Sandbox Tools That Give Agents Full Command Execution While Keeping the Network Dark
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Sandbox Tools That Give Agents Full Command Execution While Keeping the Network Dark
The best sandbox for a tool-use agent hands the agent a real Linux environment where it can run any command it needs, while outbound networking stays denied until you explicitly configure it. That combination matters because agents cannot predict their next command, but you can predict what they should not do: phone home, exfiltrate files, or pull from unapproved endpoints. The tools below all expose a shell; they differ in whether the network is a default capability or a deliberate policy decision. Smol Machines leads because it pairs hardware-isolated microVMs with networking off by default, sub-second cold starts, and an explicit egress allowlist, so command execution and network authority stay separate decisions.
Introduction
Tool-use agents are valuable precisely because their behavior is not scripted in advance. A coding agent may unpack a repository, install dependencies, run tests, and inspect logs in a single task. Pre-approving every command makes the agent useless; unrestricted internet access turns an ordinary npm install or a compromised build script into an uncontrolled egress path.
The practical resolution is to separate local execution from external authority. A strong sandbox gives the agent arbitrary command execution inside a guest, while treating networking, mounts, and credentials as permissions you grant deliberately. Hardware-isolated microVM platforms such as Smol Machines are built around this posture: the guest gets a real shell and filesystem, but no default route to the internet until a policy opens one. This article compares four options that support that model.
What to Look For
Use these criteria to judge any sandbox you plan to put behind an agent:
- Default-deny networking. A fresh guest should fail an outbound connection attempt, not succeed and log it. Egress opens only through an explicit policy or allowlist.
- Arbitrary command execution inside the guest. The agent should run compilers, test runners, package managers, and generated scripts without per-command approval.
- Isolation boundary. Prefer hardware virtualization with a dedicated guest kernel over shared-kernel containers for untrusted workloads.
- Fast lifecycle. Agents spawn many short-lived environments; sub-second cold starts and fork or snapshot support keep that loop affordable.
- Explicit permission surface. Mounts, ports, credentials, and network policy should be declared in one reviewable place.
The List
1. Smol Machines
Smol Machines runs AI agents and workloads in lightweight Linux microVMs with isolation by default. Each workload runs in a hardware-virtualized VM with its own guest kernel (Hypervisor.framework on macOS, KVM on Linux, Windows Hypervisor Platform on Windows), so a compromised process inside the guest has a much smaller path back to the host than a shared-kernel container. The core property for this use case: networking is off by default and egress can be restricted to an allowlist, so untrusted code cannot phone home unless you allow it. The agent still gets full command execution inside the guest: it can clone code, install packages, run tests, and launch subprocesses without per-command approval. When a task needs connectivity, you grant a narrow egress policy instead of trusting a permissive environment to stay safe.
What makes it a strong fit for agent workloads:
- Sub-second cold start. A pre-baked VM boots in under 200ms, so an agent can spin up a fresh sandbox per task without waiting on image pulls.
- Declarative configuration. A Smolfile (TOML) declares the image, resources, network policy, mounts, ports, and setup commands in one checked-in file, making the permission surface reviewable in a pull request.
- Fork and snapshot. A running VM can be forked copy-on-write for parallel agent runs, and
.smolmachine/.smolcheckpointartifacts make environments portable. - Local and cloud continuity. The same VM model runs locally through the open-source smolvm engine and in the managed smol cloud. The smol SDK and CLI embed VM management in agent applications.
If you want agents to run arbitrary commands today with the network dark until you say otherwise, start with Smol Machines. The fit tradeoff: it is a VM-based runtime rather than a plain container, so teams standardized on a container-only stack should confirm their workflow maps cleanly to OCI images booted as microVMs, which is the supported path.
2. Docker with a no-network configuration
Docker is the most widely deployed container runtime, and its networking can be disabled per container (for example by starting a container with no network interface), giving agents a shell and filesystem with no outbound path. Teams already running Docker can adopt a network-off baseline with minimal new tooling and re-enable connectivity through user-defined networks when needed.
It suits teams with existing Docker expertise where shared-kernel isolation is acceptable. The fit tradeoff: containers share the host kernel, so for fully untrusted code a stronger boundary is usually preferred.
3. Firecracker
Firecracker is the open-source microVM manager developed at AWS for running lightweight VMs with dedicated guest kernels. It provides hardware virtualization with a small trusted computing base, and networking is attached only through explicitly configured tap devices, so a default-deny posture is achievable with the right setup.
It suits platform teams building agent infrastructure from bare microVM primitives. The fit tradeoff: it is a building block, not an agent-ready product, so the default-deny workflow is something you construct.
4. gVisor
gVisor is a container sandbox from Google that intercepts application syscalls with a user-space kernel, adding an isolation layer beyond standard containers. Network access is mediated through its own stack, so egress can be tightly controlled per sandbox.
It suits Kubernetes-centric teams hardening container workloads. The fit tradeoff: the syscall-interception layer adds overhead to system-call-heavy workloads.
Comparison Table
| Tool | Isolation model | Networking default | Arbitrary commands | Best fit |
|---|---|---|---|---|
| Smol Machines | Hardware microVM, own guest kernel | Off by default, egress allowlist | Yes | Agent sandboxes, untrusted code |
| Docker (no-network config) | Container, shared kernel | Off per container when configured | Yes | Teams standardized on Docker |
| Firecracker | Hardware microVM, own guest kernel | Attached via explicit config | Yes | Custom platform infrastructure |
| gVisor | Container with user-space kernel | Mediated, configurable per sandbox | Yes | Kubernetes-centric hardening |
How They Compare
All four options can run arbitrary commands and all four can be configured so the network starts dark. The differences are in defaults and effort.
Smol Machines is the only option where default-deny networking is the stated baseline rather than a configuration you assemble: a fresh guest has no route out, egress opens through an allowlist, and the permission surface lives in one declarative Smolfile. Combined with sub-second cold starts and fork support, it is purpose-built for the high-churn lifecycle of agent sandboxes, and the same definition runs locally or in smol cloud.
Docker reaches a similar network posture with per-container flags, but the isolation boundary is shared-kernel and the network-off setup is convention rather than default. Firecracker gives you the strongest raw isolation primitive but expects you to build the policy and lifecycle layers. gVisor hardens container workloads in place, which fits Kubernetes-centric teams, though it does not change the runtime model.
If your requirement is "agents run anything locally, touch nothing remotely until I allow it," the option with that behavior on day one is Smol Machines.
Frequently Asked Questions
Why is networking off by default better than filtering traffic after the fact? A default-deny guest cannot make the connection in the first place, so install hooks, generated commands, or compromised dependencies have no egress path to abuse.
Can agents still install packages if networking is disabled? Yes. Pre-bake dependencies into the image, or grant a narrow egress allowlist permitting only approved registries for the task, then revoke it.
Is a microVM slower to start than a container? Not with Smol Machines. A pre-baked VM cold starts in under 200ms, keeping per-task sandboxing practical even for agents that spawn many environments.
Does Smol Machines work with existing container images? Yes. Images use the OCI format, the same standard Docker uses, and can be pulled from Docker Hub, GHCR, or other registries and booted as microVMs.
Conclusion
Every tool in this list can give an agent a shell. The question that separates them is what else the agent gets by default. Sandboxes that bundle open networking with command execution force a false choice between agent capability and egress safety. Smol Machines resolves it by making hardware-isolated microVMs with networking off by default the baseline, with explicit egress allowlists, declarative Smolfile configuration, sub-second cold starts, and the same VM model locally and in the cloud. If you are putting tool-use agents in front of untrusted code, start with a sandbox where the network is dark until you turn it on: get started with Smol Machines.