KubeCap: A Framework for Capability Minimization in Kubernetes via Static Analysis and LLM-Assisted Rule Inference

• URL: http://arxiv.org/abs/2608.26699v1
• Date: Thu, 27 Aug 2026 06:55:24 GMT
• Title: KubeCap: A Framework for Capability Minimization in Kubernetes via Static Analysis and LLM-Assisted Rule Inference
• Abstract summary: Developers rely on default settings or coarse-grained security contexts in practice, violating the principle of least privilege.<n> Existing studies either detect vulnerable patterns in manifests or infer required capabilities for standalone Linux programs.<n>We propose KubeCap, a framework for capability minimization.
• Score: 11.250679084544062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the most widely used container orchestration platform, Kubernetes provides flexible privilege configuration by allowing developers to manage Linux capabilities via manifest files. However, developers rely on default settings or coarse-grained security contexts in practice, violating the principle of least privilege and enlarging the attack surface of containerized workloads. Existing studies either detect vulnerable patterns in Kubernetes manifests or infer required capabilities for standalone Linux programs, but they do not directly address capability minimization in Kubernetes. To bridge this gap, we first conduct an empirical study on three open-source datasets, revealing that 74.67% of projects lack capability configurations. Motivated by our observations, we propose KubeCap, a framework for Kubernetes capability minimization. KubeCap translates deployment specifications into deterministic manifests, locates container entrypoints, performs reachability-guided system call analysis, and leverages LLM-assisted rule specification to derive syscall--parameter--capability relations from Linux kernel code. Based on these results, KubeCap infers the minimal capability set required by each workload and automatically generates repaired manifests. Evaluation on 10 representative Go-based Kubernetes projects shows an average capability reduction rate of 54.97%, outperforming rapid type analysis and class hierarchy analysis baselines while maintaining practical analysis cost. These results demonstrate KubeCap's effectiveness in enforcing least privilege in Kubernetes.

Related papers

• PrivEscalate: Measuring and Augmenting the Threat of LLM-Automated Linux Privilege Escalation [6.17282657130573]
  Large Language Model (LLM) agents increasingly automate offensive operations across the cyber kill chain.<n>PrivEscalate is a large-scale benchmark for Linux privilege escalation comprising 531 Dockerized scenarios spanning 14 sub-categories.<n>We develop PrivEscAgent, a wrapper that augments a generic ReAct agent with deterministic enumeration, category matching, and step planning.
  arXiv  Detail & Related papers  (2026-09-08T17:34:02Z)
• SIR: Self-improving Red-teaming for Compute Use Agents [71.71987044117371]
  Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal.<n>Because they can be exposed to untrusted content while operating, they are vulnerable to indirect prompt injection (IPI)<n>We present SIR, a black box IPI attack that composes stealthy injections from a small library of reusable principles stated in plain language.
  arXiv  Detail & Related papers  (2026-08-31T03:39:43Z)
• Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications [57.33208520608409]
  This paper presents a systematic study of prompt leaking in large language model (LLM)-based applications.<n>We find that over 80% of deployments leak system prompts under realistic adversarial queries.<n>We propose AREA, a practical defense that re-anchors the model's attention using an optimizable soft prompt.
  arXiv  Detail & Related papers  (2026-06-17T04:20:00Z)
• What to Test Next: Interpretable Coverage Gap Discovery in Driving VLMs [52.50210189669399]
  Driving vision-language models (VLMs) must accurately understand scenes across diverse conditions defined by Operational Design Domains (ODDs)<n>We propose SliceScorer, a deterministic scoring rule for missing-slice recommendation.<n>We show that SliceNav surfaces high-risk coverage gaps more effectively than prior slice-discovery methods.
  arXiv  Detail & Related papers  (2026-06-01T03:18:01Z)
• Context-Instrumental Data Distillation for Kubernetes Manifest Generation: Method and Experimental Evaluation [36.94429692322632]
  This paper examines the specialization of Small Language Models with up to 4 billion parameters for generating artifacts in domain-specific languages (AML)<n>We propose the context-instrumental data distillation method: the source corpus is formed through synthetic generation and, in an extended scheme, through reverse instruction generation from real Y files.
  arXiv  Detail & Related papers  (2026-05-25T13:30:38Z)
• NimbusGuard: A Novel Framework for Proactive Kubernetes Autoscaling Using Deep Q-Networks [0.0]
  Cloud native architecture is about building and running scalable microservice applications to take full advantage of the cloud environments.<n>We propose NimbusGuard, an open-source autoscaling system that leverages a deep reinforcement learning agent to provide proactive autoscaling.<n>The evaluations were conducted by comparing NimbusGuard against the built-in scaling controllers, such as Horizontal Pod Autoscaler, and the event-driven autoscaler KEDA.
  arXiv  Detail & Related papers  (2026-04-13T05:32:12Z)
• Optimizing OpenFaaS on Kubernetes: Comparative Analysis of Language Runtimes and Cluster Distributions [0.0]
  Serverless computing has revolutionized cloud computing by abstracting infrastructure management and enabling dynamic resource allocation.<n>This paper examines the performance and compatibility of OpenF, when deployed on various distributions, including Kubeadm, K3s, MicroK8s, and K0s.<n>This study examines the impact of Python, Go, and Node.js programming languages on the performance of OpenF-enabled functions.
  arXiv  Detail & Related papers  (2026-04-07T06:38:29Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• Cloud-OpsBench: A Reproducible Benchmark for Agentic Root Cause Analysis in Cloud Systems [51.2882705779387]
  Cloud-OpsBench is a large-scale benchmark that employs a State Snapshot Paradigm to construct a deterministic digital twin of the cloud.<n>It features 452 distinct fault cases across 40 root cause types spanning the full stack.
  arXiv  Detail & Related papers  (2026-02-28T05:04:42Z)
• KubeGuard: LLM-Assisted Kubernetes Hardening via Configuration Files and Runtime Logs Analysis [21.638320707123444]
  KubeGuard is a novel runtime log-driven recommender framework for cluster security.<n>It is designed to harden K8s environments through two complementary tasks: Resource Creation and Resource Refinement.<n>Our evaluation demonstrates that KubeGuard effectively generates and refines K8s manifests for Roles, NetworkPolicies, and Deployments.
  arXiv  Detail & Related papers  (2025-09-04T13:13:57Z)
• Comparative Analysis of Lightweight Kubernetes Distributions for Edge Computing: Security, Resilience and Maintainability [0.0]
  This study compares and analyzes the lightweight distributions k3s, k0s, KubeEdge, and OpenYurt.<n>Results indicate that while k3s and k0s offer superior ease of development due to their simplicity, they have lower security compliance compared to, KubeEdge, and OpenYurt.<n>Findings highlight trade-offs between performance, security, resiliency, and maintainability, offering insights for practitioners deploying in edge environments.
  arXiv  Detail & Related papers  (2025-03-04T20:05:40Z)
• Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond [171.07853346630057]
  Linear relaxation based perturbation analysis (LiRPA) for neural networks has become a core component in robustness verification and certified defense. We develop an automatic framework to enable perturbation analysis on any neural network structures. We demonstrate LiRPA based certified defense on Tiny ImageNet and Downscaled ImageNet.
  arXiv  Detail & Related papers  (2020-02-28T18:47:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.