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Why a better prompt is not enough
An AI agent can encounter instructions in a web page, document, email, or tool result that conflict with the user’s request. These are forms of indirect prompt injection: the agent may treat untrusted content as instructions. Even if a model recognizes an attack most of the time, a prompt or model-based guardrail cannot reliably serve as the security boundary.
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The risk grows when an agent has more authority than its task needs. For example, an agent asked only to find an email could become a route for exposing private information if its mail tool can also send messages and malicious content persuades it to forward data. OWASP identifies excessive functionality, permissions, and autonomy as agent risks, alongside data exposure, tool abuse, and goal hijacking.
Use layered controls
Build controls so that a missed prompt-injection attempt still cannot exceed the agent’s permitted authority. The model may propose an action; a trusted tool gateway or downstream service should decide whether that action is allowed.
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| Control | What it limits | What to verify |
|---|---|---|
| Capability and permission limits | Which tools, operations, resources, and credentials the agent can use | Tools and access are limited to the task; read and write permissions are separated where practical. |
| Authorization at execution | Whether a particular request is permitted when it reaches the system that will carry it out | Every request is checked against the user, tool, resource, operation, arguments, and applicable policy. |
| Human approval | Consequential actions that warrant a person’s decision before execution | The reviewer sees the actual action, target, and relevant parameters—not just the agent’s explanation. |
| Isolation | What code or tools can reach in the filesystem and network | Execution is contained, with access limited to the paths and destinations the task requires. |
| Monitoring and rate limits | How quickly or quietly an unexpected action can cause damage | Operators can inspect tool activity and downstream effects and respond to anomalies. |
Set permissions according to the action’s risk
Inventory what the agent can do before connecting it to real data or services. For each tool, record its operations, accessible resources, credentials, and downstream effects. Classify actions by impact and reversibility. OWASP’s examples below are illustrative, not a universal regulatory standard.
| Example action | Illustrative risk level |
|---|---|
| Search documents or read a file | Low |
| Write a file | Medium |
| Send email or execute code | High |
| Delete database records or transfer funds | Critical |
Use that inventory to remove permissions the task does not need. Prefer a specific read-only email lookup or a constrained file-writing function over a general-purpose shell or an extension that combines broad read and write powers. Restrict access to particular mailboxes, records, repositories, or database tables. Where possible, connect downstream systems with the requesting user’s identity and minimum necessary authorization rather than a shared, high-privilege account.
Enforce authorization where actions execute
Check each proposed tool request at the trusted gateway or service that will execute it. Apply policy to the requesting user, tool, resource, operation, and arguments; do not treat the model’s confidence, rationale, or instructions found in retrieved content as proof of permission. This is complete mediation: each downstream request must be validated, not just the initial user prompt.
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OWASP’s LLM06:2025 Excessive Agency guidance puts the principle plainly: “Implement authorization in downstream systems rather than relying on an LLM to decide if an action is allowed or not.” A model can help identify or explain a proposed action, but it should not grant itself authority.
Put meaningful approval gates around consequential actions
Require a human decision before actions such as deleting data, sending or publishing information, transferring funds, changing access, or deploying to production. The approval request should identify what will happen, to which target, with which parameters, and what information will leave the system. Bind approval to that specific action so a materially changed request needs a fresh decision.
Fail closed if risk classification, policy lookup, approval validation, or required audit logging fails: do not execute the action while the check is unavailable. Avoid asking for approval on every trivial step. Repeated, vague prompts encourage approval fatigue; reserve review for consequential or uncertain operations and make the request understandable enough to judge.
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Treat retrieved content as data, not authority
Web pages, documents, emails, tool results, and messages from other agents can contain instructions aimed at the model. They do not have authority to change the user’s task or grant new access. Before executing a proposed action, check whether it is actually needed for the original request and whether its target and arguments fit that request.
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Run code and terminal operations inside an OS-level sandbox, container, or comparable execution boundary. Restrict filesystem access to the workspace and network access to necessary destinations; keep sensitive files outside the agent’s accessible area when possible. A narrow tool set is safer than giving an agent general access and hoping it chooses not to use it.
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Some products provide product-specific boundaries. Microsoft’s VS Code documentation describes workspace-limited access, temporary session permissions, a tool picker, and agent sandboxing. It advises using sandboxing or a dev container when prompt injection is a concern rather than relying on auto-approval rules alone. These are VS Code-specific capabilities; do not assume another agent product has the same controls.
Log activity, limit damage, and test attacks
Keep records of tool invocations and the effects they produce in downstream systems. Monitor for unexpected access or action patterns, and apply rate limits that can slow damage while an incident is investigated. Logs and rate limits improve visibility and containment; they do not prevent an unauthorized request from succeeding if the execution path permits it.
Test the boundaries before relying on an agent for consequential work. Include adversarial cases such as malicious instructions embedded in a document, attempts to send or delete information, manipulated tool arguments, and requests to access another user’s resources. Confirm that the request is blocked at the tool or service boundary, that approval is required where intended, and that the event is visible to operators.
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