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What do sandboxing and access restriction each do?
An agent can access the files, credentials, tools, and network resources made available to its execution environment. A sandbox narrows the execution boundary; access controls narrow the resources available within or through that boundary. They work together, but one does not replace the other.
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- Sandboxing: constrains where commands and agent-directed code run, such as in a container or provider-managed environment.
- Access restriction: limits operations and resources, including file permissions, tool permissions, database access, credentials, and network destinations.
Prompting an agent not to read a file or call a tool is not an enforceable security boundary. Put restrictions in the operating environment and trusted application layers. OpenAI’s sandbox security guidance recommends isolating workloads, restricting outbound traffic, separating application credentials from the executor, and brokering third-party credentials outside it.
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Choose the execution environment based on the boundary you need, not on whether a product uses the word “sandbox.” A workspace directory alone does not confine a process. Compare options across isolation, network policy, credential handling, sharing, and operational ownership.
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| Execution option | What the documented guidance establishes | Network and credentials | Who operates the boundary |
|---|---|---|---|
| Unix-local Linux | The OpenAI Agents SDK client guide says commands run as host processes and receive no OS-level confinement. A workspace path, HOME, or cwd does not limit host access. |
Resources allowed to the host process are reachable; workspace settings do not isolate network access. | The local operator is responsible for external isolation and host configuration. |
| Local macOS | The SDK guide says the local backend applies filesystem restrictions, but does not provide network isolation or the same boundary as a container. | Network isolation is not provided by this backend. | The local operator remains responsible for additional controls. |
| Docker or other containerized execution | The SDK guide recommends an appropriately configured Docker sandbox or external isolation for untrusted commands; a container label alone does not establish its configuration or guarantee. | Configure mounts, permissions, and network policy deliberately. The guide does not specify a universal network or credential policy. | The operator must configure and maintain the container boundary. |
| OpenAI-hosted sandbox | OpenAI documents separate workspaces per session. | Documented network modes allow outbound access, disable it, or restrict it to listed domains. Enabled is the documented default unless an inherited template policy applies. Vault credentials can keep real secrets outside the sandbox. | OpenAI provides the hosted execution service; configure the documented policy and integrations for the workflow. |
| Self-hosted sandbox | The operator chooses and prepares the laptop, container, or remote sandbox. Agents sharing an environment can access the same files, credentials, and other resources. | Scope the environment key and keep the application API key out of the sandbox. A trusted proxy or server is needed if secrets must be brokered. | The operator controls and is responsible for preparation, isolation, and maintenance. |
These are product-specific documented behaviors, not guarantees about every local, container, hosted, or self-hosted system. For the OpenAI SDK’s implementation details, see Sandbox clients, Sandbox Agents, OpenAI-hosted sandboxes, and Self-hosted sandboxes.
Keep the harness outside the execution boundary
The harness coordinates the agent loop, model calls, tool routing, approvals, tracing, recovery, and run state. Sandbox compute executes commands and manipulates files. Keeping the harness in trusted infrastructure and execution in a sandbox can keep authentication, billing, audit logs, human review, and recovery state outside the execution container. A sandbox is especially useful when a task needs a workspace, commands, packages, artifacts, exposed services, or resumable work; a short response without a persistent workspace may not need one. OpenAI describes this division in its Sandbox Agents guide.
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How do I stop an AI agent from accessing files or secrets?
Isolate users and workloads
Use separate environments when users or workloads must not share data. Treat any shared environment as shared access: agents in it may be able to reach the same files, credentials, and other resources. Control mounts and shared workspaces rather than assuming a separate working directory creates separation. OpenAI’s self-hosted sandbox guidance calls out this shared-environment risk.
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Grant only the permissions needed for the specific task. Singapore government guidance recommends scoping agent and delegation roles, strictly limiting execution privileges, avoiding admin or sudo by default, limiting database write access, restricting access to sensitive personal data, and preventing agents from changing their own privileges. Prefer read-only access where it is sufficient.
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Apply the same scrutiny to tools and integrations as to files. A connected application can extend effective access beyond the execution environment: OpenAI warns that users may access data or perform actions through a creator’s personal app connections. Its Workspace Agents guidance recommends least privilege, limiting the agent’s audience, avoiding sensitive or high-impact connectors, and regularly auditing configurations.
Keep valuable secrets out of agent-readable environments
Do not place application API keys or third-party secrets where model-directed code can read them. Instead, use a trusted broker or vault-backed proxy to grant scoped access outside the sandbox. For an OpenAI-hosted sandbox, documented vault credentials keep real secrets outside the sandbox; a proxy can provide a real secret only for approved hosts while code sees a placeholder. With self-hosted infrastructure, the operator must provide that trusted proxy or server. See OpenAI’s sandbox security guidance.
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- Dedicated Manager App: Use the Thetis Manager App for the initial hardware PIN setup. Setting the PIN on the device first ensures a smooth registration process. Once the PIN is configured, you can begin registering the key across your favorite FIDO2-compatible online services.
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How do I restrict an AI agent’s network access?
Make egress disabled or narrow by default when the workflow allows it. If the agent needs external services, permit only the required destinations and route sensitive credential use through a trusted broker. Network rules should be enforced outside the model, at the sandbox, host, proxy, or infrastructure layer.
OpenAI-hosted sandboxes document three network choices: allow outbound access, disable it, or restrict it to listed domains. The documentation says outbound access is enabled by default unless an inherited template policy applies. Do not assume these defaults or controls apply to other providers or local environments. Singapore government guidance recommends blocking inward and outward network access by default, then granting only necessary connectivity.
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What other agent resources need protection?
Shared memory and retrieved information
Shared agent memory is a distinct access and integrity risk. AWS Prescriptive Guidance describes it as dynamic and potentially difficult to validate with conventional database constraints. Treat it as partially trusted: limit who can modify it, use read-only permissions where practical, and validate retrieved information before acting. A deterministic gateway can centralize filtering, integrity checks, policy enforcement, and audit trails. See AWS Security for agentic AI on AWS.
Generated scripts and third-party tools
Test third-party tools in hardened sandboxes before production, with syscall and network-egress restrictions. Sandbox and monitor generated scripts rather than allowing them to run with broad host privileges. These measures are among the recommendations in Singapore’s Securing Agentic AI addendum.
Quick Recap
Deployment checklist
- Define the boundary: identify whether code runs as a host process, in a configured container, or in a provider-managed sandbox; verify the actual OS or virtualization controls.
- Separate workloads: provide distinct environments for users or tasks that must not share files, credentials, or other resources; inspect mounts and shared workspaces.
- Scope permissions: grant task-specific roles and tool operations, avoid admin or sudo by default, minimize database writes, and prevent privilege changes.
- Set network policy: disable egress where possible; otherwise allow only necessary destinations and control where tool connections originate.
- Protect credentials: keep application and third-party secrets outside agent-readable execution; use a trusted broker or vault-backed proxy for scoped access.
- Protect data paths: restrict sensitive personal data, treat shared memory as partially trusted, and validate retrieved information before consequential actions.
- Keep trusted services outside execution: where practical, keep orchestration, authentication, approvals, audit records, and recovery state in trusted infrastructure.
- Assign operational ownership: determine who configures, patches, monitors, isolates, and audits the environment. Self-hosting gives the operator control and responsibility; hosted compute delegates provisioning to the provider.
- Review integrations and test: audit connected apps and intended audience; test tools and generated scripts under restrictive syscall and egress policies before production.
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