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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

Install / Use

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill benchmarking-kubernetes-with-kube-bench

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

98/100

Category

Automation

Supported Platforms

Universal

Our assessment of benchmarking-kubernetes-with-kube-bench

benchmarking-kubernetes-with-kube-bench scores 98/100 on our quality scale, 42nd of 1,111 Automation skills we index (top 4%).

Its SKILL.md is 9.1 KB long, well organised into 44 sections with 9 code examples: a thorough specification that gives an agent plenty to work with.

With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 25 days ago, so benchmarking-kubernetes-with-kube-bench is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

benchmarking-kubernetes-with-kube-bench compared with similar skills

All 4 of these similar skills score higher than benchmarking-kubernetes-with-kube-bench; compare them before choosing.

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benchmarking-kubernetes-with-kube-bench (this skill)by mukul9759833.3k25d agoSKILL.md
Agent-Reachby Panniantong10085.4k9d agoCLAUDE.md
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Frequently asked questions

How do I install benchmarking-kubernetes-with-kube-bench?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill benchmarking-kubernetes-with-kube-bench. The install tabs above show the steps for each supported agent.
Which AI agents does benchmarking-kubernetes-with-kube-bench work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is benchmarking-kubernetes-with-kube-bench safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is benchmarking-kubernetes-with-kube-bench still maintained?
The repository was last updated 25 days ago, so benchmarking-kubernetes-with-kube-bench is actively maintained.

name: benchmarking-kubernetes-with-kube-bench description: >- 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. domain: cybersecurity subdomain: container-security tags:

  • kubernetes
  • kube-bench
  • cis-benchmark
  • container-security
  • hardening
  • compliance
  • cluster-security version: '1.0' author: mahipal license: Apache-2.0 nist_csf:
  • PR.PS-01 mitre_attack:
  • T1610

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):
# 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 ID | Name | Tactic | Relevance | |--------------|------|--------|-----------| | T1610 | Deploy Container | Execution / Defense Evasion | CIS 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:

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:

# 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:

# 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:

# 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:

# 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:

# 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:

# 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:

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 / Resource | Purpose | Link | |------------------|---------|------| | kube-bench | CIS Kubernetes Benchmark checker | https://github.com/aquasecurity/kube-bench | | kube-bench docs | Running / platforms / flags | https://aquasecurity.github.io/kube-bench/ | | CIS Kubernetes Benchmark | Source hardening standard | https://www.cisecurity.org/benchmark/kubernetes | | Trivy Operator | Continuous in-cluster compliance + vuln scanning | https://github.com/aquasecurity/trivy-operator | | kube-hunter | Complementary penetration-testing tool | https://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).

Related Skills

View on GitHub
GitHub Stars33.3k
CategoryAutomation
Updated25d ago
Forks4.0k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions