GKE Sandbox

This page describes how GKE Sandbox protects the host kernel on your nodes when containers in the Pod execute unknown or untrusted code. For example, multi-tenant clusters such as software-as-a-service (SaaS) providers often execute unknown code submitted by their users. GKE Sandbox is also a useful defense-in-depth measure for running high-value containers.

GKE Sandbox uses gVisor, an open source project. This topic broadly discusses gVisor, but you can learn more details by reading the official gVisor documentation.

For instructions on how to enable and use GKE Sandbox, see Configure GKE Sandbox.


GKE Sandbox provides an extra layer of security to prevent untrusted code from affecting the host kernel on your cluster nodes. Before discussing how GKE Sandbox works, it's useful to understand the nature of the potential risks it helps mitigate.

A container runtime such as containerd provides some degree of isolation between the container's processes and the kernel running on the node. However, the container runtime often runs as a privileged user on the node and has access to most system calls into the host kernel.

Potential threats

Multi-tenant clusters and clusters whose containers run untrusted workloads are more exposed to security vulnerabilities than other clusters. Examples include SaaS providers, web-hosting providers, or other organizations that allow their users to upload and run code. A flaw in the container runtime or in the host kernel could allow a process running within a container to "escape" the container and affect the node's kernel, potentially bringing down the node.

The potential also exists for a malicious tenant to gain access to and exfiltrate another tenant's data in memory or on disk, by exploiting such a defect.

Finally, an untrusted workload could potentially access other Google Cloud services or cluster metadata.

How GKE Sandbox mitigates these threats

gVisor is a userspace re-implementation of the Linux kernel API that does not need elevated privileges. In conjunction with a container runtime such as containerd , the userspace kernel re-implements the majority of system calls and services them on behalf of the host kernel. Direct access to the host kernel is limited. See the gVisor architecture guide for detailed information about how this works. From the container's point of view, gVisor is nearly transparent, and does not require any changes to the containerized application.

When you request GKE Sandbox in a Pod in Autopilot clusters, GKE runs that Pod in a sandbox. In GKE Standard, if you enable GKE Sandbox on nodes, all Pods that run on those nodes run in sandboxes.

Each sandbox uses its own user space kernel. With this in mind, you can make decisions about how to group your containers into Pods, based on the level of isolation you require and the characteristics of your applications.

GKE Sandbox is an especially good fit for the following types of applications. See Limitations for more information to help you decide which applications to sandbox.

  • Untrusted or third-party applications using runtimes such as Rust, Java, Python, PHP, Node.js, or Golang
  • Web server front-ends, caches, or proxies
  • Applications processing external media or data using CPUs
  • Machine-learning workloads using CPUs
  • CPU-intensive or memory-intensive applications
  • GPU-intensive workloads

Additional security recommendations

When using GKE Sandbox, we recommend that you also follow these recommendations:

  • Specify resource limits on all containers running in a sandbox. This protects against the risk of a defective or malicious application starving the node of resources and negatively impacting other applications or system processes running on the node.

  • If you are using Workload Identity Federation for GKE, block cluster metadata access using Network Policy to block access to This protects against the risk of a malicious application accessing information to potentially private data like project ID, node name and zone. Workload Identity Federation for GKE is always enabled in GKE Autopilot clusters.


GKE Sandbox works well with many applications, but not all. This section provides more information about the current limitations of GKE Sandbox.

GPUs in GKE Sandbox

In GKE version 1.29.2-gke.1108000 and later, GKE Sandbox supports the use of NVIDIA GPUs.

GKE Sandbox doesn't mitigate all NVIDIA driver vulnerabilities, but retains protection against Linux kernel vulnerabilities. For details about how the gVisor project protects GPU workloads, see GPU Support Guide

The following limitations apply to GPU workloads within GKE Sandbox:

  • Only the latest NVIDIA driver version on a given GKE version is supported. Autopilot clusters automatically install NVIDIA driver versions that support GKE Sandbox.
  • Only CUDA workloads are supported.
  • The following GPU models are supported: nvidia-tesla-t4, nvidia-tesla-a100, nvidia-a100-80gb, nvidia-l4, and nvidia-h100-80gb.

You can use GKE Sandbox with GPU workloads at no additional cost.

Node pool configuration

Applies to Standard clusters

  • You cannot use GKE Sandbox on Windows Server node pools.
  • You cannot enable GKE Sandbox on the default node pool to separate system services running in the default node pool from untrusted workloads using GKE Sandbox.
  • When using GKE Sandbox, your cluster must have at least two node pools. You must always have at least one node pool where GKE Sandbox is disabled. This node pool must contain at least one node, even if all your workloads are sandboxed.
  • GKE versions earlier than 1.24.2-gke.300 don't support the e2-micro, e2-small, and e2-medium machine types. GKE version 1.24.2-gke.300 and later support these machine types.
  • Nodes must use the Container-Optimized OS with containerd (cos_containerd) node image.

Access to cluster metadata

Applies to Autopilot and Standard clusters

  • Nodes running sandboxed Pods are prevented from accessing cluster metadata at the level of the operating system on the node.
  • In GKE Standard, you can run regular Pods on a node with GKE Sandbox enabled. However, by default those regular Pods cannot access Google Cloud services or cluster metadata.
  • Use Workload Identity Federation for GKE to grant Pods access to Google Cloud services.

SMT may be disabled

Applies to Autopilot and Standard clusters

Simultaneous multithreading (SMT) settings are used to mitigate side channel vulnerabilities that take advantage of threads sharing core state, such as Microarchitectural Data Sampling (MDS) vulnerabilities.

In GKE versions 1.25.5-gke.2500 or later and 1.26.0-gke.2500 or later, gVisor is configured to use Linux Core Scheduling to mitigate side channel attacks. SMT settings are unchanged from default. Core Scheduling is used only for workloads running with gVisor.

Starting in GKE version 1.24.2-gke.300, SMT is configured by machine type based on how vulnerable the machine is to MDS, as follows:

Prior to version 1.24.2-gke.300, SMT is disabled on all machine types.

Enable SMT

Applies to Standard clusters

In GKE Standard clusters, you can enable SMT if it's disabled on your selected machine type. You're charged for every vCPU, regardless of whether you turn SMT on or keep it turned off. For pricing information, refer to the Compute Engine pricing.

GKE version 1.24.2-gke.300 and later

Set the --threads-per-core flag when creating a GKE Sandbox node pool:

gcloud container node-pools create smt-enabled \
  --cluster=CLUSTER_NAME \
  --machine-type=MACHINE_TYPE \
  --threads-per-core=2 \
  --sandbox type=gvisor
  • CLUSTER_NAME: the name of the existing cluster.
  • MACHINE_TYPE: the machine type.

For more information about --threads-per-core, refer to Set the number of threads per core.

GKE versions before 1.24.2-gke.300

  1. Create a new node pool in your cluster with the node label cloud.google.com/gke-smt-disabled=false:

    gcloud container node-pools create smt-enabled \
        --cluster=CLUSTER_NAME \
        --machine-type=MACHINE_TYPE \
        --node-labels=cloud.google.com/gke-smt-disabled=false \
        --image-type=cos_containerd \
        --sandbox type=gvisor
  2. Deploy the DaemonSet to the node pool. The DaemonSet will only run on nodes with the cloud.google.com/gke-smt-disabled=false label.

    kubectl create -f \
  3. Ensure that the DaemonSet pods are in the running state.

    kubectl get pods --selector=name=enable-smt -n kube-system

    The output is similar to the following:

    NAME               READY     STATUS    RESTARTS   AGE
    enable-smt-2xnnc   1/1       Running   0          6m
  4. Check that SMT has been enabled appears in the logs of the pods.

    kubectl logs enable-smt-2xnnc enable-smt -n kube-system


Applies to Standard clusters

By default, the container is prevented from opening raw sockets, to reduce the potential for malicious attacks. Certain network-related tools such as ping and tcpdump create raw sockets as part of their core functionality. To enable raw sockets, you must explicitly add the NET_RAW capability to the container's security context:

  - name: my-container
        add: ["NET_RAW"]

If you use GKE Autopilot, Google Cloud prevents you from adding the NET_RAW permission to containers because of the security implications of this capability.

External dependencies

Applies to Autopilot and Standard clusters

Untrusted code running inside the sandbox may be allowed to reach external services such as database servers, APIs, other containers, and CSI drivers. These services are running outside the sandbox boundary and need to be individually protected. An attacker can try to exploit vulnerabilities in these services to break out of the sandbox. You must consider the risk and impact of these services being reachable by the code running inside the sandbox, and apply the necessary measures to secure them.

This includes file system implementations for container volumes such as ext4 and CSI drivers. CSI drivers run outside the sandbox isolation and may have privileged access to the host and services. An exploit in these drivers can affect the host kernel and compromise the entire node. We recommend that you run the CSI driver inside a container with the least amount of permissions required, to reduce the exposure in case of an exploit. GKE Sandbox supports using the Compute Engine Persistent Disk CSI driver.

Incompatible features

You can't use GKE Sandbox with the following Kubernetes features:

Workload characteristics

Applies to Autopilot and Standard clusters

Imposing an additional layer of indirection for accessing the node's kernel comes with performance trade-offs. GKE Sandbox provides the most tangible benefit on large multi-tenant clusters where isolation is important. Keep the following guidelines in mind when testing your workloads with GKE Sandbox.

System calls

Applies to Autopilot and Standard clusters

Workloads that generate a large volume of low-overhead system calls, such as a large number of small I/O operations, may require more system resources when running in a sandbox, so you may need to use more powerful nodes or add additional nodes to your cluster.

Direct access to hardware or virtualization

Applies to Autopilot and Standard clusters

If your workload needs any of the following, GKE Sandbox might not be a good fit because it prevents direct access to the host kernel on the node:

  • Direct access to the node's hardware
  • Kernel-level virtualization features
  • Privileged containers

What's next