auditing-kubernetes-cluster-rbac
by mukul975auditing-kubernetes-cluster-rbac helps audit Kubernetes RBAC for overbroad roles, risky bindings, secret access, and privilege escalation paths. It is built for security audit workflows across EKS, GKE, AKS, and self-managed clusters, with practical guidance for kubectl, rbac-tool, KubiScan, and Kubeaudit.
This skill scores 82/100, which means it is a solid directory listing candidate for users who need Kubernetes RBAC audit help. The repo provides a real, security-focused workflow with concrete tooling and code, so install value is clear, though some operational details still require user judgment.
- Explicitly scoped to Kubernetes RBAC auditing with clear use cases and non-goals, making triggerability strong.
- Includes a substantial SKILL.md plus a Python agent script and API reference, giving agents concrete workflow leverage.
- Calls out practical tools and targets threat areas like wildcard permissions, dangerous ClusterRoleBindings, service account abuse, and privilege escalation paths.
- No install command or setup recipe in SKILL.md, so users may need to figure out environment setup themselves.
- The excerpt shows prerequisites and API details, but the repo evidence does not confirm end-to-end run instructions or output interpretation guidance.
Overview of auditing-kubernetes-cluster-rbac skill
What this skill does
The auditing-kubernetes-cluster-rbac skill helps you audit Kubernetes RBAC for overbroad access, risky bindings, and privilege escalation paths. It is most useful when you need a fast, evidence-based review of cluster permissions for a Security Audit, not just a generic “check RBAC” prompt.
Who should use it
Use the auditing-kubernetes-cluster-rbac skill if you work with EKS, GKE, AKS, or self-managed clusters and need to validate least privilege for users, service accounts, and workloads. It is a good fit for cloud security engineers, platform teams, auditors, and incident responders.
What makes it different
This skill is oriented around concrete RBAC failure modes: wildcard verbs or resources, dangerous ClusterRoleBindings, secret access, and service account abuse. It also aligns with common Kubernetes tooling such as kubectl, rbac-tool, KubiScan, and Kubeaudit, which makes the output more actionable than a vague policy review.
How to Use auditing-kubernetes-cluster-rbac skill
Install and first-read path
For auditing-kubernetes-cluster-rbac install, add the skill from the repository and then read skills/auditing-kubernetes-cluster-rbac/SKILL.md first. After that, inspect references/api-reference.md for API patterns and scripts/agent.py for the actual detection logic. Those files show what the skill expects to inspect and where its recommendations come from.
Give it the right audit scope
The best auditing-kubernetes-cluster-rbac usage starts with a specific cluster, namespace set, or incident question. Good inputs name the environment, identity type, and concern, for example: “Audit RBAC in EKS cluster prod-west for any subject that can read secrets or create role bindings.” Weak inputs like “review Kubernetes permissions” usually produce shallow results.
Prompt shape that works
Use the auditing-kubernetes-cluster-rbac guide like a short assessment brief:
- cluster type and context: EKS, GKE, AKS, or on-prem
- target scope: cluster-wide or one namespace
- focus area: wildcard permissions, secret access, binding drift, service accounts
- constraints: read-only access, no Helm changes, no cluster-admin assumptions
- output format: findings table, risk ranking, remediation steps
A stronger request is: “Run an RBAC audit on namespace payments, identify Roles or RoleBindings that allow secret reads, wildcard verbs, or privilege escalation, and return remediation recommendations with exact resource names.”
Practical workflow
Start broad, then narrow. First enumerate ClusterRoles and ClusterRoleBindings, then inspect namespace-scoped Roles and RoleBindings, then map high-privilege subjects to service accounts and pods. If the first pass finds a risky binding, trace which workloads or teams inherit it before deciding whether it is a true issue or an intentional admin path.
auditing-kubernetes-cluster-rbac skill FAQ
Is this better than a normal prompt?
Yes, if you need a repeatable Kubernetes RBAC audit rather than a one-off answer. The skill provides a tighter workflow, clearer detection targets, and better file-based guidance than prompting from scratch.
Do I need Kubernetes experience?
Basic cluster familiarity helps, but the skill is still usable for beginners who can provide a kubeconfig and describe the audit goal. If you do not know the difference between Roles and ClusterRoles, read the references first so you can phrase the request accurately.
When should I not use it?
Do not use auditing-kubernetes-cluster-rbac for network policy review, container image scanning, or runtime detection. It is focused on access control and RBAC, so those other problems need different tools and different prompts.
What is the main limitation?
The skill depends on meaningful cluster visibility. If your account cannot list RBAC objects or inspect service account usage, the output will be incomplete. It also cannot decide intent on its own, so you still need to confirm whether a risky binding is approved or accidental.
How to Improve auditing-kubernetes-cluster-rbac skill
Provide evidence, not just a goal
The best way to improve auditing-kubernetes-cluster-rbac results is to include concrete objects and constraints: role names, namespaces, suspicious subjects, and the access path you want checked. For example, ask it to trace ClusterRoleBinding admin-binding to the service account used by payments-api and check whether it can reach secrets or create pods with elevated security contexts.
Watch for common failure modes
The most common miss is vague scope. Another is asking for “all risks” without specifying whether you care about read access, write access, escalation, or compliance evidence. A third is assuming every wildcard is automatically malicious; the better workflow is to have the skill surface candidates, then evaluate them against intended operational needs.
Iterate after the first pass
Use the first output to refine the next request. If it returns too many low-value findings, narrow by namespace, resource type, or verb. If it misses suspected abuse paths, ask for a second pass focused on service accounts, pods, and any binding chain that can lead to cluster-admin-like behavior.