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Limit an AI agent by restricting the tools it can call, the resources and operations those tools permit, and the files, credentials, and network available to its runtime. Enforce those limits in the connected services and infrastructure—not just in the agent’s instructions. Use a distinct identity, separate read from write access, require fresh human approval for consequential actions, and review and test the controls as the system changes.
What should you control?
An agent’s access is the combined reach of its tools, credentials, and execution environment. A chat interface may appear limited while its connected shell, browser, API, plugin, MCP server, or delegated agent can act on files or business systems. Map the whole route from the agent to the data or action, including indirect access.
- Tools: Which capabilities can it invoke—such as file search, shell commands, email, CRM updates, database queries, publishing, or account administration?
- Resources: Which directories, records, accounts, endpoints, or datasets can each tool reach?
- Operations: Can it read, create, edit, delete, send, purchase, deploy, or change permissions?
- Identity and credentials: Whose authority does it use, and what can its tokens or secrets authorize?
- Runtime and network: What files and destinations can code running for the agent access?
- Oversight: Which actions require additional validation or human approval, and what is recorded for review?
NIST’s Center for AI Standards and Innovation (CAISI) groups agent tools by what they do and by constraints such as read-only, constrained write, or write, as well as whether they operate in trusted or untrusted environments. Its examples include retrieval-augmented generation, browser use, application-specific APIs, coding agents, and computer use. These categories help describe capability; they are not a universal risk score.
How to restrict an agent step by step
1. Inventory every route to action
List the agent’s direct tools and the systems behind them. Include shell and filesystem access, APIs, MCP servers, plugins, browser or computer-use capabilities, and any tools another agent can invoke. For each, record the available operations and target resources. A tool name such as “CRM” is not a permission description: establish whether it can read a particular set of records, edit fields, export data, or administer users.
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2. Start with no access, then add only what the task needs
Remove tools that are not required, then scope the remaining capabilities as narrowly as the task allows. For files, use specific directories and operations rather than broad filesystem access or wildcard shell commands. For business systems, limit both the permitted records and the permitted actions.
Make read and write separate capabilities where practical. An agent that only needs to find a document should not also receive permission to overwrite or delete it. If a workflow genuinely needs writes, constrain which records or fields it can change and prefer reversible operations where possible. OWASP’s AI Agent Security Cheat Sheet advises granting only the minimum tools required for a task and scoping tool access.
3. Enforce authorization at the service boundary
Do not treat a system prompt, tool description, or the model’s decision to comply as an access-control check. Before executing a call, the relevant service, API, gateway, or tool server should verify the initiating person or workload identity, the requested operation, the target resource, and the applicable scope. Reject an unauthorized request even if the agent asks confidently or the user’s prompt appears to authorize it.
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Validate tool inputs against strict schemas, and validate outputs where appropriate. For MCP servers, OWASP’s MCP Security Cheat Sheet recommends authorization on protected requests and scoped credentials for each server. Inspect tool names, descriptions, parameters, and schemas; do not pass unrestricted model-generated paths or raw shell commands to a privileged executor. Schema validation can catch malformed input, but a well-formed request still needs an authorization decision for that user, resource, and action.
4. Give each agent a distinct identity and narrow credentials
Avoid letting an agent act through a shared, all-purpose human account or a standing credential with broad access. Prefer a distinct identity for each agent or workload, separate credentials for different tools, and narrow OAuth scopes. Use short-lived or task-scoped tokens when the platform supports them, and review and revoke access that is no longer needed.
A secret available to code or tools in the agent’s environment is exposed to that environment. Keep long-lived credentials in a secrets manager and broker access outside the sandbox where feasible; do not assume a secrets manager protects a secret after it has been injected into an agent-accessible process. OpenAI’s API documentation on sandbox security warns that agent-generated code can access files, credentials, and network resources available to its environment, and advises keeping the application key outside that environment. AWS and Microsoft publish vendor-specific guidance on least-permission roles, scoped access, and agent identities; their service examples are not universal platform requirements.
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5. Isolate the runtime and restrict network access
Run agent-generated code in an environment suited to the task’s risk, such as a dedicated virtual machine or restricted container. Mount only the paths it needs, avoid sharing data between workloads that should be isolated, and block outbound network connections unless the task requires them. If network access is needed, allow only approved destinations where your infrastructure supports that control.
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Isolation reduces the potential reach of a mistake or attack, but it does not compensate for a tool that is itself over-permissioned. Review how data moves through connected tools as well as what the runtime can see. OWASP’s MCP guidance also recommends sandboxing local servers, restricting directories, and disabling network access when it is unnecessary; in MCP deployments, treat servers as separate trust domains and monitor changes to their tool definitions.
6. Put a specific approval gate in front of consequential actions
Classify operations by impact and reversibility. A controlled read-only lookup may not need an approval for every call. Deleting data, changing permissions, sending an external message, purchasing, deploying, or exporting sensitive records warrants additional validation or fresh human approval.
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Show the approver the actual proposed action and its parameters: for example, which records will be changed, what message will be sent, or what amount will be spent. Bind approval to that specific action rather than treating a general instruction or earlier consent as permission for later actions. The execution path must enforce the approval; the agent should not be able to skip it by generating a different response or invoking another tool.
7. Log, review, and test the boundaries
Record tool calls and denied attempts with enough identity and scope context to investigate them, while avoiding plaintext secrets and unnecessary sensitive payloads. Review identities and permissions periodically, remove unused access, and monitor for behavior outside the intended workflow.
Test realistic abuse cases before deployment and after changes to tools, credentials, prompts, or policies. Include prompt injection in a document or other untrusted content, path traversal, an unauthorized write, data exfiltration, and an attempt to bypass an approval step. OWASP recommends adversarial testing and release checks when high-risk tool policies or scopes change. Narrow permissions reduce the possible impact of prompt injection or other misuse; they do not guarantee the agent will behave correctly.
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How to choose the right access level
Choose the least capable arrangement that completes the business task. Assess operation, resource scope, environment, identity, and impact together rather than treating access as simply enabled or disabled.
| Control dimension | Narrower arrangement | Broader arrangement | Practical decision |
|---|---|---|---|
| Operation | Read-only or constrained write | Unrestricted write | Grant write only when the workflow requires state changes; separate it from read access where feasible. |
| Resource scope | Specific paths, record sets, or approved endpoints | Broad directories, datasets, or endpoints | Identify the smallest usable scope for each tool, not just for the agent overall. |
| Environment | Curated trusted data and restricted execution | Untrusted web or user-supplied content with broad execution access | Account for the possibility that external content can try to influence tool use. |
| Identity and credentials | Distinct identity, narrow scopes, and short-lived grants where supported | Shared account or broad standing credentials | Use separate identities and credentials by workload or tool when practical. |
| Impact and oversight | Reversible, low-impact calls with proportionate checks | Destructive, financial, or externally visible actions without a separate gate | Require specific validation or fresh approval as impact rises. |
NIST CAISI’s examples illustrate how context changes a classification: it places retrieval-augmented generation in a read-only, trusted-environment category; application-specific GUI or API use as constrained write in a trusted environment; and computer use as write in an untrusted environment. These are illustrative placements in its taxonomy, not classifications that apply to every implementation.
What access controls can—and cannot—guarantee
- Prompt instructions are not authorization. They can guide behavior, but the connected system must decide whether a requested operation is permitted.
- A valid request is not necessarily an authorized one. Schema checks help validate form; the service still needs to check identity, resource, operation, and scope.
- A sandbox only limits what it does not expose. Code in the environment can access the files, credentials, and network made available there.
- Least privilege limits blast radius, not every failure. An agent may still misuse an allowed capability, so consequential actions need appropriate checks and monitoring.
- More restrictions can also constrain a useful workflow. Establish the actual task requirements, then grant narrow permissions deliberately rather than adding broad access preemptively.
The guidance here draws on OWASP’s AI Agent and MCP Security Cheat Sheets, NIST CAISI’s “Lessons Learned from the Consortium: Tool Use in Agent Systems” (released August 5, 2025; updated August 7, 2025), AWS Prescriptive Guidance, OpenAI’s sandbox security documentation, and Microsoft Learn’s identity and least-privilege guidance (last updated August 1, 2026). Platform-specific setup and available controls vary, so apply each vendor’s examples only to the services they describe.
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