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Limit an AI assistant by restricting what its environment can reach, which accounts and credentials it can use, and what its tools are allowed to do. A prompt asking an assistant to “be careful” is not an access control: code or tools available to the assistant may still reach accessible files, credentials, and networks. Use the checklist below to configure an existing assistant or design a safer deployment.
Start by checking what the assistant can actually reach
Access is determined by the assistant’s environment and the permissions enforced around it—not just by what you ask it to do. OpenAI’s sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.” That principle applies when evaluating any assistant with file, account, or tool access: inventory the resources and capabilities available to the session, then remove what the task does not need.
For an existing product, inspect its actual settings for connected accounts, file access, enabled tools, network access, and approval requirements. Controls and labels vary by platform; do not assume that linking an account grants only harmless or read-only access.
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- Grant access only to the folder, repository, or documents required for the task, rather than a whole drive or home directory.
- Check which mounted drives, local files, repositories, and memory stores remain available to the session. Remove attachments or mounts that are no longer needed.
- Use separate workspaces or environments for users or workloads whose data should not be shared. OpenAI recommends isolated compute and separate environments for workloads that should not share data in its sandbox security guidance.
- Prefer read-only file access when the assistant only needs to inspect or summarize content. If edits are needed, limit write access to the specific working area.
Limit connected accounts and credentials
Choose the narrowest account permissions
Connect only the accounts the task requires. When selecting permissions or OAuth scopes, choose the narrowest available scope and prefer read-only access for tasks that only require reading. Confirm the permissions actually granted in the product or authorization flow. For integrations you build, verify the relevant scope on every backend tool call; OpenAI’s plugin security guidance recommends enforcing scopes on each call.
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Keep secrets outside the agent environment
Do not put long-lived application secrets in prompts, source code, images, logs, or an environment the agent can read. Where possible, keep third-party credentials in a trusted application, proxy, or secret-management boundary, and give the agent only the result or an approved, scoped capability. If a credential must be injected into the agent’s environment, assume agent-generated code can read it.
If you suspect a credential was exposed, revoke or rotate it and review activity associated with it. A connection or secret should not remain available merely because a previous task used it.
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Give tools only the operations they need
Disable tools the task does not require, and distinguish reading from changing data. A calendar assistant that needs to find open times may need event-reading access, but not permission to delete meetings. Scope each tool to particular resources where possible.
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For developers, enforce permissions in trusted backend code—not in the model’s instructions. Before executing a tool call, independently check the tool, target resource, requested operation, and approval state. Validate inputs and reject calls outside the user’s authorization. OWASP’s AI Agent Security Cheat Sheet describes these kinds of authorization and least-privilege controls.
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Constrain network access
If the assistant does not need open internet access, restrict outbound connections to approved destinations and review that allowlist regularly. A permitted host can still be used to transfer data, so an allowlist reduces reach but does not make exfiltration impossible. Also check whether network rules for sandboxed agent calls and hosted tools such as web search are configured separately.
Platform settings are not interchangeable. For example, Anthropic’s managed-agent environment documentation describes a limited networking mode with explicit allowed hosts and says that omitting the networking field in the relevant API request results in unrestricted networking. That behavior is specific to the documented API environment; check the defaults and controls for the product you use.
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Require approval for consequential actions
Require action-specific confirmation before the assistant sends messages, deletes or changes records, makes purchases, initiates financial activity, or performs another consequential or hard-to-reverse operation. The approval should identify the action being authorized; it should not become blanket permission for unrelated future actions.
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Product controls differ. Anthropic’s April 9, 2026 article on trustworthy agents in practice describes Claude.ai and Claude Desktop users choosing enabled tools and setting actions to always allow, require approval, or block. Treat that as a description of those products, not a universal feature set. OWASP likewise cautions that guardrails do not replace least-privilege scopes and human approval in its prompt-injection prevention guidance.
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Account for untrusted instructions in content
Webpages, emails, documents, and tool results can contain instructions aimed at redirecting an assistant. Treat that material as untrusted input, even when the assistant is processing it as part of an ordinary task. OpenAI’s prompt-injection guidance recommends limiting an agent’s access to the data it needs. Least privilege and review of consequential actions reduce the potential damage if an injected instruction succeeds; they do not guarantee that it will be detected.
Ask for a specific task instead of granting broad discretion. For a deployment, screen proposed actions against the user’s task and consider processing risky content in a tool-less or quarantined component before it reaches an action-capable agent.
Use a layered checklist, then review it
- Inventory: List the files, connected accounts, credentials, tools, and network destinations available to the assistant.
- Reduce: Remove unnecessary resources and tools; narrow file locations, account scopes, and network destinations.
- Separate: Isolate workspaces and environments when users or workloads must not share data.
- Enforce: Check resource, operation, and scope permissions in trusted code for every backend action.
- Approve: Require confirmation for consequential actions and tie it to the specific proposed action.
- Review: Remove stale connections, credentials, tools, and allowed hosts. Monitor authorization failures and unusual activity.
- Test: Before deployment, test permission boundaries and prompt-injection scenarios, then include adversarial and regression tests when policies change. OWASP recommends this kind of validation in its agent security guidance.
These controls address different failure paths: a sandbox limits the environment, authorization limits allowed operations, approvals add human review, and model-level defenses may help detect malicious instructions. Anthropic notes that combined safeguards are not a guarantee, and current agent systems do not have a rigorous, standardized measure of prompt-injection resistance. More access can make an assistant more capable, but it also increases the possible impact of misinterpretation or an injection attempt. Make extra permissions or approvals available when a real task needs them, with friction proportionate to the action’s consequences.
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