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Defending Kubernetes

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trilwu
defending-kubernetes

Harden and monitor a Kubernetes cluster against the attacks that actually happen — RBAC least privilege and escalation paths, Pod Security Admission enforcement, network policy default-deny, secrets and service-account token exposure, control-plane and kubelet exposure, and audit-log-based detection. Use when reviewing a cluster's security posture, responding to a suspected cluster compromise, deciding what to enforce and detect, or translating an attack path from attacking-eks-gke-aks into a defense.

Overview

Publishertrilwu
Repositorysecskills
Skill namedefending-kubernetes
Stars
144
Forks
15
Bundled files
Instructions only
LicenseMIT
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 trilwu on GitHub. Read the source before you install it.

Installation

Install the Defending Kubernetes 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.
  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/trilwu/secskills.git /tmp/secskills
mkdir -p .claude/skills
cp -r /tmp/secskills/secskills-defense/skills/defending-kubernetes .claude/skills/defending-kubernetes
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Defending Kubernetes 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 Defending Kubernetes 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 Defending Kubernetes 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.

Defending Kubernetes

Kubernetes is insecure in useful defaults, not in exotic bugs. The attacks that land are RBAC that grants more than intended, pods that run privileged because nothing stops them, a flat pod network, and mounted service-account tokens with cluster-wide reach. Defense is mostly closing those, in priority order, and being able to see when someone tries.

This is the counterpart to attacking-eks-gke-aks and exploiting-containers: read those to know what the attacker does; use this to know what to enforce and what to watch.

When to Use

  • Reviewing a cluster's security posture or an admission/RBAC configuration
  • Responding to a suspected cluster compromise (post-triage)
  • Deciding what to enforce (Pod Security, network policy) and what to detect
  • Translating an offensive cluster finding into a concrete control
  • Hardening the control plane, kubelet, or etcd exposure

When NOT to Use

  • Attacking the cluster — use attacking-eks-gke-aks
  • Container escape and runtime internals specifically — use exploiting-containers / escaping-hardened-containers for the technique; return here for the control
  • The cloud IAM plane around the cluster (IRSA, workload identity, node role) — that is investigating-*-incidents / hardening-cloud-posture; a GKE/EKS/AKS incident usually needs both planes
  • Whether an alert is an incident — use triaging-security-alerts

Enforce in Priority Order

Order matters — these are ranked by how often the gap is the actual entry path.

1. RBAC least privilege

The most common real weakness. Look for the bindings that are escalation primitives regardless of how innocent they look:

bash
# Who can create pods anywhere? (→ mount any secret, run as any SA)
kubectl auth can-i create pods --all-namespaces --as=system:serviceaccount:ns:sa

# Subjects bound to cluster-admin
kubectl get clusterrolebindings -o json | \
  jq '.items[] | select(.roleRef.name=="cluster-admin") | .subjects'

The dangerous verbs are not just *. create pods lets a subject run a pod as any service account in the namespace and mount any secret — effectively namespace-admin. escalate and bind on roles let a subject grant themselves more than they hold. create on pods/exec, and access to secrets, serviceaccounts/token, and nodes/proxy are each escalation paths. Enumerate what subjects can do, not what their role is named.

2. Pod Security Admission

PodSecurityPolicy was removed in Kubernetes 1.25; the built-in replacement is Pod Security Admission, which enforces the three Pod Security Standards levels — privileged, baseline, restricted — per namespace via labels:

yaml
# Namespace label: enforce the restricted profile, and warn/audit on violations
pod-security.kubernetes.io/enforce: restricted
pod-security.kubernetes.io/warn: restricted
pod-security.kubernetes.io/audit: restricted

restricted blocks the pod configurations that make escape and privilege escalation easy: privileged containers, host namespaces (hostPID, hostNetwork, hostIPC), host-path mounts, running as root, added capabilities. A cluster with no enforced profile is one securityContext.privileged: true away from a node takeover. If you need policy beyond the three levels (image provenance, registry allow-lists), that is an external admission controller (Kyverno, OPA Gatekeeper) — note it, do not pretend PSA covers it.

3. Network policy default-deny

By default every pod can reach every other pod. A default-deny ingress policy per namespace, with explicit allows, is what stops a single compromised pod from becoming lateral movement. Confirm the CNI actually enforces NetworkPolicy — some configurations accept the objects and enforce nothing, which is worse than none because it looks covered.

4. Service-account tokens and secrets

  • automountServiceAccountToken: false on pods that do not call the API. A mounted token plus a permissive RBAC binding is the standard in-cluster pivot.
  • Kubernetes Secrets are base64, not encrypted, in etcd unless encryption-at-rest is configured. Confirm it is.
  • Look for tokens and cloud credentials passed as env vars — they leak into logs and crash dumps.

5. Control-plane and kubelet exposure

  • The API server should not be internet-facing without authn/authz and, ideally, network restriction. Anonymous auth must be off.
  • The kubelet read-only port (10255) and the authenticated port (10250) must not be reachable from workloads; nodes/proxy RBAC and an exposed 10250 are a direct route to command execution on nodes.
  • etcd must require mutual TLS — etcd access is game-over, it holds every secret.

Detection: Turn on the Audit Log

Most clusters run with no meaningful audit policy, so there is nothing to investigate after the fact. A cluster without an audit policy configured is the Kubernetes version of GCP Data Access logging being off — the activity is simply not recorded.

High-value audit signals:

  • exec, attach, and port-forward into pods — interactive access
  • Secret get/list at scale, especially cluster-wide
  • create/update on clusterrolebindings and rolebindings
  • Pods created with privileged, host namespaces, or host-path mounts
  • Anonymous or system:unauthenticated requests that succeed
  • Service-account token creation via the TokenRequest API

On managed clusters the audit log ships to the cloud logging plane (GKE → Cloud Audit Logs, EKS → CloudWatch, AKS → Azure Monitor), which is where the investigation joins up with the investigating-*-incidents skills.

Rationalizations to Reject

  • "The role isn't named admin, so it's fine." Names are irrelevant. create pods or secrets get in a namespace is namespace-admin in effect. Enumerate capabilities, not titles.
  • "We enforce Pod Security, so containers are contained." Only if the profile is restricted and actually enforced, not merely warn. A baseline or audit-only label stops almost none of the escape paths.
  • "NetworkPolicies are defined, so the network is segmented." Only if the CNI enforces them. Verify enforcement, not the presence of the objects.
  • "Secrets are in etcd, so they're protected." They are base64 unless encryption-at-rest is on. Anyone who can read etcd or get secrets has them.
  • "It's a managed cluster, the provider secures it." The provider secures the control plane it runs; RBAC, Pod Security, network policy, and workload identity are yours. Shared responsibility does not include your bindings.
  • "No alerts fired." Check whether an audit policy exists at all before reading silence as safety.

Reading External Sources

Fetch public advisories, specifications, and vendor reports as Markdown:

bash
curl -sL "https://defuddle.md/<url>"      # scheme in the path is optional

This strips page boilerplate — roughly 78% fewer tokens on a prose page — and returns the full text rather than a summary, so you can grep it and trust a negative result.

Three things it is not for. Fetch JSON and API responses raw, because readability extraction mangles structured data. Fetch authenticated or JavaScript-rendered pages directly, because it retrieves them anonymously. And never route adversary infrastructure (phishing links, C2, malware hosting), client-owned hosts, or engagement URLs through it — the request leaves your machine to a third party, and for live adversary infrastructure it also tips off the operator.

Some sites block the extractor and return an error blob rather than the page — {"error":"Failed to fetch: 418 I'm a teapot"} from freedesktop.org, for instance. That is the fetch being refused, not the source saying the thing does not exist. Re-fetch the URL directly before drawing any conclusion from it.

References

  • attacking-eks-gke-aks — the attack paths these controls close
  • exploiting-containers, escaping-hardened-containers — the escape techniques Pod Security aims to prevent
  • hardening-cloud-posture — the cloud IAM plane around the cluster
  • engineering-detections — turning the audit signals above into rules
  • investigating-aws-incidents / investigating-gcp-incidents — where a managed-cluster audit trail leads

Frequently asked questions

What does the Defending Kubernetes AI skill do?

Harden and monitor a Kubernetes cluster against the attacks that actually happen — RBAC least privilege and escalation paths, Pod Security Admission enforcement, network policy default-deny, secrets and service-account token exposure, control-plane and kubelet exposure, and audit-log-based detection. Use when reviewing a cluster's security posture, responding to a suspected cluster compromise, deciding what to enforce and detect, or translating an attack path from attacking-eks-gke-aks into a defense.

Why use Defending Kubernetes on TypingMind?

Because you install it once and use it with any model. Defending Kubernetes 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 Defending Kubernetes in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trilwu/secskills/tree/main/secskills-defense/skills/defending-kubernetes. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Defending Kubernetes?

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 Defending Kubernetes?

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

Is the Defending Kubernetes AI skill free?

Yes. It is published on GitHub by trilwu under the MIT 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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