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What an AI sandbox does—and does not—protect
Agent-generated code can use the files, credentials, tools, and network routes available to its environment. OpenAI describes this directly in its sandbox security guidance. If an agent is prompt-injected or otherwise compromised, instructions alone cannot prevent it from trying to use those capabilities.
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Think of the boundary as the combined effect of the host, execution runtime, filesystem mounts, network controls, credentials, and connected tools. A permission prompt can support human review, but it is not a substitute for operating-system-enforced isolation. Keep review, approval, audit, billing, and recovery functions outside the container where feasible.
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Checklist: restrict filesystem and execution permissions
Isolate workloads and users
- Run untrusted agent-generated code in an isolated workload rather than on a developer’s unrestricted workstation or a shared host.
- Use a separate environment for each user or trust boundary when workloads must not share data. Confirm that temporary files, caches, mounted directories, and persisted artifacts do not create an unintended path between users.
Limit both readable and writable paths
- Give the agent write access only to the project or task data it needs. Protect source-of-truth files and host paths that are outside that scope.
- Review readable paths as carefully as writable ones. A file does not need to be modifiable to be exposed: if the agent can read a secret and reach an external destination, it may be able to send that secret out.
- Inventory mounts, including their host-side sources, permissions, and whether they expose credentials, configuration, sockets, or other sensitive data.
Include every execution path in the permission review
Do not assess only the agent’s main interface. Record which tools and processes are available and where they run:
- Shell access, installed tools, subprocess creation, and any ability to invoke scripts or package managers.
- Environment variables and files visible to the workload.
- Custom tools and MCP servers, including the location from which each makes connections.
- Approval, audit, billing, and recovery functions that should remain outside the workload.
OpenAI’s Sandbox Agents documentation describes sandbox configurations, but a listed capability does not establish that a particular deployment has enabled, correctly configured, or tested it.
Checklist: control network access where connections happen
Start with the smallest necessary access
- Disable outbound access when the workflow can operate without it.
- If the task requires network access, allow only the endpoints and protocols it needs. Review access to internal services separately from public destinations.
- Check redirects and proxy behavior; a short host allowlist may permit broader reach than it appears to if destinations can redirect or a proxy handles requests differently.
Find the actual connection point
Map each network request to the process and environment that makes it. Enforcement may belong at a local executor, remote tool, proxy, VPC, or firewall. A rule on the agent’s container will not necessarily constrain a tool that connects from somewhere else.
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OpenAI distinguishes executor MCPs, which connect from the user environment, from remote MCPs, which connect from OpenAI’s service. Anthropic’s managed-agent guidance places network access under VPC and firewall configuration. These examples show why teams must verify each tool’s connection location rather than assuming all traffic passes through one sandbox:
- OpenAI: Sandbox security
- Anthropic: Security model – Claude Platform Docs
- Anthropic: Cloud environment setup – Claude Platform Docs
Cover subprocesses and connected tools
Confirm that network policy applies to child processes and custom tools, not only the primary agent process. Model instructions such as “do not access this host” express intent; they do not enforce a network boundary.
Checklist: keep secrets out of the execution environment
Avoid injecting long-lived credentials
- Keep application credentials and long-lived third-party API keys outside the agent’s execution environment wherever possible.
- Assume code running in the environment can read any credential injected as an environment variable. Do not place secrets in source files, images, or logs.
- Separate credentials used to connect an executor from credentials that authorize application or account actions. Limit each credential’s scope to the work it must perform.
Broker access when the task needs a credential
When an agent must call a third-party API, consider brokering access through a vault, trusted proxy, or server that supplies only the authorization needed for an approved destination. Configure the broker and destination restrictions deliberately: using a secret-management service does not by itself prevent exposure if the secret is still made readable to agent-generated code.
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Plan for suspected exposure
Rotate credentials regularly and revoke them promptly if exposure is suspected. Check whether logs, artifacts, or persisted workspace files could retain credential values after a run.
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Verify the boundary with adversarial checks
Test the configured environment rather than inferring its protections from a product feature list or prompt behavior. Run checks in a controlled test workload and confirm what the agent and its tools can actually do:
- Attempt to read files outside the allowed paths.
- Attempt to modify protected files and locations.
- Attempt to reach an unapproved external host and an internal service that should be inaccessible.
- Attempt to inspect credentials in environment variables, files, logs, and other exposed locations.
- Repeat relevant checks through shell commands, subprocesses, custom tools, and MCP servers.
Record the policy, exceptions, tool execution locations, and observed access so the team can audit the result. Re-run the checks after changing the runtime, mounts, tools, or network configuration.
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Compare configurations by the boundary they enforce
When comparing agent runtimes or deployment configurations, use the same questions for each. A feature’s presence is not evidence that it is active or effective in your deployment.
| Area | What to establish |
|---|---|
| Filesystem | Which paths are readable and writable? What mounts and persisted data are exposed? |
| Network | Is outbound access disabled by default? How narrowly can destinations and protocols be allowed? |
| Policy coverage | Do filesystem and network rules apply to child processes as well as the main agent? |
| Tools and MCP | Where does each tool connect from, and which component enforces its network policy? |
| Credentials | Are credentials kept outside the workload, narrowly scoped, and brokered where necessary? |
| Workload separation | Are users or trust boundaries isolated from one another and from shared data? |
| Auditability | Can operators review the policy, exceptions, execution locations, and observed access? |
| Operator effort | What must the team configure, test, monitor, and recheck as the deployment changes? |
OpenAI’s GPT-5.2-Codex cybersecurity addendum is relevant to model-specific security context, but it does not replace testing the execution and network boundaries of a particular deployment. No independent cross-product statistic establishes that one sandbox configuration produces better security outcomes than another.
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