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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchKeep an AI agent’s authority outside the model: limit its tools and data, check every proposed action against the user’s current permissions, and require fresh approval for consequential operations. Instructions can define the task, but they cannot reliably enforce its limits—especially when the agent reads untrusted emails, webpages, or documents.
What actually enforces an agent’s boundaries?
An agent can propose an action; it should not be able to grant itself permission to perform it. Put authorization checks in the tool-execution path and, where possible, in the systems the agent acts on. OWASP puts it plainly: “Enforce authorization in the execution component, outside the agent’s context.” (OWASP AI Agent Security Cheat Sheet.)
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This distinction matters because an agent may encounter instructions embedded in material it is asked to process. A malicious webpage, email, document, or tool response could try to redirect the task or induce an action. Treat that content as data, not as a source of authority. OpenAI recommends limiting access to the data needed for a task, while OWASP’s guidance focuses on enforcing permissions beyond the agent’s context (OpenAI: Understanding prompt injections; OWASP).
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There is no control that makes prompt injection impossible. The practical goal is to ensure that a successful attempt to manipulate the model cannot, by itself, unlock a sensitive tool, expand the task’s scope, or bypass authorization. OpenAI and Anthropic both describe layered defenses without promising universal prevention (OpenAI: Designing AI agents to resist prompt injection; Anthropic: Trustworthy agents in practice).
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Set the boundaries in seven steps
1. Define a narrow task contract
State the goal, which data is relevant, what actions are permitted, what is prohibited, and when the agent must stop or ask for help. Avoid open-ended instructions such as “take whatever action is needed”: broad delegation can make hidden malicious content more influential. The contract helps the agent understand the task, but execution controls must enforce it (OpenAI: Understanding prompt injections).
2. Give the agent only the capabilities it needs
Inventory the tools, resources, and operations available to the agent, then remove what the task does not require. Separate read access from writing, deleting, sending, and administration. Prefer a narrowly scoped function—such as writing one approved file—to an open-ended shell or generic connector. OWASP identifies excessive agency as a risk when systems grant agents more capability than their tasks require (OWASP: LLM06:2025 Excessive Agency).
3. Check identity and authorization on every action
At the point a tool call executes, validate the acting user or service identity, the task’s scope, the target resource, the requested operation, and the current policy. Do not rely on the model’s description of what it is allowed to do. If permissions have changed since the task began, use the current authorization rather than an earlier assumption.
4. Match controls to the action’s impact
Classify actions by their consequences and reversibility. A permitted, low-impact read may be suitable for automatic execution. Sending or publishing content, deleting data, making purchases, moving money, changing privileges, or exposing sensitive information usually calls for stronger checks and often human approval. This is a practical application of OWASP’s risk categories—destructive, financial, administrative, and externally visible actions—not a universal taxonomy or legal threshold (OWASP AI Agent Security Cheat Sheet; OWASP: Excessive Agency).
5. Bind approval to the exact action
An approval should show what will happen and identify the actor, tool, target, and parameters. Bind it to those details, make it expire, and prevent it from being replayed. If the target or parameters change, request approval again. A generic “user confirmed” flag is not sufficient because it may not establish which operation the person actually approved (OWASP AI Agent Security Cheat Sheet).
6. Keep untrusted content from becoming authority
Distinguish instructions from external data in the design of prompts and tool workflows. Validate structured inputs and outputs, and avoid pipelines in which untrusted text directly triggers a consequential downstream action. A prompt-injection detector can be one layer, but it should not be the only safeguard; permissions and approval gates must still hold if detection fails (OpenAI: Designing AI agents to resist prompt injection; OpenAI Developers: Safety in building agents).
7. Test the controls and limit the blast radius
Test realistic direct and indirect injection attempts, unauthorized tool choices, changed parameters after approval, repeated calls, data leakage, and multi-step workflows. Repeat attempts and adapt tests to the task: NIST CAISI’s January 2025 guidance says, “Evaluations need to be adaptive.” Its example evaluation used Claude 3.5 Sonnet, released in October 2024, so that experiment is not a current model ranking (NIST CAISI: Strengthening AI Agent Hijacking Evaluations).
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Choose an approval model that fits the workflow
Approval design is a trade-off between interruption and oversight. The right choice depends on action impact, how reversible it is, and whether a person can meaningfully review the proposed operation.
| Approval approach | Useful when | Main trade-off |
|---|---|---|
| Automatic execution within a narrow scope | Actions are low-impact, permitted in advance, and bounded by enforceable permissions. | Fewer interruptions, but relies on the scope and execution checks being correctly configured. |
| Approval for each consequential action | An action has external, financial, destructive, administrative, or sensitive-data consequences. | Provides a direct review point, but can interrupt a multi-step workflow frequently. |
| Plan-level approval with intervention during execution | A multi-step task can be reviewed as a proposed plan, while consequential steps remain subject to execution-time checks. | Can reduce repetitive prompts, but a changed action or target still needs renewed authorization. |
Anthropic’s examples distinguish lower-risk calendar reading from sending invitations and describe plan-level approval as one way to handle repetitive prompts in multi-step tasks. These are product examples, not a universal policy rule. Anthropic also reports that users interrupt more often on complex tasks and that Claude’s check-in rate roughly doubles, but the cited passage gives no exact percentages or denominator; treat the observation as directional, not as a numerical benchmark (Anthropic: Trustworthy agents in practice).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare designs by where they can fail
“More autonomy” is not a useful security measure by itself. Compare an agent design on the controls that determine whether a mistaken or manipulated action can reach the outside world.
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- Permission granularity: Does the agent have broad connector or shell access, or only specific resources and operations?
- Approval granularity: Does every action require approval, only actions that cross a risk threshold, or a plan-level review with intervention when execution changes?
- Failure containment: Can the agent reach production systems and sensitive data, or is it confined to lower-privilege environments with bounded actions, replay protection, logging, and rate limits?
- Evaluation quality: Are checks generic and one-off, or task-specific, repeated, adaptive adversarial tests with measurable outcomes?
These comparison axes synthesize guidance from OWASP, Anthropic, NIST CAISI, and OpenAI; they are not a complete standard declared by any one source. The sources offer implementation recommendations, not one legally binding boundary standard for every jurisdiction or deployment.
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Keep incident figures in context
OpenAI’s 2025 article describes an example attack from external security researchers that worked 50% of the time with the specific prompt and test described there. That is a result for that scenario, not a general prompt-injection success rate for AI agents (OpenAI: Designing AI agents to resist prompt injection).
OpenAI’s developer guidance page currently says Agent Builder is being deprecated. Avoid treating it as a stable, long-term implementation path without checking the current official documentation for the product or API you use (OpenAI Developers: Safety in building agents).
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