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OpenAI’s Sandbox security documentation states the boundary plainly: “Agent-generated code can access the files, credentials, and network available to its environment.” Your sandbox therefore determines the agent’s blast radius. It reduces the consequences of a bad decision; it cannot guarantee that the agent will never make one.
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What a secure AI-agent sandbox must contain
Think in terms of two planes. The control plane is your trusted application or harness. It starts model requests, routes tools, authenticates users, records policy decisions, handles approvals, stores audit data, and performs recovery. The execution plane is disposable or tightly isolated compute where the model’s shell commands, generated programs, and file operations run.
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Do not let the execution plane own the credentials or authority that the control plane uses to manage production. If an agent compromises its workspace, the attacker or faulty code should find only task-specific data and permissions, not your application API key, administrator token, home directory, or deployment network.
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Use these design properties as a minimum:
- A separate runtime for each user, tenant, or trust boundary when their data must not be shared.
- An explicit filesystem scope with only the mounts the task needs.
- Read-only access for reference material and write access only to intended outputs.
- Default-deny network egress, with narrowly reviewed destinations when connectivity is required.
- Short-lived, least-privilege credentials supplied only for a specific operation.
- A trusted stop, snapshot, rollback, logging, and cleanup path outside the agent.
These controls are independent. A read-only filesystem does not stop data exfiltration over an allowed network route. A blocked network does not prevent deletion of every file mounted into the workspace. A prompt telling the model to “act safely” is not a substitute for either control.
How do I stop an AI agent from deleting files?
First, ensure it cannot see or write the files you need to protect. Create a dedicated workspace for the task instead of running the agent in your normal home directory or a repository that contains unrelated projects.
1. Create a task-specific workspace
- Provision a new directory, container, or virtual machine for the run.
- Copy in only the source files and configuration required for the task.
- Mount reference material as read-only whenever the agent does not need to edit it.
- Expose a separate writable directory for generated artifacts, patches, or test results.
- Destroy or reset the environment after the run unless persistence is an explicit requirement.
Never mount the host root, your complete home directory, SSH keys, cloud-provider configuration, password stores, or broad shared volumes by default. OpenAI’s self-hosted sandbox guidance warns that agents sharing an environment may be able to access shared files, credentials, and other resources. Isolation per workload is the safer default when data separation matters.
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Use disposable workspaces or snapshots so a failed run can be discarded instead of repaired file by file. Keep the snapshot and restore mechanism in the trusted harness, not in a process the agent can modify. Before applying generated changes to a valuable repository, review a diff in the control plane and require a human or policy approval for destructive operations.
3. Test the actual write boundary
During setup, verify that the agent can create and modify files only below the approved output path. Test attempts to write to a parent directory, a mounted reference directory, a sibling tenant’s workspace, and a host path. A path filter in your application is not enough if the same agent can also execute an unrestricted shell command.
Can an AI agent access files outside its project?
It can if the operating system, runtime, mounts, or credentials make those files reachable. “Project directory” is a convention; the security boundary is the permission set enforced by the execution environment.
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Filesystem controls to apply
- Explicit mounts: pass individual directories, not an entire host volume.
- Access modes: mark inputs read-only and grant write permission only to output locations.
- Ownership: run as a non-root user whose UID and group memberships include only required resources.
- Shared storage: avoid shared writable volumes between users or tasks unless the data-sharing requirement is deliberate and reviewed.
- Temporary secrets: do not place credentials in files that remain visible after the operation.
- Cleanup: remove temporary workspaces, caches, and generated logs according to your retention policy.
If the agent needs source-control access, provide a checkout containing only the relevant repository or branch. If it needs a package cache, expose a controlled cache rather than the host’s complete development environment. Keep deployment manifests and production state outside the writable workspace until a reviewed change is ready.
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The OpenAI Agents SDK documentation describes different behavior by platform. On macOS, local commands can use sandbox-exec for filesystem restrictions, but that mechanism does not automatically provide network isolation. On Linux, local path grants do not constrain arbitrary shell commands unless an external isolation layer enforces the restriction.
Consequently, an application-level file API or a path allowlist is not a complete boundary when the agent can invoke unrestricted shell tools. Confirm which commands are intercepted, which processes inherit the policy, whether child processes are covered, and what happens when the agent launches a compiler, interpreter, or another helper program.
How do I prevent an agent from reaching my network or cloud infrastructure?
Start with no outbound network access. Add connectivity only for a documented task requirement, and enforce the decision at a firewall, proxy, virtual network, or runtime boundary that the model cannot rewrite.
Use an allowlist, not a general internet route
- List every destination the task needs, such as a package mirror, source-control service, or one API endpoint.
- Allow only those hosts and required ports at the network boundary.
- Decide whether the agent needs read-only or write access at each destination.
- Block private address ranges, metadata services, management interfaces, and internal production networks unless a specific operation requires them.
- Log connection attempts, denials, destination changes, and proxy requests in the trusted control plane.
Review the capability of each allowed host. An endpoint that appears harmless may permit uploads, repository writes, issue creation, or access to other projects. Anthropic’s cloud-environment guidance warns that prompt injection in untrusted input can use an allowed host to copy files out of the sandbox. Model behavior is therefore not a network security control; the allowlist and the permissions at the destination must limit what exfiltration can accomplish.
Keep cloud credentials outside the runtime
Do not inject a general administrator key into the agent environment. Create a separate identity for each workload, grant only the specific operation required, and make the credential short-lived where the platform supports it. Revoke or rotate it after the task when appropriate.
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OpenAI’s self-hosted guidance specifically recommends keeping the application API key outside the sandbox and scoping an environment key to the environment connection. The harness can perform a privileged action on the agent’s behalf after policy checks, without exposing the underlying application credential to generated code.
How do I separate the trusted harness from untrusted execution?
The harness should be the only component that can approve tools, issue credentials, stop a run, inspect policy events, or restore a snapshot. The sandbox should receive a narrow task description and the minimum data needed to perform it.
Responsibilities of the control plane
- Authenticate the human or service requesting the task.
- Select the runtime image and isolation profile.
- Translate policy into mounts, identities, capabilities, and network rules.
- Broker model calls and tool requests without exposing master credentials.
- Require approval for actions that affect protected systems.
- Record commands, file operations, network decisions, approvals, and errors.
- Stop the runtime, preserve relevant logs, and restore or destroy state.
Responsibilities of the execution plane
- Run generated code and tools under the assigned identity.
- Access only the mounted workspace and approved services.
- Use task-scoped credentials, if any.
- Produce artifacts, patches, and test results for review.
OpenAI’s sandbox-agent architecture describes this separation as a way to keep model-directed work away from the harness that owns recovery and control. A compromise in the execution plane should not become a compromise of the orchestration service.
Which isolation layer should you choose?
No technology is automatically safe. Evaluate the configured boundary, not the product label. The following comparison reflects the questions that matter when selecting an implementation.
| Approach | Useful boundary | Questions to verify |
|---|---|---|
| Local OS sandbox | Can restrict selected filesystem and process operations with relatively little setup. | Does it isolate network traffic? Are child processes covered? Can arbitrary shell commands bypass an application’s path policy? What changes between macOS and Linux? |
| Container | Provides a separate execution environment with configurable mounts, users, capabilities, and networking. | Which host paths are mounted? Is the container privileged? Are unnecessary Linux capabilities dropped? Can it reach host services, metadata endpoints, or other containers? Is the root filesystem writable? |
| Virtual machine or remote isolated compute | Provides workload-level separation suited to a stronger host boundary and per-user or per-task environments. | Is the instance isolated for each workload? How are egress, secrets, persistence, snapshots, patching, and cleanup controlled? What is the recovery time after a failed run? |
Compare candidates on seven axes: host-escape and cross-workload isolation; filesystem mounts and write scope; network egress; credential granularity; persistence and snapshots; operating-system or backend limitations; and operational burden. Verify the runtime configuration in your deployment rather than assuming a default profile provides the boundary you need.
How should a self-hosted sandbox be hardened?
Self-hosting moves responsibility to the operator. Anthropic’s security-model guidance identifies image hardening, egress controls, and key storage as operator duties.
Build a minimal runtime image
- Start from a small, maintained base image.
- Include only interpreters, compilers, package managers, and utilities required by the task.
- Pin and review dependencies; remove debugging tools that increase attack surface.
- Scan and rebuild the image on a regular schedule and after critical security updates.
Reduce process privilege
- Run as a non-root user.
- Drop Linux capabilities that the workload does not need.
- Do not grant privileged-container mode or host-device access unless a documented task requires it.
- Consider a read-only root filesystem and provide narrowly scoped writable temporary and output directories.
Protect service keys
Store environment and application keys in the trusted control plane or a dedicated secret service. If the task needs a credential, issue the smallest possible token for the shortest practical lifetime and prevent it from being written into logs, snapshots, or generated artifacts.
A practical deployment workflow
Use this sequence for every new agent capability or tool.
- Define the task boundary. Write down the exact files, services, commands, and destinations required. Anything not on the list starts denied.
- Choose the isolation layer. Select a local sandbox, container, virtual machine, or remote isolated environment based on host-boundary and cross-user requirements.
- Create the identity. Use a dedicated non-root user and a separate service identity with least-privilege permissions.
- Configure mounts. Add only required paths; make reference paths read-only and isolate writable outputs.
- Configure networking. Deny egress by default, then allowlist exact destinations and operations through an enforcement point outside the model.
- Keep control credentials out. Broker privileged actions through the harness rather than placing application or administrator keys in the runtime.
- Run adversarial checks. Attempt path traversal, parent-directory writes, access to sibling workspaces, metadata-service requests, unexpected DNS lookups, and uploads to an unapproved host.
- Review outputs. Inspect diffs, generated scripts, dependency changes, and network logs before applying anything to a valuable system.
- Finalize state. Export only approved artifacts, then destroy or reset the environment and revoke temporary credentials.
Monitoring, recovery, and maintenance
Log more than the model’s final answer. Capture tool calls, command lines, process identity, mount configuration, policy decisions, approval events, network denials, and the hashes or versions of the runtime image. Send these records to storage the agent cannot modify.
Keep a recovery path that remains available when the agent hangs or behaves maliciously. The harness should be able to terminate the process, revoke credentials, block egress, and discard the workspace without asking the model to cooperate. Snapshots or resumable state can reduce lost work, but snapshots must not contain unprotected secrets and should be accessible only to the trusted control plane.
Re-test after changing the operating system, runtime backend, SDK version, image, network topology, or tool set. Documentation can describe a capability while a particular backend, host policy, or version implements it differently.
Common failure modes and fixes
The agent edits files outside the project
Likely cause: the host home directory or a broad volume is mounted, or shell commands bypass an application-level path check.
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Fix: replace broad mounts with explicit task paths, use read-only inputs, run under a restricted identity, and test arbitrary child processes rather than only the intended file API.
The agent reaches an internal service
Likely cause: the runtime has a general network route, inherited proxy settings, or access to cloud metadata and private address ranges.
Fix: enforce default-deny egress outside the runtime, block private and metadata destinations, and allowlist only the required hosts and ports.
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Likely cause: a credential was placed in an environment variable or file visible to the agent, then echoed or copied into an artifact.
Fix: broker the operation through the harness, issue a narrowly scoped short-lived token when unavoidable, redact logs, and destroy the workspace after revocation.
macOS restrictions work but network traffic is unrestricted
Likely cause: sandbox-exec supplied filesystem restrictions without a separate network policy.
Fix: add an independent firewall, proxy, or isolated runtime for egress control; do not infer network isolation from filesystem confinement.
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Linux path grants do not stop a shell command
Likely cause: the SDK’s local path policy governs an application API but not arbitrary commands launched by the agent.
Fix: run shell-capable tasks in an external isolation layer and verify child-process behavior, mounts, users, and network routes with adversarial tests.
Quick Recap
Preflight checklist
- Is the model-directed process outside the trusted harness?
- Can the runtime see any host path, credential, or tenant data not required for this task?
- Are reference mounts read-only and outputs isolated?
- Can arbitrary shell commands escape the filesystem policy?
- Is outbound traffic denied unless an exact destination is approved?
- Are private networks, metadata services, and management endpoints blocked?
- Are credentials task-specific, least-privilege, short-lived, and kept out of logs?
- Can the harness stop, snapshot, restore, revoke, and destroy the run independently?
- Are tool actions and policy decisions logged outside the sandbox?
- Have you tested path traversal, sibling-workspace access, exfiltration, and child-process behavior?
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