The reliable way to stop an AI agent from taking an unauthorized action is to enforce limits outside the model: restrict its tools and permissions, check every call at the execution boundary, and require approval for consequential operations. Prompts can steer an agent, but they should not be the control that decides whether a high-impact action is allowed to happen.
What counts as a wrong action?
A wrong action is more than a model making a factual mistake. It can be an operation the user did not request, an action taken on the wrong resource, or an otherwise valid operation performed without the required authority or approval. Causes include a misunderstood task, overly broad access, model error, and malicious instructions hidden in content the agent reads.
That last case is often called agent hijacking: an email, file, or web page contains instructions intended to redirect the agent. NIST describes this risk in its January 2025 discussion of agent-hijacking evaluations. OWASP also identifies risks such as prompt injection, tool abuse, data exfiltration, goal hijacking, and excessive autonomy in its AI Agent Security Cheat Sheet.
Put safeguards around the agent, not just in its prompt
Use defense in depth. Prompts and content filters can guide or flag behavior; permissions and authorization can block disallowed calls; human approval can pause consequential operations for review; and monitoring and recovery controls can limit damage. None of these layers alone guarantees that an agent will never act incorrectly.
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1. Give the agent only the tools and access it needs
Start by narrowing the agent’s capabilities. Scope access to the relevant resources and separate read from write permissions. Prefer small, specific functions over broad shell access, unrestricted URL fetching, or an all-purpose mailbox connection.
For example, a mail summarizer that only needs to read messages should not have functions to send or delete them. OWASP’s guidance on excessive agency explains why unnecessary tools and permissions increase the consequences of mistakes or misuse.
2. Check every action where it executes
Do not rely on the model to decide whether its own proposed action is permitted. At the tool wrapper or downstream service, validate the actor, operation, target resource, and permission every time a request is made. This is important even if the agent has already been instructed to follow a policy: the authorization check must independently reject calls outside that policy.
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OWASP recommends downstream authorization and complete mediation in its excessive-agency guidance. In practice, the check belongs in the component that can actually allow or deny the operation, not only in the conversation prompt.
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3. Require approval according to the consequences
Not every action needs the same friction. A read-only lookup within an approved scope may proceed automatically. Sending a message externally, spending money, deleting data, changing permissions, or modifying production should trigger an explicit approval step.
Show the reviewer the exact action, including its target and parameters, before execution. Bind the approval to the actor, tool, target, normalized parameters, time, and expiry; otherwise, an approval for one operation might be reused for a different one. If approval validation, policy checks, or required audit logging fails, fail closed rather than executing anyway. OWASP’s cheat sheet states: “Require explicit approval for high-impact or irreversible actions.”
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4. Treat retrieved content as data, not authority
Instructions inside an email, web page, or document should not automatically gain the authority of the user who asked the agent to process that content. Keep task instructions specific, avoid exposing data the task does not require, and screen proposed tool calls against the original user request.
OWASP’s prompt-injection prevention guidance describes architectural measures such as isolating untrusted content in a parser without tool access and tracking data capabilities. OpenAI also discusses the challenge of prompt injections. These defenses can reduce risk, but model-based guardrails remain vulnerable; they should supplement, not replace, permission and authorization checks.
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Validate structured tool arguments, limit scope and request rates, and bound retries, chain depth, and token or cost budgets. Record tool activity so an operator can see what the agent attempted and what actually ran. Provide a way to interrupt execution, and support rollback where the underlying operation makes it possible.
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Monitoring and rate limits can contain damage or help reveal a failure, but they do not guarantee prevention. OWASP discusses these controls in its AI Agent Security Cheat Sheet and excessive-agency guidance.
6. Test attacks and ordinary failure cases
Test the agent with malicious instructions embedded in retrieved documents, emails, and web pages. Also test tool misuse, multi-step action chains, repeated attempts, and task-specific outcomes. NIST CAISI’s January 17, 2025 guidance recommends adapting evaluations as defenses change, examining task-specific attack performance, and testing across multiple attempts.
Evaluation results describe performance under the tested conditions; they do not establish that an agent can never take a wrong action. No general incident or prompt-injection prevalence percentage is established by the cited guidance.
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How to choose controls for your agent
When reviewing an agent or its implementation, compare safeguards by where they are enforced and what failure they address. A prompt can influence behavior, but it cannot substitute for a service-side permission check. A human gate is useful for high-consequence operations, while logs and interruption controls help operators investigate or contain trouble.
- Enforcement point: Is the control only in a prompt or model filter, or can the tool wrapper or downstream service reject the operation?
- Scope: Are tools, data, and permissions limited to the resources and operations the task requires?
- Approval trigger: Which consequences require review, and does the reviewer see the exact operation before it runs?
- Approval binding: Is approval tied to the specific actor, target, parameters, and validity period?
- Observability and recovery: Are calls logged, execution interruptible, and operations reversible where possible?
- Operational cost: What latency or user effort do checks add, and is that proportionate to the possible harm?
There is no single safeguard that makes an agent safe in every situation. The practical objective is to make unauthorized operations impossible at the enforcement boundary, pause risky operations for review, and make failures easier to detect and contain.
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