Pod Access Control logo

Pod Access Control

Organization
RedHatProductSecurity
pod-access-control

Configure Kubernetes RBAC bindings, service accounts, namespaces, resource quotas, and service types for pods. Use when writing, reviewing, or auditing Deployments, ServiceAccounts, Roles, RoleBindings, ResourceQuotas, or Services for least-privilege access control.

Overview

PublisherRedHatProductSecurity
Repositoryprodsec-skills
Skill namepod-access-control
Stars
56
Forks
9
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Pod Access Control 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/RedHatProductSecurity/prodsec-skills/tree/main/ge-core/pod-access-control
  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/RedHatProductSecurity/prodsec-skills.git /tmp/prodsec-skills
mkdir -p .claude/skills
cp -r /tmp/prodsec-skills/ge-core/pod-access-control .claude/skills/pod-access-control
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pod Access Control 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 Pod Access Control 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 Pod Access Control 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.

Pod Access Control

Configure least-privilege access control for Kubernetes workloads by scoping RBAC bindings, isolating namespaces, enforcing resource quotas, and restricting service exposure.

Service Accounts

Every workload pod must use a dedicated, named service account — not the default service account. Default service accounts often carry excessive privileges and make it impossible to apply fine-grained RBAC per workload.

Required for: all profiles (mandatory)

yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: app-sa
  namespace: app-ns
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: app
  namespace: app-ns
spec:
  template:
    spec:
      serviceAccountName: app-sa

RBAC Bindings

No ClusterRoleBindings

Workload pods must not use ClusterRoleBindings. Cluster-wide role bindings grant excessive privileges that enable lateral movement across the entire cluster. Use namespace-scoped RoleBindings instead.

Required for: Telco (mandatory), Far-Edge (mandatory), Extended (mandatory), Non-Telco (optional)

yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: app-role-binding
  namespace: app-ns
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: app-role
subjects:
  - kind: ServiceAccount
    name: app-sa
    namespace: app-ns

No Cross-Namespace RoleBindings

Workload RoleBindings must only exist in the workload's own namespace. Cross-namespace role bindings violate tenant isolation and create unintended privilege escalation paths.

Required for: all profiles (mandatory)

CRD-Scoped Roles

If a workload creates Custom Resource Definitions, it must supply a Role that only grants access to those CRDs — not to other API resources. This enforces least-privilege for custom resource access.

Required for: Extended (mandatory), all others (optional)

yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: app-crd-role
  namespace: app-ns
rules:
  - apiGroups: ["app.example.com"]
    resources: ["myresources"]
    verbs: ["get", "list", "watch", "create", "update", "delete"]

Namespace Management

Workload resources must be deployed to declared namespaces. Do not use:

  • default
  • Any namespace prefixed with openshift-
  • Any namespace prefixed with istio- or aspenmesh-

These namespaces are reserved for platform components and service mesh infrastructure.

Required for: all profiles (optional, recommended best practice)

Resource Quotas

Workload namespaces must have a ResourceQuota applied to prevent unbounded resource consumption. Without quotas, a single workload can starve other applications of CPU and memory.

Required for: Extended (mandatory), all others (optional)

yaml
apiVersion: v1
kind: ResourceQuota
metadata:
  name: app-quota
  namespace: app-ns
spec:
  hard:
    requests.cpu: "4"
    requests.memory: 8Gi
    limits.cpu: "8"
    limits.memory: 16Gi
    pods: "20"

Resource Requests

All containers must specify CPU and memory resource requests. Requests enable the Kubernetes scheduler to make informed placement decisions and prevent resource contention.

Required for: Telco (mandatory), Far-Edge (mandatory), Extended (mandatory), Non-Telco (optional)

yaml
containers:
  - name: app
    image: registry.example.com/app:v1.2.3
    resources:
      requests:
        cpu: 100m
        memory: 128Mi
      limits:
        cpu: 200m
        memory: 256Mi

Service Types

Services must not use NodePort. NodePort services expose applications directly on host ports, creating security risks and potential port conflicts with host services. Use ClusterIP (default) or LoadBalancer instead.

Required for: all profiles (mandatory)

yaml
apiVersion: v1
kind: Service
metadata:
  name: app-svc
  namespace: app-ns
spec:
  type: ClusterIP
  selector:
    app: app
  ports:
    - port: 8080
      targetPort: 8080

Implementation Checklist

  • Each pod uses a dedicated, named service account (not default)
  • No ClusterRoleBindings are used by workload pods
  • RoleBindings exist only in the workload's own namespace
  • CRD roles only grant access to CRDs, not other API resources
  • Workloads are deployed to declared namespaces (not default, openshift-*, istio-*)
  • Workload namespaces have ResourceQuota applied
  • All containers specify CPU and memory resource requests
  • Services do not use NodePort

Certsuite Test Mapping

GuidanceCertsuite Test IDProfiles
Dedicated service accountaccess-control-pod-service-accountAll profiles: mandatory
No ClusterRoleBindingsaccess-control-cluster-role-bindingsTelco/Far-Edge/Extended: mandatory, Non-Telco: optional
No cross-namespace RoleBindingsaccess-control-pod-role-bindingsAll profiles: mandatory
CRD-scoped rolesaccess-control-crd-rolesExtended: mandatory, all others: optional
Declared namespacesaccess-control-namespaceAll profiles: optional (recommended)
Namespace resource quotaaccess-control-namespace-resource-quotaExtended: mandatory, all others: optional
Resource requestsaccess-control-requestsTelco/Far-Edge/Extended: mandatory, Non-Telco: optional
No NodePort servicesaccess-control-service-typeAll profiles: mandatory

References

Frequently asked questions

What does the Pod Access Control AI skill do?

Configure Kubernetes RBAC bindings, service accounts, namespaces, resource quotas, and service types for pods. Use when writing, reviewing, or auditing Deployments, ServiceAccounts, Roles, RoleBindings, ResourceQuotas, or Services for least-privilege access control.

Why use Pod Access Control on TypingMind?

Because you install it once and use it with any model. Pod Access Control 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 Pod Access Control in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/ge-core/pod-access-control. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pod Access Control?

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 Pod Access Control?

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

Is the Pod Access Control AI skill free?

Yes. It is published on GitHub by RedHatProductSecurity 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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