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

Yuhao Liu, Yingnan Zhou, Weijie Liu, Yan Jia, Zheli Liu

arXiv:2608.26699v1cs.CRcs.SE

TL;DR

Kubernetes projects often omit explicit capability configuration, leaving a gap in least-privilege enforcement. KubeCap combines manifest rendering, entrypoint localization, reachability-guided syscall analysis, and LLM-assisted kernel-rule inference to generate minimized capability configurations. Across 10 representative Go-based projects, it achieved a 54.97% average capability reduction while maintaining practical analysis cost.

  • Problem

    Capability minimization in Kubernetes is not directly addressed by prior work, while 74.67% of projects lack explicit capability configurations.

  • Method

    KubeCap renders deployment specifications, locates container entrypoints, analyzes reachable system calls, derives syscall–parameter–capability rules from kernel code, and generates repaired manifests.

  • Results

    KubeCap achieved a 54.97% average capability reduction across 10 representative Kubernetes-related projects and outperformed RTA and CHA.

  • Takeaways & Limitations

    KubeCap provides a practical and effective approach toward least-privilege capability enforcement in Kubernetes.

  • Takeaways & Limitations

    The current implementation focuses on Go-based Kubernetes-related projects, limiting its direct generalizability.

Abstract

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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.

I. INTRODUCTION

Kubernetes capability configuration is often over-privileged or absent, motivating KubeCap, which infers workload-specific minimal capabilities and repairs manifests. Evaluation reports substantial capability reduction with practical overhead.

  • Over-privileged configurations expand attack surfaces, weaken container isolation, and may increase risks of container escape or host-level abuse.The example grants cloudify-manager CAP_SYS_ADMIN, exposing it to a broad set of kernel-level operations.
  • KubeCap translates deployment specifications into manifests, locates container entrypoints, analyzes reachable system calls, derives kernel-based rules, and infers minimal capability sets.
  • KubeCap compares declared and inferred capabilities and automatically generates repaired manifests that enforce least privilege.
  • 54.97% average capability reduction was achieved across 10 Kubernetes projects with practical time and memory overhead.The evaluation also reports higher average reduction than RTA and CHA.
  • 74.67% of projects do not explicitly configure capabilities, while only 19.33% explicitly remove default capabilities.

II. RELATED WORK

Prior work addresses Kubernetes misconfiguration detection, standalone-program capability inference, runtime mitigation, and API-layer hardening. KubeCap instead links rendered workloads to entrypoints, reachable syscalls, and kernel-derived rules for Linux capability minimization.

  • Existing Kubernetes studies detect security misconfigurations and trace configuration parameters across manifest files.
  • Other tools infer capabilities for privileged standalone programs through static or path-sensitive analysis, while runtime systems mitigate capability abuses.
  • Recent Kubernetes systems harden API access or recommend broader least-privilege configurations using manifests and runtime logs.
  • KubeCap focuses specifically on Linux capability minimization in container securityContext fields and is complementary to these systems.

B. Threat Model

Excessive capabilities create postcompromise risks that can extend from a container to the host or the wider cluster. The empirical study measures how frequently projects explicitly configure capabilities and apply least-privilege patterns.

  • Threat Model: Unnecessary capabilities enlarge the attack surface available after an attacker compromises a container.
  • Threat Model: Without Linux user namespaces, a root container may obtain node-root privileges after breakout, while granted capabilities remain valid on the host.
  • Threat Model: Excessive capabilities can enable container escape or node-level compromise and may contribute to cluster-wide compromise risks.
  • Study Design: The empirical study asks how often capabilities are explicitly configured and how granularly projects apply least-privilege patterns.
  • Study Design: The study re-analyzes two public datasets and a GitHub-derived dataset under a unified standard.

B. Prevalence of Explicit Capabilities Configuration

Capability configuration is uncommon across the three datasets, and securityContext usage is substantially more common than explicit capability configuration. These findings motivate capability analysis and minimization.

  • 74.67% of projects do not set capabilities, while only 19.33% drop all capabilities.
  • In Rahman et al.’s dataset, 17.3% configure capabilities and 13.5% use the drop-all pattern.
  • In Shamim et al.’s dataset, 26.7% configure capabilities and 13.3% adopt drop: ["ALL"].
  • The authors’ dataset shows that 28.9% configure capabilities and 24.1% use the drop-all pattern.
  • 53.33% of container projects specify securityContext, compared with 25.33% explicitly configuring capabilities.
  • The results indicate that least-privilege capability configuration is not common practice and motivate capability analysis and minimization.

B. Configuration Translator

KubeCap converts dynamic Kubernetes deployment specifications into deterministic manifests, then recovers each container’s actual executable and source entrypoint for workload-specific analysis.

  • Configuration paradigms: KubeCap supports Helm charts, Kustomize overlays, and plain YAML files as configuration paradigms.It prioritizes Helm, then Kustomize, then plain YAML when identifying repository configuration.
  • Deterministic rendering: Direct analysis of raw templates can lose deployment semantics and produce inaccurate privilege inference.The translator addresses this by generating deterministic Kubernetes manifests before analysis.
  • Deterministic rendering: For Helm repositories, KubeCap builds dependencies, renders charts, and persists the expanded output as a fixed intermediate artifact.It recursively discovers chart directories through Chart.yaml files while excluding common testing fixtures.
  • Manifest extraction: The translator extracts workloads, security contexts, container types, images, commands, and arguments from rendered manifests.It distinguishes regular containers from initContainers and records fields needed for entrypoint recovery.
  • Entrypoint recovery: KubeCap combines image defaults with Kubernetes command-resolution semantics, handles wrappers heuristically, and maps the executable to its source entry file.Go symbol analysis and repository path matching ensure later analysis targets workload-induced code paths rather than unrelated binaries.

D. System Call Analysis based on Graph Reachability

KubeCap uses SSA-based reachability from the true container entrypoint to classify system calls while filtering unrelated and unexecuted code.

  • Motivation: Full source-level scanning is impractical for cloud-native projects because standard libraries and dependencies create many false positives.KubeCap therefore restricts analysis to execution-reachable code.
  • Reachability analysis: The analysis initializes entry functions from the target main package and iteratively expands a worklist until reaching a fixed point.Each visited function is classified before its SSA callees are added for further exploration.
  • Syscall classification: KubeCap classifies reachable system calls into direct traps, library wrappers, and runtime calls.Examples include syscall.Syscall, x/sys/unix operations, and runtime futex- or mmap-related calls.
  • Analysis result: Restricting analysis to paths from the true entrypoint filters unlinked and unexecuted dead code.The resulting syscall set is tied to the specific Kubernetes configuration context.

E. LLM-assisted Rule Specification

KubeCap combines kernel capability-check slices, LLM-inferred conditional rules, and argument resolution to compute capabilities exercised by reachable workload paths and generate repaired manifests.

  • Kernel slice extraction: KubeCap extracts capability-relevant Linux kernel slices by seeding analysis at APIs such as capable() and ns_capable().Bounded caller tracing recovers syscall-level context around each capability check.
  • Rule inference: One-shot LLM prompting converts kernel slices into structured syscall–parameter–condition–capability rules.The required output includes syscall name, parameter information, trigger condition, inferred capability, and side condition.
  • Constraint resolution: Conditional rules require constraint resolution because capability checks may depend on syscall arguments rather than trigger unconditionally.KubeCap resolves relevant argument values through constant analysis and syscall-specific handlers.
  • Capability minimization: KubeCap aggregates satisfied requirements over reachable execution and computes Capmin, the minimized capability set.This connects syntactic syscall reachability with argument-level privilege requirements.
  • Manifest repair: KubeCap defines Capdel = Caporig \ Capmin and uses it to identify capabilities granted but not required by workload behavior.It generates patched manifests by dropping ALL and adding only Capmin.

VI. EVALUATION

The evaluation examines KubeCap’s effectiveness, call-graph strategy, and runtime performance on Kubernetes projects, using capability reduction and analysis cost as key measures.

  • Research questions: The evaluation addresses effectiveness in minimizing over-privileged capabilities, call-graph strategy effects, and runtime performance for large-scale analysis.These correspond to RQ1, RQ2, and RQ3.
  • Experimental setup: KubeCap is evaluated on 10 representative Go-based Kubernetes projects.The implementation uses ar-go-tools for program loading, SSA analysis, data-flow reasoning, and reachability analysis.

B. RQ1: Effectiveness of KubeCap

KubeCap removes redundant capabilities from real-world Kubernetes workloads while checking that minimized manifests preserve intended functionality. Across 10 projects, it achieves substantial capability reductions, including in highly over-privileged deployments.

  • Overall reduction effectiveness: 85.37% of capabilities were removed from Intel-ECI-ExCat-K8s, whose container initially used privileged: true.KubeCap identified 35 removable capabilities in this highly over-privileged workload.
  • Case studies: KubeCap retained capabilities required for correct execution while removing unnecessary privileges in the evaluated case studies.The patched amazon-vpc-cni-k8s manifest remained healthy and preserved inter-Pod connectivity; podinfo and Minibroker also passed their tested functionality checks.
  • Overall reduction effectiveness: 54.97% average capability reduction was achieved across 10 representative Go-based Kubernetes projects.KubeCap identifies declared capabilities unnecessary on reachable execution paths and removes them from the final policy.
  • Overall reduction effectiveness: 42.86% to 71.43% of default capabilities were eliminated across most evaluated subjects.For example, reductions included 42.86% in deckhouse, 50% in twitter-go, and 71.43% in both podinfo and ced.

C. RQ2: Ablation study

The ablation study compares reachability-guided analysis with CHA, RTA, and PTA. Reachability achieves the strongest reduction while avoiding PTA’s frequent timeouts and maintaining practical offline-analysis cost.

  • Capability reduction: 54.97% average capability reduction was achieved by Reachability, compared with 38.00% for RTA and 7.68% for CHA.Reachability removes an average of 8.8 capabilities, versus 6.9 for RTA and 1.8 for CHA.
  • Capability reduction: Reachability outperforms CHA across all projects and exceeds RTA on several key subjects.The compared subjects include amazon-vpc-cni-k8s, eks-anywhere, podinfo, ced, and Intel-ECI-ExCat-K8s.
  • Analysis precision: CHA analyzes more nodes but removes fewer capabilities because its coarse over-approximation retains unnecessary privilege requirements.Reachability often analyzes fewer nodes and focuses on code relevant to the deployed container through entrypoint-guided pruning.
  • Scalability: PTA is competitive when it completes but frequently exceeds the timeout threshold on larger real-world entrypoints.The reported timeouts arise from the cost of whole-program Andersen-style pointer analysis with reflection during call-graph construction.
  • Runtime overhead: Reachability incurs the highest runtime among strategies completing on all subjects, but remains practical for offline analysis.Its largest reported costs are around 150 seconds on amazon-vpc-cni-k8s and 130 seconds on eks-anywhere.

2) Memory overhead:

Reachability’s memory usage remains broadly comparable to CHA and RTA across the evaluated projects. KubeCap completes within minutes on the largest subjects while keeping memory usage generally within 1 to 3.5 GiB.

  • Memory overhead: 1 to 3.5 GiB was the approximate heap-memory range required by most subjects for Reachability, CHA, and RTA.The largest memory overhead occurred on amazon-vpc-cni-k8s, eks-anywhere, and gardener-extension-cri-resmgr, where all three methods exceeded 3 GiB on average.
  • Memory overhead: Reachability does not introduce a substantial memory penalty compared with CHA and RTA.Its peak heap usage is generally close to the other methods and was sometimes slightly lower than CHA.
  • Overall practicality: Reachability completes within a few minutes on the largest projects and within tens of seconds on most others.This runtime and memory profile supports practical scaling to the evaluated real-world cloud-native projects.
  • Scope boundary: KubeCap’s current implementation focuses on Go-based Kubernetes-related projects, limiting direct generalizability.The entrypoint recovery, SSA construction, and subsequent static analysis depend on the Go toolchain.

APPENDIX A RELIABILITY ANALYSIS OF CONDITIONAL SYSCALL–CAPABILITY RULES

KubeCap supplements coarse syscall-capability mappings with conditional rules that reason about syscall arguments and branch conditions. Auditing shows that many extracted rules can be resolved precisely, while case studies distinguish required from removable privileges.

  • Conditional rule construction: 40 capabilities, 124 syscalls, and 164 capability–syscall mappings are covered by the unconditional mapping.This mapping is coarse because it does not specify the argument values or branch predicates under which a capability is required.
  • Rule audit: 97 conditional rules were extracted, of which 77 were valid according to the kernel implementation.Validity required consistent capability mappings and condition directions.
  • Rule audit: 73 conditional rules had conditions solvable through constants or literals, and 64 were resolvable without over-approximation.These results support explicit argument-level reasoning for a substantial portion of conditional rules.
  • Rule examples: Concrete fcntl commands such as F_SETFL, F_SETLEASE, and F_SETPIPE_SZ enable more precise capability inference.Examples include CAP_FOWNER for F_SETFL with O_NOATIME, CAP_LEASE for F_SETLEASE, and CAP_SYS_RESOURCE when enlarging a pipe beyond its limit.
  • Case studies: Removing CAP_NET_ADMIN or all capabilities from aws-node caused readiness failures, while removing CAP_NET_ADMIN from aws-eks-nodeagent preserved functionality.Removing privileged execution from aws-node caused CrashLoopBackOff, so KubeCap conservatively retained required privileges.
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