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To keep an AI agent from reading sensitive files or exposing credentials, run its model-directed code in isolated compute, give it only task-specific files, keep real credentials outside that environment, and restrict network access. Treat anything the agent can read or reach as potentially usable by its code—even if the agent has been instructed not to use it.

Start with the boundary, not the prompt

An agent’s safety depends in part on the environment in which its tools run. OpenAI’s sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.” Design as though any file, credential, or reachable service in that environment may be read or used.

Separate the trusted harness or control plane from sandbox compute. The control plane should handle functions such as model calls, tool routing, authentication, approvals, audit logging, billing, recovery, and session state. The sandbox is where model-directed code reads and writes files, runs commands, and may install packages. Keep sensitive orchestration outside the sandbox where practical; the OpenAI sandbox guidance describes this separation.

A VM or containerized sandbox can provide useful isolation, but the word “container” alone does not establish a complete security boundary. Isolation depends on the host, runtime, provider, and configuration. Verify the actual file, process, network, credential, persistence, and output boundaries you have configured.

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Limit what the agent can read and write

Give each task a fresh, explicit workspace containing only its required inputs, repository, helper files, and output directory. Prefer narrow mounts to a home directory, a collection of repositories, or a broad cloud bucket. OpenAI’s sandbox SDK guidance describes mounts as workspace inputs and recommends mounting only what the agent should use.

  • Mount inputs read-only where the runtime supports it; provide a separate, limited writable output path.
  • Keep private data out of prompts, task files, and generated artifacts unless the task genuinely requires it.
  • Use per-run workspaces and define cleanup or expiration behavior.
  • Review outputs before transferring them into trusted storage, especially when the agent could read private documents.

Check the provider’s documentation to establish whether these controls are supported and what they mean in practice. Do not assume a mount is read-only, or that deleting a workspace removes snapshots or other retained state, without verifying its semantics.

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Keep real credentials out of agent-readable compute

A secrets manager can protect a credential at rest and help manage its lifecycle, but it cannot keep a secret safe from code that can read it after injection. OpenAI’s sandbox security guidance says to keep application API keys outside the execution environment and describes using a restricted environment key and proxy to supply third-party secrets for approved hosts. Its SDK guidance says credentials should not appear in prompts, instructions, task files, committed manifests, or generated artifacts.

Instead, put the actual credential in trusted infrastructure and expose a narrow application function or proxy. For each capability:

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  1. Store the credential outside model-directed compute.
  2. Allow only the required action and destination.
  3. Have the broker make the approved request using the real credential, rather than returning that credential to the agent.
  4. Return only the result the task needs, and log the operation without logging secret values.

If a real credential may have been exposed to agent-readable code, revoke or rotate it. A “secret” delivered into that runtime is no longer protected from the code running there.

Restrict network access to necessary destinations

Default to no outbound network access when a task does not need it. If it does, allow only the required hosts, protocols, and services. Account for where each connector executes: the OpenAI Agents API guide distinguishes executor-side connections from remote MCP connections and directs developers to allow the relevant hosts.

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Egress restrictions reduce opportunities for an agent to contact malicious resources or send data to unapproved destinations. They do not prevent local file reads, and they cannot replace limiting the data and credentials available inside the sandbox.

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Assume external content can manipulate the agent

Prompt injection is malicious instruction content placed in material such as a web page or document. A page the agent is asked to summarize may also try to make it disclose data or take an unrelated action. OpenAI’s March 11, 2026 article, “Designing AI agents to resist prompt injection,” emphasizes constraining impact rather than relying only on input filtering.

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  • Give the agent a bounded, task-specific objective and only the tools and data needed to complete it.
  • Require review or confirmation for consequential actions, such as sending messages or changing important records.
  • Monitor sensitive systems for agent activity.

A confirmation is a final control before an action, not a substitute for limiting what the agent can see or attempt. Instructions and filters may help, but should not be the boundary protecting sensitive files or credentials.

Define persistence and inspect exports

Establish whether an environment is fresh, reused, resumed, or restored from a snapshot. OpenAI’s sandbox SDK documentation notes that the effective workspace can come from a live session, serialized state, or snapshot—not only the initial manifest. Decide what can survive between runs, what is excluded from snapshots, who can resume a session, and how outputs are inspected before transfer.

Choose hosted or self-hosted execution by requirement

Neither deployment model is universally safer. The right choice depends on the network boundary, workspace lifecycle, isolation needs, and who will operate and monitor the environment.

Decision area Hosted sandbox Self-hosted environment
Infrastructure ownership Compute is provider-managed. Your organization operates the infrastructure.
Network boundary Check whether its egress controls meet your requirements. Can suit needs for your own infrastructure, software, or private network, as described in OpenAI’s self-hosted sandbox guidance.
Isolation scope Verify how environments and workspaces are separated for users and workloads. Agents sharing an environment can share access: OpenAI warns, “Agents that share an environment can access the same files, credentials, and other resources.” Use separate environments when access must not be shared.
Credentials Check provider-native secret handling and whether it keeps real credentials outside agent-readable compute. An organization-managed proxy or application broker can hold credentials outside the environment.
Workspace lifecycle Verify mount, persistence, snapshot, and artifact-retrieval behavior. Configure and verify mount, persistence, snapshot, and artifact-retrieval behavior in your own setup.
Operational responsibility Determine which controls and operational tasks the provider manages and which remain yours. Your organization is responsible for operating the infrastructure, including patching, monitoring, auditing, and responding to exposure.

OpenAI’s self-hosted sandbox guidance identifies an organization’s own infrastructure, software, or private network as reasons to consider self-hosting. Whichever model you choose, verify its specific security properties rather than inferring them from the deployment label.

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