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Benchmarking Kubernetes With Kube Bench

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mukul975
benchmarking-kubernetes-with-kube-bench

Installs and runs the kube-bench tool against a Kubernetes cluster as a Job, DaemonSet, or standalone binary, selecting the correct benchmark version and targets (control plane, etcd, kubelet, worker nodes) and emitting JSON or JUnit output for pipelines. Use when setting kube-bench up for the first time, choosing which benchmark version and node targets to run, wiring it into CI, or troubleshooting skipped or misdetected checks. Keywords: kube-bench, DaemonSet, --benchmark, --targets, JSON output, JUnit, CI integration. Do not use for interpreting the findings or producing an audit report - use performing-kubernetes-cis-benchmark-with-kube-bench.

Overview

Publishermukul975
RepositoryAnthropic-Cybersecurity-Skills
Skill namebenchmarking-kubernetes-with-kube-bench
Stars
34.3K
Forks
4.2K
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Benchmarking Kubernetes With Kube Bench AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/benchmarking-kubernetes-with-kube-bench
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p .claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/benchmarking-kubernetes-with-kube-bench .claude/skills/benchmarking-kubernetes-with-kube-bench
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Benchmarking Kubernetes With Kube Bench in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Benchmarking Kubernetes With Kube Bench on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Benchmarking Kubernetes With Kube Bench is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Benchmarking Kubernetes with kube-bench

Overview

kube-bench (by Aqua Security) is an open-source tool that checks whether a Kubernetes cluster is deployed securely by running the checks documented in the CIS Kubernetes Benchmark. It inspects the control-plane components (API server, controller manager, scheduler, etcd), the kubelet and worker-node configuration, and cluster-wide policy settings, then reports each check as PASS, FAIL, WARN, or INFO with a remediation recommendation drawn directly from the CIS guidance. Tests are configuration-driven YAML files, so kube-bench tracks new Kubernetes versions and benchmark revisions and supports managed distributions (EKS, GKE, AKS, ACK, OpenShift, RKE, k3s).

Hardening a cluster against the CIS Benchmark directly reduces the attack surface for T1610 (Deploy Container), where an adversary deploys a container to execute code or evade defenses — for example by abusing privileged containers, host namespaces, anonymous API access, or insecure kubelet settings that an unhardened cluster leaves exposed.

kube-bench can run as a standalone binary on a node, inside a container, or — most commonly — as a Kubernetes Job whose pod has the host filesystem mounted so it can read the relevant config files. Output is available as human-readable text, JSON, JUnit, or AWS Security Finding Format (ASFF) and can be pushed to a PostgreSQL database for trend tracking.

When to Use

  • When establishing a security baseline for a new Kubernetes cluster against the CIS Kubernetes Benchmark.
  • When performing periodic compliance audits of control-plane and node hardening.
  • When validating remediation after applying hardening changes (re-run to confirm checks now PASS).
  • When integrating cluster compliance scanning into CI/CD or a continuous monitoring pipeline.
  • When preparing evidence for SOC 2, PCI DSS, or internal hardening compliance.

Prerequisites

  • Access to the cluster: either SSH access to a control-plane/worker node (binary mode) or kubectl with permission to create Jobs (in-cluster mode).
  • Knowledge of the cluster's Kubernetes version (kube-bench auto-detects, or specify with --version / --benchmark).
  • Install kube-bench (Aqua Security official methods):
bash
# Binary release (Linux)
KB_VERSION=0.10.7
curl -L -o kube-bench.tgz \
  "https://github.com/aquasecurity/kube-bench/releases/download/v${KB_VERSION}/kube-bench_${KB_VERSION}_linux_amd64.tar.gz"
tar -xzf kube-bench.tgz
sudo mv kube-bench /usr/local/bin/
sudo cp -R cfg /etc/kube-bench/cfg

# Via Go install
go install github.com/aquasecurity/kube-bench@latest

# Run as a one-off container directly on a node (mounts host config)
docker run --rm --pid=host \
  -v /etc:/etc:ro -v /var:/var:ro \
  -t docker.io/aquasec/kube-bench:latest run --targets node

# Verify
kube-bench version

Objectives

  • Run kube-bench against the appropriate benchmark for the cluster's Kubernetes version.
  • Scan control-plane (master), node, etcd, control-plane policies, and managed-service targets.
  • Produce machine-readable JSON/JUnit output for pipelines and dashboards.
  • Triage FAIL and WARN results and apply CIS remediation guidance.
  • Re-run to validate that remediations now PASS.

MITRE ATT&CK Mapping

Technique IDNameTacticRelevance
T1610Deploy ContainerExecution / Defense EvasionCIS Benchmark hardening enforced by kube-bench restricts privileged/host-namespace deployments, anonymous API access, and insecure kubelet settings that adversaries abuse when deploying malicious containers.

Workflow

1. Run the default scan (auto-detect)

Run all applicable targets, letting kube-bench detect the Kubernetes version and benchmark:

bash
sudo kube-bench

2. Run as a Kubernetes Job (in-cluster)

Apply the provided Job manifest from the kube-bench repo and read the results from the pod logs:

bash
# General-purpose job
kubectl apply -f https://raw.githubusercontent.com/aquasecurity/kube-bench/main/job.yaml

# Wait, then retrieve results
kubectl get pods -l app=kube-bench
kubectl logs -l app=kube-bench

# Platform-specific jobs are available, e.g. EKS:
kubectl apply -f https://raw.githubusercontent.com/aquasecurity/kube-bench/main/job-eks.yaml

3. Target specific components

Use run --targets to scope the scan to particular component groups:

bash
# Control-plane (API server, scheduler, controller manager)
sudo kube-bench run --targets master

# Worker node (kubelet, proxy)
sudo kube-bench run --targets node

# etcd datastore
sudo kube-bench run --targets etcd

# Cluster-wide policies (RBAC, pod security, network policy)
sudo kube-bench run --targets policies

# Combine multiple targets
sudo kube-bench run --targets master,node,etcd,policies

4. Pin a specific benchmark or Kubernetes version

When auto-detection is wrong or you must audit against a specific revision, pin the benchmark explicitly:

bash
# Pin to a specific CIS benchmark revision
sudo kube-bench run --benchmark cis-1.8

# Or map by Kubernetes version
sudo kube-bench --version 1.27

# Managed/distribution-specific benchmarks
sudo kube-bench run --benchmark eks-1.5.0
sudo kube-bench run --benchmark gke-1.6.0
sudo kube-bench run --benchmark rke2-cis-1.7

5. Run or skip individual checks

Focus on or exclude specific check IDs during remediation cycles:

bash
# Run only specific checks
sudo kube-bench run --targets master --check 1.2.1,1.2.2

# Skip noisy/known-accepted checks
sudo kube-bench run --targets node --skip 4.2.6

6. Produce machine-readable output

Emit JSON or JUnit for ingestion into pipelines, SIEM, or dashboards, and write to a file:

bash
# JSON to a file
sudo kube-bench run --targets master,node --json --outputfile kube-bench-report.json

# JUnit (for CI test reporting)
sudo kube-bench --junit --outputfile kube-bench-junit.xml

# AWS Security Finding Format (for Security Hub)
sudo kube-bench run --targets node --asff

7. Triage and remediate FAIL/WARN findings

Each failing check prints a remediation. Apply the CIS-recommended fix on the node/manifest, for example tightening API server flags in the static pod manifest:

bash
# Example remediation for a common control-plane FAIL:
# CIS 1.2.x — ensure anonymous-auth is disabled on the API server.
# Edit the static pod manifest and set the flag:
sudo vi /etc/kubernetes/manifests/kube-apiserver.yaml
#   - --anonymous-auth=false
# The kubelet restarts the static pod automatically.

# Example node remediation — kubelet config file permissions (CIS 4.1.x):
sudo chmod 600 /etc/kubernetes/kubelet/kubelet-config.json
sudo chown root:root /etc/kubernetes/kubelet/kubelet-config.json

8. Re-validate after remediation

Re-run the relevant target and confirm the previously failing checks now PASS, then track the score over time:

bash
sudo kube-bench run --targets master --check 1.2.1 --json --outputfile recheck.json

# Optional: persist results to PostgreSQL for trend tracking
sudo kube-bench run --targets master,node --pgsql

Tools and Resources

Tool / ResourcePurposeLink
kube-benchCIS Kubernetes Benchmark checkerhttps://github.com/aquasecurity/kube-bench
kube-bench docsRunning / platforms / flagshttps://aquasecurity.github.io/kube-bench/
CIS Kubernetes BenchmarkSource hardening standardhttps://www.cisecurity.org/benchmark/kubernetes
Trivy OperatorContinuous in-cluster compliance + vuln scanninghttps://github.com/aquasecurity/trivy-operator
kube-hunterComplementary penetration-testing toolhttps://github.com/aquasecurity/kube-hunter

Validation Criteria

  • kube-bench installed (kube-bench version) or running as a Job.
  • Scan run against the correct benchmark for the cluster's Kubernetes version.
  • master, node, etcd, and policies targets each scanned.
  • JSON/JUnit output produced for pipeline/dashboard ingestion.
  • FAIL and WARN findings triaged and prioritized.
  • CIS remediation applied to control-plane manifests and node configs.
  • Re-run confirms previously failing checks now PASS.
  • Results tracked over time (file archive or PostgreSQL).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Benchmarking Kubernetes With Kube Bench AI skill do?

Installs and runs the kube-bench tool against a Kubernetes cluster as a Job, DaemonSet, or standalone binary, selecting the correct benchmark version and targets (control plane, etcd, kubelet, worker nodes) and emitting JSON or JUnit output for pipelines. Use when setting kube-bench up for the first time, choosing which benchmark version and node targets to run, wiring it into CI, or troubleshooting skipped or misdetected checks. Keywords: kube-bench, DaemonSet, --benchmark, --targets, JSON output, JUnit, CI integration. Do not use for interpreting the findings or producing an audit report...

Why use Benchmarking Kubernetes With Kube Bench on TypingMind?

Because you install it once and use it with any model. Benchmarking Kubernetes With Kube Bench is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Benchmarking Kubernetes With Kube Bench in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/benchmarking-kubernetes-with-kube-bench. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Benchmarking Kubernetes With Kube Bench?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Benchmarking Kubernetes With Kube Bench?

As many as you like. As long as a model supports skills, you can use Benchmarking Kubernetes With Kube Bench with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Benchmarking Kubernetes With Kube Bench AI skill free?

Yes. It is published on GitHub by mukul975 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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