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Practice with synthetic or explicitly approved, minimized data first; inspect the skill; and give the agent only the files, tools, and network access required for one narrow exercise. If the task needs files or commands, isolate execution in a sandbox. Keep credentials out of the agent-visible environment, and require independent authorization and human review for consequential actions. These controls reduce different risks; none makes an AI agent safe on its own.
What “safe practice” means
An AI skill is part of the agent’s instruction and execution surface. Its instructions and supporting files can shape what the agent does, while connected tools and the execution environment determine what it can actually access or change. Review the skill before enabling it, then bound the exercise by data, permissions, and an independently enforced approval process.
There is an important distinction between what an agent can do and what it is authorized to do. A model’s stated intention or risk assessment should not be the control that permits a sensitive action. Put authorization checks in the application or execution layer, especially for destructive, financial, administrative, or externally visible operations.
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Choose a practice setup that fits the exercise
Start with the least sensitive data and narrowest permissions that still let you test the skill. A short exercise that only reasons over prompt context may not need a persistent sandbox. Use an isolated environment when work involves files, shell commands, installed packages, generated artifacts, or resumable workspace state. OpenAI’s Sandbox Agents guidance distinguishes sandbox execution from trusted harness duties such as authentication, approvals, audit logging, and recovery.
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| Practice setup | Data and execution | Permissions and safeguards |
|---|---|---|
| Prompt-only exercise | Synthetic or explicitly approved, minimized content; no files or persistent workspace required. | Task-specific instructions and only the tools needed, if any. Do not treat clear instructions as a replacement for access controls. |
| Isolated workspace | Synthetic or approved, minimized files; commands, packages, generated artifacts, or persistent state run inside a sandbox. | Restrict file access and outbound network traffic to what the exercise requires. Keep trusted authorization, logging, and recovery outside the execution boundary where practical. |
| Connected company workflow | Only the specific company records needed for the task; use restricted data only when explicitly approved. | Separate read-only work from writes and external actions. Enforce permissions outside the model, keep credentials out of the agent-visible environment, and require review for consequential actions. |
The table describes practice patterns, not a guarantee: the right boundary depends on the task and what the environment makes available to agent-generated code.
Build a safe practice loop
1. Define one low-risk exercise
Pick a narrow skill and a task with a clear expected result. Begin with synthetic data or company data that is explicitly approved and minimized to what the task needs. State what the agent may read, what it may produce, which tools it may use, and which actions it must not take. Clear guidance and examples can reduce ambiguity, but they do not enforce permissions.
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2. Review the skill and its supporting files
Read the skill instructions and inspect the files it depends on before making them available. Look for unrelated instructions, unexpected tool or network requirements, and requests for broader company access than the task needs. OpenAI’s Skills API guide identifies prompt-injection-driven data exfiltration as a risk to consider when using skills.
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Decide whether the task needs an isolated workspace. If it does, keep the work there and separate it from trusted application functions where practical. The trusted harness or application should handle duties such as authentication, authorization, approvals, audit logs, and recovery rather than relying on model-directed code to enforce them.
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Allow only the files, tool operations, and network destinations needed for the exercise. Keep read-only work separate from write or externally visible actions. Restrict outbound connections to approved endpoints. OpenAI warns that agent-generated code can access the files, credentials, and network available to its environment in its Sandbox security guidance.
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5. Test with safe adversarial examples
Documents, websites, and tool responses can contain prompt injection: untrusted text that tries to redirect the agent or override its instructions. Treat external content as data, limit the information the agent can access, and make the task specific. OpenAI’s prompt-injection guidance recommends limiting access to the data needed for a task and reviewing consequential actions.
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Use benign test content that includes hostile or irrelevant instructions. Check whether the skill stays within its task or attempts an unrelated disclosure or action. Also test tool abuse, permission boundaries, and how high-impact actions are handled. The application’s authorization layer—not the model’s stated intent—should block actions outside the exercise scope. OWASP’s AI Agent Security Cheat Sheet covers least privilege, independent controls, and structured security testing.
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6. Review and record before expanding access
Require a person to review consequential actions before they are confirmed. Make the approval identify the exact action and target, rather than granting blanket permission. Where the platform supports it, record relevant tool calls, decisions, approvals, results, and network-policy outcomes. OpenAI describes sandboxing, approvals, policy rules, and telemetry in its May 8, 2026 account of its own Codex deployment, Running Codex safely at OpenAI; this is an organizational example, not proof that the same setup works in every environment.
7. Re-test after material changes
Repeat structured security tests when prompts, tools, memory, retrieval, policies, or model providers change. If an exercise behaves unexpectedly, keep it bounded while you investigate; adjust permissions and controls before trying the next level of access.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to do when a test fails
- The agent follows instructions embedded in a document or tool result: stop the exercise, reduce the data and tool access, and test again with benign hostile content. Treat the external text as untrusted data rather than as authority to change the task.
- The agent can reach files or services outside the exercise: remove those resources from its environment and restrict outbound traffic to approved destinations before resuming.
- A secret is visible to agent-generated code: remove it from the environment and use a trusted application-side function, proxy, or supported secret-brokering approach instead.
- A high-impact action can proceed without a separate check: disable that operation until an independent authorization layer validates it and the required reviewer can approve the exact action.
- Logs do not show what happened: do not widen access until the platform can record the relevant actions and outcomes, or the exercise can be redesigned to operate within the available oversight.
Why layered controls matter
Prompt injection, data exposure, and excessive tool authority are different failure paths. Instructions and structured outputs can make behavior clearer; data minimization limits what can be exposed; sandboxing limits the execution environment; least privilege narrows available actions; and independent authorization and human review address consequential operations. Testing and logging help reveal whether those boundaries worked. None of these controls, alone or together, is a promise of complete prevention.
Product-specific interfaces and guidance can change. OpenAI’s Safety in building agents page includes a notice that Agent Builder is being deprecated and schedules shutdown for November 30, 2026. Check the live documentation before relying on that product or timeline.
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