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Choose an isolation platform by defining what an evaluated AI agent may execute, which files and credentials it can reach, and which network destinations it needs. Then compare the actual execution boundary, separation between runs and users, network rules, secret handling, workspace persistence, cleanup responsibility, and operating model. A local process, a container, a hosted sandbox, and a self-hosted environment offer different combinations of convenience and control; none is safe merely because it is called a “sandbox.”
What does an isolation platform need to protect?
An isolation platform is the environment in which an agent’s code, commands, and tools run. Its job is to limit the damage or unintended access that could result when an agent executes code, including code influenced by an untrusted task, input, or tool result. OpenAI’s “Sandbox security” documentation summarizes the core exposure: “Agent-generated code can access the files, credentials, and network available to its environment.” The practical implication is to assess what the environment makes available, rather than relying on the platform label.
Start by writing down the assets and boundaries for the evaluation. Distinguish the agent’s execution environment from the application server or harness that coordinates the run. The harness may need to schedule work, approve actions, record results, or authenticate to application services; those responsibilities do not automatically belong inside the sandbox.
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- Data: Which workspace files, mounted directories, snapshots, or prior-run artifacts are visible or writable?
- Credentials: Can generated code read environment variables, API keys, tokens, or cloud credentials?
- Network: Is inbound access possible, and where can the environment make outbound connections?
- Separation: Can one run, user, or workload see another’s files, processes, or network?
- Operations: Who creates the environment, applies policy, monitors it, and destroys or resets it?
These questions turn “How isolated is it?” into a set of concrete controls that can be checked for the particular provider, configuration, and workload.
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How do local execution, containers, hosted sandboxes, and self-hosting compare?
They are not interchangeable security tiers. Local execution may be simplest for trusted development, containers can provide a configured boundary, hosted sandboxes move much of the environment operation to a provider, and self-hosting offers infrastructure control at the cost of operating the lifecycle yourself.
| Option | What the cited documentation establishes | Key decision |
|---|---|---|
| Local execution | The OpenAI Agents SDK guide says Unix-local execution on Linux runs commands as host processes without OS-level confinement. A workspace directory, HOME, or current directory does not restrict host access. Its macOS filesystem restrictions do not provide network isolation equivalent to a container. |
Use for trusted development or add an external isolation boundary; do not treat a working directory as a security boundary. |
| Docker/container environment | The Agents SDK describes Docker as a container option for basic isolation and a specific image, and its Docker client documents network_mode="none". Docker Sandboxes are a distinct product/configuration whose security documentation describes a microVM boundary, shared-workspace modes, proxy-governed outbound networking, and credentials injected by a host-side proxy. |
Identify the exact container or sandbox product and verify its mounts, permissions, network mode, credentials, and persistence. Do not assume Docker Sandboxes controls apply to an ordinary Docker container. |
| Hosted sandbox provider | The SDK guide lists hosted provider clients including Blaxel, Cloudflare, Daytona, E2B, Modal, and Vercel. OpenAI-hosted sandboxes provide a managed Linux workspace. Daytona documents runtime, network, and project isolation, with open egress by default, closed ingress by default, and no shared network between sandboxes. | Check the provider’s documented defaults and policy granularity, then verify whether they fit the evaluation. Provider descriptions are not independent comparative audits. |
| Self-hosted environment | OpenAI’s guidance describes self-hosting for needs such as a custom image, compute, or private network, and says the application then takes responsibility for provisioning and lifecycle. | Choose it when infrastructure or private-network control matters enough to justify operating isolation, policy, and cleanup. |
The available documentation does not establish a universal winner for price, performance, reliability, or security effectiveness. Compare the specific setup you intend to run, not a broad category name.
When is local execution appropriate?
Local execution is a development convenience, not a host-protecting sandbox in the Linux behavior described by the Agents SDK guide. A process running from a dedicated folder can still access host resources available to that process; changing its current directory or setting HOME does not confine it. The documented macOS filesystem restrictions also should not be mistaken for container-equivalent network isolation.
That does not make local execution unusable for every task. It can be reasonable for trusted code and low-risk development when the operator understands that it runs as a local host process. For evaluations involving untrusted or agent-generated code, put an external isolation mechanism between the execution and the developer’s host.
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What should I know about Docker and container setups?
The SDK’s Docker option is described as a container environment for basic isolation and for using a specific image. Its Docker client documentation includes a setting to disable networking with network_mode="none". Whether that is useful depends on the task: a benchmark that needs no external data may fit a no-network mode, while a task requiring approved services needs a deliberate allowlist or other controlled route.
“Docker” can also refer to Docker Sandboxes, which must be assessed separately from the SDK’s ordinary Docker container option. Docker’s “Security model” page states, “The primary trust boundary is the microVM,” describing Docker Sandboxes specifically. That page also describes shared-workspace modes, outbound traffic governed through a proxy, and credentials injected by a host-side proxy. Confirm that the actual product and configuration in use provide the control being discussed; do not infer microVM isolation or proxy-based secret handling from the word “container.”
What changes with a hosted sandbox?
A hosted service can provide a managed execution environment and reduce how much infrastructure the application must build itself, but the customer still needs to understand and configure the relevant policy. The Agents SDK guide lists clients for Blaxel, Cloudflare, Daytona, E2B, Modal, and Vercel; the presence of a client says that an integration exists, not that all providers have identical isolation, networking, or persistence.
OpenAI-hosted sandboxes are documented as managed Linux workspaces. OpenAI points users toward self-hosting when they need their own image, compute, or private network. Daytona documents isolation across runtime, network, and project, with egress open by default, ingress closed by default, and sandboxes not sharing a network. Those stated defaults matter: if a workload must not access the public internet, “hosted” alone does not imply that outbound access is disabled.
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When does self-hosting make sense?
Self-hosting is a control and operations choice. It can suit environments that require a custom image, specific compute, or access to a private network, but it moves responsibility for provisioning, policy, and lifecycle to the application operator. A self-hosted setup still needs a verified boundary; ownership of the infrastructure by itself does not confine a process or prevent one run from reaching another run’s data.
Which security controls should I compare?
Compare controls at the level of the specific environment and workload. A useful evaluation records both the provider’s documented behavior and the setting you actually deploy, especially where defaults are permissive.
| Control | Questions to answer | What to verify |
|---|---|---|
| Isolation boundary | Does execution run as a host process, inside a container, or inside a documented microVM-based environment? | Identify the boundary that applies to this product and configuration. Confirm permissions and host access rather than relying on a product category. |
| Run and user separation | Can one workload access another’s workspace, files, or runtime state? Can users share data unintentionally? | Check the per-session or per-user model. OpenAI’s self-hosting guidance calls for isolation by user or workload where data must not be shared. |
| Inbound and outbound networking | Can external systems connect into the environment? Can the environment connect outward, and can destinations be restricted? | Record ingress and egress separately. Check allowlist support, policy granularity, and actual defaults; for example, Daytona documents closed ingress but open egress by default. |
| Credential exposure | Which tokens, environment variables, or service credentials can generated code read? | Keep application API credentials outside the sandbox where possible. For third-party access, consider scoped credentials and a trusted proxy or secret-brokering design. |
| Workspace and persistence | What files are mounted or shared? Does state persist after a run, and can a later run resume it? | Review mount permissions, shared-workspace behavior, snapshots, persistence, and reset behavior. Ensure retained state is intentional and does not cross user or run boundaries. |
| Lifecycle and cleanup | Who provisions, resets, monitors, and destroys the environment? | Make ownership explicit. In a self-hosted setup, the application is responsible for provisioning and lifecycle; for a provider, confirm the documented lifecycle and the controls available to the customer. |
| Control-plane separation | Can the agent reach the service that handles application authentication, billing, approvals, or evaluation records? | Keep sensitive control-plane functions in the application or harness rather than exposing them directly to generated code when feasible. |
How should I choose a platform step by step?
Move from the task’s required capabilities to the smallest environment that satisfies them. This avoids both under-isolation and unnecessary operational complexity.
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- Describe the workload. List the commands and tools the agent must run, the input and output data it needs, and whether it needs internet or private-service access. Distinguish required access from access that would merely be convenient.
- Set the trust boundary. Decide whether agent-generated code is trusted, partly trusted, or untrusted for the evaluation. If it must not access the host or another user’s data, eliminate any option whose documented configuration does not establish that separation.
- Choose a network policy. Decide whether the run needs no network, a limited set of approved destinations, or broader access. Check inbound and outbound rules independently. Open egress is a material configuration choice, not a neutral default.
- Design credential access. Remove application-level API keys and unrelated secrets from the execution environment. If an agent needs a third-party service, use a scoped credential and consider a trusted proxy or broker so the agent does not receive a broadly privileged application secret.
- Choose the operating model. Select a managed hosted environment if its controls and workspace fit the task and you want provider-managed execution infrastructure. Prefer self-hosting when custom images, compute, or private networking require it and you can own provisioning and cleanup. Use local execution only when its host-process behavior is acceptable for the task or a separate isolation layer is in place.
- Verify the exact product and configuration. Confirm the isolation mechanism, filesystem mounts, permissions, network defaults, credential path, persistence, and cleanup behavior for the particular client and provider. SDK compatibility or a provider’s security description does not verify your deployed settings.
- Run a policy check before the evaluation. With non-sensitive test data, verify that the environment can access the intended workspace and services and cannot reach resources that the policy excludes. Record the configuration and observed behavior for the run; do not use a sandbox’s marketing label as evidence that a control is active.
How do I keep agent credentials and application controls out of the sandbox?
Keep the application server and agent execution environment separate wherever practical. OpenAI’s architecture guidance distinguishes the agent harness, execution environment, and application server; its self-hosting guidance says application API keys should remain outside the sandbox. An executor-environment key may be narrower than an application key, but the guidance still notes that agent-generated code can read it.
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Use the following design principles:
- Do not pass application authentication or billing credentials into the sandbox merely to simplify a tool call.
- Give a run only scoped credentials needed for its specific task, with the shortest practical authority and lifetime supported by the design.
- For a third-party service, route requests through a trusted proxy or secret-brokering layer when feasible instead of making a broadly privileged secret readable by generated code.
- Keep approvals, audit records, and sensitive control-plane operations in the application or harness. Let the sandbox return work products or requests for action rather than directly owning those controls.
- Check whether host-side proxy injection, shared workspaces, or environment variables are part of the chosen setup, and identify what the agent can inspect.
These controls reduce exposure but do not substitute for the execution boundary or network policy. If code can read a credential, assume it can use that credential within the access it grants.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What common selection mistakes should I avoid?
- Equating a workspace directory with confinement. On Linux, the Agents SDK guide says local Unix execution is a host process without OS-level confinement; a selected folder,
HOME, or current directory does not limit host access. - Treating every Docker option as the same boundary. The SDK’s Docker container option and Docker Sandboxes have different documented designs. Verify which one is actually running before relying on microVM, proxy, or credential-injection claims.
- Assuming outbound traffic is blocked by default. Network behavior varies. The SDK documents a no-network Docker setting, while Daytona documents open egress by default. Check the deployed setting and the needed destination policy.
- Putting secrets in the environment for convenience. Agent-generated code can access credentials available to its environment. Keep application secrets outside and use scoped access or a trusted broker for service calls.
- Sharing state without a data boundary. A persistent or shared workspace can be useful for resume behavior, but it also raises questions about cross-run and cross-user visibility. Match sharing to the data-separation requirement.
- Assuming a provider statement is an audit. Provider documentation describes a design and its stated defaults; it is not, by itself, an independent comparative security assessment. The sources available here do not establish a provider-wide winner for security, cost, speed, or reliability.
- Letting the agent own the control plane. Authentication, billing, approvals, and audit responsibilities can expose broader application authority than the task requires. Keep these functions outside execution where feasible.
How should network access change for an evaluation?
There is no one network policy that fits every evaluation. OpenAI recommends restricting outbound access to approved endpoints, and its frontier-containment article describes defense in depth using sandbox restrictions, virtual machines, monitoring, and network or cloud perimeters. That article says that live internet is outside the boundary for OpenAI’s training and most evaluation; this is OpenAI’s described practice, not a universal rule for all evaluations.
Use the task requirements to choose among a no-network environment, an approved-destination policy, or broader access. If the evaluation needs live services, name those dependencies and ensure the network route enforces the intended destinations. If it does not, removing network access can avoid granting a capability the task does not need. In either case, separately consider ingress: an environment with constrained outbound access may still need an explicit rule against inbound connections.
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For trusted local development, local execution may be sufficient if its host-process behavior is understood. For basic container isolation or a specific image, a Docker-based setup can fit when its mounts, permissions, and network configuration are verified. For managed Linux workspaces, consider hosted providers and compare their actual defaults and controls. For custom images, compute, or private-network requirements, self-hosting may be appropriate if the application team can operate provisioning and lifecycle.
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Across all four choices, make the decision against the same criteria: execution boundary, separation by run and user, ingress and egress, credential brokering, workspace persistence, cleanup ownership, and control-plane separation. Then verify those controls in the configuration you will actually use.
Frequently Asked Questions
Can a sandbox stop an AI agent from following a malicious instruction?
Isolation limits what the resulting code can reach; it does not guarantee that an agent will interpret instructions correctly. Treat untrusted inputs as a reason to constrain tools, credentials, files, and network access even when the agent is expected to follow policy.
Should evaluation runs have internet access?
Only when the evaluated task requires it. OpenAI describes keeping live internet outside the boundary for its training and most evaluation, but that is a description of its practice rather than a rule for every benchmark.
What should I record so an evaluation can be reproduced?
Record the environment type and version or image, the isolation and network settings, mounted or persisted workspace details, credential mechanism, and cleanup behavior alongside the task configuration. This lets a later reviewer distinguish a changed result caused by the execution environment from one caused by the agent.
Does a hosted sandbox mean my evaluation data is private?
The term “hosted sandbox” alone does not establish data handling, retention, or access terms. Check the provider’s applicable documentation and agreement for those questions, and separately verify whether the runtime shares files or state across users or runs.
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