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Secure an AI agent sandbox by limiting what it can read and change, restricting where its processes and tools can connect, and keeping long-lived credentials out of the execution environment. Then test those boundaries directly. “Sandbox” is not one universal security boundary: the effective protections depend on the host, runtime, mounts, network path, tools, and credentials your deployment actually uses.

Start with the actual boundary, not the product label

An agent can use the files, credentials, tools, and network routes available to its environment. OpenAI’s sandbox security guidance describes this as a core consideration: agent-generated code can access what the environment makes available. A prompt-injected or otherwise compromised agent may therefore exercise permissions that were intended for ordinary task work.

Before deployment, map the execution path: where the agent process runs, where shell commands and subprocesses run, which tools execute locally or remotely, and which component makes each network connection. A restriction on the agent’s main process does not necessarily constrain a remote service, an MCP server, or a child process.

Checklist: constrain filesystem and process permissions

  • Isolate untrusted generated code. Run it in a separate workload, and use separate environments for users or trust boundaries when their data must not be shared.
  • Limit writable paths. Give the agent write access only to project or task data it needs. Keep protected source, configuration, and host files outside that scope.
  • Review readable paths too. Read-only access is not safe by itself: an agent that can read a secret and reach an external destination may be able to expose it.
  • Inventory the full execution surface. Check mounts, environment variables, installed tools, shell access, subprocess behavior, custom tools, and MCP servers. Each can add capabilities beyond the agent interface.
  • Keep sensitive control functions outside the workload. Where feasible, place review, approval, audit, billing, and recovery functions outside the container or execution environment.

Approval prompts can help people supervise actions, but they are not a substitute for operating-system-enforced boundaries. If a command or tool can reach a file or service without an enforced restriction, a prompt policy alone does not make that access impossible.

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Checklist: restrict network access where connections happen

  1. Begin with outbound access disabled when the task can work without it.
  2. Allow only required destinations and protocols when network access is necessary. Treat package managers and code-hosting services cautiously: allowing them can enable access beyond a small, explicit host list.
  3. Enforce policy at the connection point. Identify whether connections originate from a local executor, remote tool, proxy, VPC, or firewall, and apply rules there. OpenAI distinguishes executor MCPs that connect from the user environment from remote MCPs that connect from OpenAI’s service in its security documentation.
  4. Check redirects and proxy behavior and assess whether allowed routes can reach internal services.
  5. Confirm coverage for child processes and connected tools. Model instructions do not enforce network isolation. A shell command or custom tool may use a different connection path than the primary agent.

Network configuration can be deployment-specific. Anthropic’s Claude Platform security model places network access under VPC and firewall configuration. That is a reminder to verify the deployed network path rather than assume that a product-level sandbox label defines every route.

Checklist: keep secrets out of agent execution

  • Keep application and long-lived third-party credentials outside the execution environment wherever practical.
  • Use narrowly scoped credentials. Grant only the actions and destinations required for the task.
  • Broker access when a task needs an external API. A vault, trusted proxy, or server can supply credentials only for an approved destination instead of exposing a reusable key to the agent. This requires deliberate configuration; using a secrets-management service alone does not prevent exposure.
  • Assume injected environment variables are readable by generated code. Do not treat environment-variable injection as secret isolation.
  • Keep secrets out of source, images, and logs. Separate credentials that connect to an executor from credentials that authorize application or account actions.
  • Rotate and revoke credentials. Rotate them regularly and revoke promptly if exposure is suspected.

For managed or hosted environments, inspect how credentials reach the workload and which component can read them. Anthropic documents environment setup in its cloud environment setup guide; deployment settings, rather than the general word “sandbox,” determine the practical exposure.

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Test the controls before trusting them

Verify the boundary with deliberate tests in a safe environment. Check that the agent cannot read outside allowed paths, modify protected files, reach unapproved hosts, access internal services, or inspect credentials it should not have. Repeat the checks through shell commands, subprocesses, custom tools, and connected MCP servers—not only through the primary agent process.

Record the policy, exceptions, execution locations of tools, and observed access so operators can audit them. Repeat the checks after changing the runtime, mounts, tools, or network configuration; each change can alter the effective permissions.

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How to compare sandbox configurations

Feature lists alone do not establish that a deployment is safe: a feature must be enabled, correctly configured, and tested in the path the workload actually uses. Compare configurations against these operational questions:

Area What to establish
Filesystem Which paths can the agent read and write? Are mounts and protected data accounted for?
Network Is outbound access disabled by default or restricted to necessary destinations? How fine-grained are the rules?
Processes Do policies cover shell commands and child processes, not just the main agent?
Tools and MCP Where does each tool connect from, and does it follow the same restrictions as the agent?
Credentials Are credentials isolated or brokered, narrowly scoped, and kept out of readable environment variables?
Workload separation Are users or tasks isolated when their data must not be shared?
Operations Can the team audit access and exceptions, test changes, and maintain the controls?
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What the available security claims do—and do not—show

Anthropic reported that sandboxing safely reduced permission prompts by 84% in its internal usage results, in an engineering article published October 20, 2025. That figure concerns permission prompts in Anthropic’s internal usage; it is not an independently verified reduction in security incidents and should not be generalized to other products. Anthropic describes the combination of techniques as providing a safer and faster Claude Code experience in its sandboxing article.

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OpenAI’s GPT-5.2-Codex addendum is another product-specific security source, not evidence that every agent deployment shares the same protections. Compare the configuration you actually run, and verify its behavior rather than inferring safety from a vendor feature or claim.

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