This page shows you how to reduce the risk of privilege escalation attacks in your cluster by telling Google Kubernetes Engine (GKE) to schedule your workloads on a separate, dedicated node pool away from privileged GKE-managed workloads. This page applies to Standard clusters without node auto-provisioning. To separate workloads in Autopilot clusters and in Standard clusters with node auto-provisioning enabled, refer to Configure workload separation in GKE.
GKE clusters use privileged GKE-managed workloads to enable specific cluster functionality and features, such as metrics gathering. These workloads are given special permissions to run correctly in the cluster.
Workloads that you deploy to your nodes might have the potential to be compromised by a malicious entity. Running these workloads alongside privileged GKE-managed workloads means that an attacker who breaks out of a compromised container can use the credentials of the privileged workload on the node to escalate privileges in your cluster.
Preventing container breakouts
Your primary defense should be your applications. GKE has multiple features that you can use to harden your clusters and Pods. In most cases, we strongly recommend using GKE Sandbox to isolate your workloads. GKE Sandbox is based on the gVisor open source project, and implements the Linux kernel API in the userspace. Each Pod runs on a dedicated kernel that sandboxes applications to prevent access to privileged system calls in the host kernel. Workloads running in GKE Sandbox are automatically scheduled on separate nodes, isolated from other workloads.
You should also follow the recommendations in Harden your cluster's security.
Avoiding privilege escalation attacks
If you can't use GKE Sandbox, and you want an extra layer of isolation in addition to other hardening measures, you can use node taints and node affinity to schedule your workloads on a dedicated node pool. A node taint tells GKE to avoid scheduling workloads without a corresponding toleration (such as GKE-managed workloads) on those nodes. The node affinity on your own workloads tells GKE to schedule your Pods on the dedicated nodes.
Limitations of node isolation
- Attackers can still initiate Denial-of-Service (DoS) attacks from the compromised node.
- Compromised nodes can still read many resources, including all Pods and namespaces in the cluster.
- Compromised nodes can access Secrets and credentials used by every Pod running on that node.
- Using a separate node pool to isolate your workloads can impact your cost efficiency, autoscaling, and resource utilization.
- Compromised nodes can still bypass egress network policies.
- Some GKE-managed workloads must run on every node in your cluster, and are configured to tolerate all taints.
- If you deploy DaemonSets that have elevated permissions and can tolerate any taint, those Pods may be a pathway for privilege escalation from a compromised node.
How node isolation works
To implement node isolation for your workloads, you must do the following:
- Taint and label a node pool for your workloads.
- Update your workloads with the corresponding toleration and node affinity rule.
This guide assumes that you start with one node pool in your cluster. Using node affinity in addition to node taints isn't mandatory, but we recommend it because you benefit from greater control over scheduling.
Before you begin
Before you start, make sure you have performed the following tasks:
- Enable the Google Kubernetes Engine API. Enable Google Kubernetes Engine API
- If you want to use the Google Cloud CLI for this task, install and then initialize the gcloud CLI.
- Choose a specific name for the node taint and the node label that you want
to use for the dedicated node pools. For this example, we use
Taint and label a node pool for your workloads
Create a new node pool for your workloads and apply a node taint and a node label. When you apply a taint or a label at the node pool level, any new nodes, such as those created by autoscaling, will automatically get the specified taints and labels.
You can also add node taints and node labels to existing node pools. If you use
NoExecute effect, GKE evicts any Pods running on those
nodes that don't have a toleration for the new taint.
To add a taint and a label to a new node pool, run the following command:
gcloud container node-pools create POOL_NAME \ --cluster CLUSTER_NAME \ --node-taints KEY=VALUE:EFFECT \ --node-labels NODE_LABELS
Replace the following:
POOL_NAME: the name of the new node pool for your workloads.
CLUSTER_NAME: the name of your GKE cluster.
KEY=VALUE: a key value pair associated with a scheduling
EFFECT. For example,
EFFECT: one of the following effect values:
NoExecuteprovides a better eviction guarantee than
Add a toleration and a node affinity rule to your workloads
After you taint the dedicated node pool, no workloads can schedule on it unless they have a toleration corresponding to the taint you added. Add the toleration to the specification for your workloads to let those Pods schedule on your tainted node pool.
If you labelled the dedicated node pool, you can also add a node affinity rule to tell GKE to only schedule your workloads on that node pool.
The following example adds a toleration for the
workloadType=untrusted:NoExecute taint and a node affinity rule for the
workloadType=untrusted node label.
kind: Deployment apiVersion: apps/v1 metadata: name: my-app namespace: default labels: app: my-app spec: replicas: 1 selector: matchLabels: app: my-app template: metadata: labels: app: my-app spec: tolerations: - key: workloadType operator: Equal value: untrusted affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: workloadType operator: In values: - "untrusted" containers: - name: sleep image: ubuntu command: ["/bin/sleep", "inf"]
When you update your Deployment with
kubectl apply, GKE
recreates the affected Pods. The node affinity rule forces the Pods onto the
dedicated node pool that you created. The toleration allows only those Pods to be
placed on the nodes.
Verify that the separation works
To verify that the scheduling works correctly, run the following command and check whether your workloads are on the dedicated node pool:
kubectl get pods -o=wide
Recommendations and best practices
After setting up node isolation, we recommend that you do the following:
- Restrict specific node pools to GKE-managed workloads only by
components.gke.io/gke-managed-componentstaint. Adding this taint prevents your own Pods from scheduling on those nodes, improving the isolation.
- When creating new node pools, prevent most GKE-managed workloads from running on those nodes by adding your own taint to those node pools.
- Whenever you deploy new workloads to your cluster, such as when installing third-party tooling, audit the permissions that the Pods require. When possible, avoid deploying workloads that use elevated permissions to shared nodes.
- Learn more about GKE Sandbox.
- Learn about GKE Autopilot, which implements many GKE security features by default.