运行时拓扑上下文能否改进大语言模型生成的Kubernetes安全补丁？
Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?
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中文总结 AI 辅助
研究Kubernetes安全补丁问题，引入KuTIE利用实时集群上下文改进大语言模型生成补丁，通过在VulnCare平台试验，发现拓扑上下文可大幅提升拓扑相关补丁正确性，远超仅依赖扫描器上下文。
中文摘要 AI 辅助
Kubernetes在云原生生态系统中至关重要，用于编排容器化工作负载。近期研究表明大语言模型可自动化集群安全修复，根据Kubernetes安全态势管理（KSPM）结果生成配置补丁。但现有系统孤立地用每个发现提示模型，假设一般强化知识就足够。当补丁需保留模型不可见的运行时服务依赖时此假设不成立。我们引入KuTIE（Kubernetes拓扑智能引擎），它根据Istio调用边、Trivy KSPM结果和工作负载读取的服务账户绑定构建实时集群上下文，并在此基础上进行大语言模型补丁生成。在VulnCare上评估，该平台有36个部署、4个命名空间，有7个依赖类别的31个可注入发现。248次试验中，拓扑上下文将拓扑相关补丁正确性从11.1%提高到78.0%，而与拓扑无关的控制无此效果。提供实时服务调用图及其暴露的服务账户绑定能显著改进拓扑相关发现的修复。
英文摘要
Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads. Recent work suggests that large language models (LLMs) can automate cluster security remediation, generating configuration patches from Kubernetes Security Posture Management (KSPM) findings without human authoring. Such systems, however, prompt the model with each finding in isolation from the live service call graph, assuming general hardening knowledge suffices. This assumption breaks down whenever a patch must preserve a runtime service dependency invisible to the model: an otherwise compliant fix then carries a destructive functional blast radius, crashing downstream callers or silently severing call edges across the cluster. Whether live cluster context improves patch correctness has not been measured under controlled conditions across multiple dependency classes. We introduce KuTIE (Kubernetes Topology Intelligence Engine), which builds a live cluster context from Istio call edges, Trivy KSPM findings, and the service-account bindings a workload reads, and conditions LLM patch generation on it. It is evaluated on VulnCare, a purpose-built 36-deployment, four-namespace healthcare cluster with 31 injectable findings across seven dependency classes, each labelled by topology dependence against cluster ground truth. Across 248 trials, topology context raises topology-dependent patch correctness from 11.1% to 78.0% ($Δ= 0.669$), a gap that holds for every model and for six of seven classes, from credential and network-policy ($Δ= 0.95$) to role-based access control ($Δ= 0.31$); a topology-independent control exhibits no such effect ($Δ= 0.0$), isolating the result from generic prompt enrichment. Supplying the live service-call graph and the service-account bindings it exposes thus improves remediation of topology-dependent findings well beyond scanner-only context.
发表机构
- Dynatrace Research（Dynatrace研究院）
机构由 AI 辅助整理，请以论文原文为准。
