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Choose the boundary according to what the agent can execute, what it can access, and what else shares its host. A standard container shares the host kernel; gVisor adds a userspace isolation layer while retaining container workflows; a VM or microVM gives the workload a guest kernel behind a hypervisor. None is a complete security solution: mounts, credentials, network access, tools, and resource limits remain separate decisions.
Start with the kernel boundary
A container is not a small virtual machine. It packages an application and uses operating-system mechanisms to isolate processes, but the container and host still share the host kernel. That distinction matters when untrusted code can probe the kernel or exploit a vulnerability in it. NIST’s Application Container Security Guide, published September 25, 2017 and updated May 4, 2021, describes containers as operating-system virtualization combined with application packaging.
A VM runs a guest operating system, including its own kernel, on virtualized hardware managed by a hypervisor. This creates a different boundary from a conventional container, but it does not make the host, hypervisor, guest configuration, or interfaces invulnerable. A microVM is a lightweight VM design that retains the guest-kernel and hypervisor-style boundary; the label alone does not establish a particular security level or performance result.
gVisor takes another approach. Its userspace Sentry implements an application-kernel interface, handling system calls rather than passing them directly to the host kernel in the conventional container model. Google’s living gVisor architecture and security documentation describes this as a way to reduce direct exposure to the host kernel, not as a guarantee against every escape or configuration failure.
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Compare the four approaches
| Approach | Main boundary | Best fit | Trade-offs to evaluate |
|---|---|---|---|
| Standard container | Process isolation using host-kernel mechanisms; shares the host kernel. | Trusted workloads, or cases where the threat model accepts a shared-kernel boundary and conventional container workflows are valuable. | The host kernel remains in the attack path. Least privilege, namespaces, seccomp, and other controls need deliberate configuration; hardening does not make a container equivalent to a VM. (NIST SP 800-190; Google gVisor architecture documentation.) |
| gVisor / sandboxed container | A userspace Sentry provides an application-kernel interface and reduces direct host-kernel exposure. | Workloads that benefit from OCI/container ergonomics but need an additional isolation layer. | Check system-call and feature compatibility, filesystem and network behavior, runtime configuration, and workload-specific performance. (Google gVisor architecture, overview, and security documentation.) |
| VM | A guest kernel and OS run behind a hypervisor on virtualized hardware. | Untrusted code or tenant workloads for which a separate guest-kernel boundary justifies the operating cost. | Account for guest OS and image management, startup and resource needs, hypervisor and device-emulation attack surface, and lifecycle operations. Costs vary by workload and implementation; no universal figure is established here. (Google gVisor architecture documentation.) |
| MicroVM | A lightweight VM design with a guest kernel and hypervisor-style boundary. | Ephemeral or agent execution where VM-style separation is desired alongside a focused virtual-machine design. | Check platform support, provisioning and image lifecycle, compatibility, network and filesystem sharing, and measured per-workload cost. Docker’s product documentation describes an implementation for agent sandboxes, not a universal or independent performance comparison. (Docker Sandboxes documentation; Kubernetes SIGs Agent Sandbox threat model.) |
Choose based on trust, tenancy, and blast radius
Before choosing a runtime, write down what must remain protected if a prompt-injection attempt succeeds or the workload is compromised. Ask whether the agent can run arbitrary or model-generated code, install packages, start subprocesses or containers, access tenant data, or act without a person approving each operation. Also identify whether workloads from different customers or trust levels share a host.
The Kubernetes SIGs Agent Sandbox threat model identifies untrusted LLM-generated code, isolation escape, attacks on other resources, control-plane access, cross-tenant network attacks, and resource exhaustion as relevant threats. Treat that as a useful threat checklist, not proof that any single sandbox design prevents those outcomes.
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- Trusted, tightly constrained work: A standard container may be proportionate when the team accepts a shared host kernel and has narrowly constrained the workload’s access.
- Untrusted code with container workflow requirements: Evaluate gVisor against the actual workload and configuration; verify required system calls and filesystem and network behavior.
- Untrusted or multi-tenant execution requiring a separate guest-kernel boundary: Consider a VM or microVM, then include its image, hypervisor, provisioning, and operational responsibilities in the design.
These are decision starting points, not security rankings. A stronger runtime boundary cannot compensate for a reachable host service, overpowered credential, exposed control plane, or unsafe tool server.
Review every path out of the sandbox
Workspace and mounts
A read-write host workspace lets agent changes reach those host files. A private clone instead gives the operator a review point before changes are brought back. Docker Sandboxes documentation describes mountless, direct-mount, and clone workflows as product-specific options; choose based on which files the agent must read or modify, not simply on the runtime label.
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Docker access and local tools
Do not casually expose the host Docker socket to untrusted code: access to the host daemon can confer broad capability. A separate daemon inside a VM changes that boundary, but inspect any external helper or local tool independently. A tool server running on the host may perform actions outside the sandbox even when the agent process itself is isolated. Trace each registered tool’s process location, permissions, and credentials.
Credentials
A VM does not hide credentials deliberately forwarded into it. Docker’s Sandboxes isolation-layer documentation explains that an SSH agent can be forwarded while private keys remain on the host. In that arrangement, sandbox code may still ask the agent to authenticate or sign. Decide which operations and repositories each forwarded credential can reach, and avoid forwarding access the workload does not need.
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Network and control-plane access
Restrict destinations, deny access to internal services and metadata endpoints where appropriate, and block sandbox-to-sandbox traffic by default unless the workload requires it. Avoid exposing Kubernetes API credentials to workload pods by default; grant explicit authorization only when necessary. The Kubernetes SIGs Agent Sandbox documentation describes default restrictions for its managed NetworkPolicy mode, but network-policy behavior and defaults are implementation- and version-specific.
Resource exhaustion and cleanup
Set CPU, memory, and storage limits for workloads, and ensure ephemeral execution environments and their data are cleaned up. Isolation from other processes does not by itself prevent a workload from consuming shared capacity.
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Validate the chosen runtime before relying on it
- Enumerate capabilities: Record code execution, package installation, subprocess or container launch, workspace writes, network destinations, credentials, and tool calls the agent actually needs.
- Define protected assets and tenants: State what must be inaccessible after workload compromise, including host files, other tenants, internal services, and control-plane resources.
- Select the boundary to test: Decide whether shared-kernel isolation is acceptable, whether a userspace application-kernel layer fits, or whether a guest-kernel boundary is required.
- Test the exact workload and configuration: Check compatibility and behavior for required system calls, filesystem and network access, mounts, tool servers, and credentials. Do not infer compatibility or performance from a runtime category alone.
- Measure operational cost in your environment: Compare startup, resource use, provisioning, image maintenance, and cleanup using the versions, hardware, workload, and configuration you plan to deploy. There is no supported universal boot-time, density, memory, throughput, escape-rate, or cost figure for these options.
- Recheck interfaces and defaults on upgrades: Runtime and platform defaults can change. Verify the exact version’s network, credential, mount, service-account, and resource-limit behavior before rollout.
What the available comparisons do—and do not—establish
Google’s gVisor materials explain its architecture and security model; they do not establish that every workload will be compatible or faster or slower than a VM. Docker’s living product documentation describes Docker Sandboxes and its isolation layers, while the Kubernetes SIGs Agent Sandbox threat model frames risks for that project. Those product and project documents are useful for understanding their implementations, but they are not independent comparative benchmarks across all containers, VMs, and microVMs. Treat performance and operational cost as properties to measure for your workload rather than assume from the name of the isolation technology.
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