skills/benchmarking-kubernetes-with-kube-bench/SKILL.md
Run kube-bench (Aqua Security) against a Kubernetes cluster's control-plane, kubelet, and node configuration to check compliance with the CIS Kubernetes Benchmark and remediate PASS/FAIL/WARN findings. Use when establishing a security baseline for a new cluster, performing periodic hardening audits, validating remediation after configuration changes, or gathering compliance evidence for SOC 2/PCI DSS.
npx skillsauth add mukul975/anthropic-cybersecurity-skills benchmarking-kubernetes-with-kube-benchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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.
kubectl with permission to create Jobs (in-cluster mode).--version / --benchmark).# 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
| 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. |
Run all applicable targets, letting kube-bench detect the Kubernetes version and benchmark:
sudo kube-bench
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
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
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
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
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
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
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
| 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 |
kube-bench version) or running as a Job.tools
Executes authorized phishing simulation campaigns to assess an organization's susceptibility to email-based social engineering attacks. The tester designs realistic phishing scenarios, builds credential harvesting infrastructure, sends targeted phishing emails, and tracks open rates, click-through rates, and credential submission rates to measure human security awareness. Activates for requests involving phishing simulation, social engineering assessment, email security testing, or security awareness measurement.
development
Drive a federal system through the NIST Risk Management Framework (SP 800-37 Rev 2) to an Authorization to Operate (ATO): Prepare, Categorize (FIPS 199), Select a control baseline (FIPS 200 / SP 800-53 Rev 5), Implement, Assess (SP 800-53A), Authorize, and Monitor continuously. Use when a system needs an ATO or a renewal, when working a FISMA/FedRAMP authorization package, when building or reviewing an SSP, SAR, or POA&M, when categorizing a system as Low/Moderate/High impact, when selecting or tailoring a control baseline, or when standing up continuous monitoring (ConMon) after authorization. Covers ATO, conditional ATO (cATO), and the artifacts assessors expect. Keywords: NIST RMF, 800-37, ATO, authorization to operate, FISMA, FedRAMP, SSP, SAR, POA&M, FIPS 199, FIPS 200, 800-53, 800-53A, control baseline, security categorization, continuous monitoring, authorizing official, system boundary, ongoing authorization.
development
Executes authorized attack simulations against Active Directory environments to identify misconfigurations, weak credentials, dangerous privilege paths, and exploitable trust relationships that could lead to domain compromise. The tester uses BloodHound for attack path analysis, Mimikatz for credential extraction, and Impacket for protocol-level attacks including Kerberoasting, AS-REP Roasting, and delegation abuse. Activates for requests involving Active Directory pentest, AD attack simulation, domain compromise testing, or Kerberos attack assessment.
tools
Evaluates and selects Threat Intelligence Platform (TIP) products based on organizational requirements including feed integration capability, STIX/TAXII support, workflow automation, analyst interface, and total cost of ownership. Use when conducting a TIP procurement, migrating between TIP solutions, or assessing whether the current TIP meets program maturity requirements. Activates for requests involving ThreatConnect, MISP, OpenCTI, Anomali, EclecticIQ, or TIP procurement decisions.