源和参数的文件集合。本研究旨在挖掘和经验评估Helm图表的安全性,比较现有工具在默认可用策略报告的错误配置方面的性能,并衡量llm可用于消除错误配置的程度。由于这些原因,我们提出了从Artifact Hub(一个流行的集中式存储库)挖掘Helm图表的管道,并使用最先进的开源工具(如Checkov和KICS)分析它们。首先,管道运行几个图表分析器,并识别每个工具报告的常见和唯一的错误配置。其次,它使用llm来建议每个错误配置的缓解措施。最后,由相同的工具再次分析之前生成的LLM重构图,以查看它是否满足工具的策略。我们还对图表子集执行了手动分析,以评估工具报告和LLM重构中是否存在误报的错误配置。我们发现(i) llm之间存在显著差异,(ii)与所有资源相比,提供YAML模板的一个片段作为输入可能不够,以及(iii)即使llm可以生成正确的修复,它们也可能删除破坏应用程序的其他不相关配置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Empirical Software Engineering
Empirical Software Engineering 工程技术-计算机:软件工程
CiteScore
8.50
自引率
12.20%
发文量
169
审稿时长
>12 weeks
期刊介绍: Empirical Software Engineering provides a forum for applied software engineering research with a strong empirical component, and a venue for publishing empirical results relevant to both researchers and practitioners. Empirical studies presented here usually involve the collection and analysis of data and experience that can be used to characterize, evaluate and reveal relationships between software development deliverables, practices, and technologies. Over time, it is expected that such empirical results will form a body of knowledge leading to widely accepted and well-formed theories. The journal also offers industrial experience reports detailing the application of software technologies - processes, methods, or tools - and their effectiveness in industrial settings. Empirical Software Engineering promotes the publication of industry-relevant research, to address the significant gap between research and practice.
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