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Enable least-privilege tool access for every agent, validate every tool call outside the model, and require action-specific human approval before high-impact or hard-to-reverse operations. Enforce authorization in application code or the downstream service—not in the prompt. Then add output validation, audit records, rate limits, and recovery controls to reduce the impact of mistakes.
Start with controls that apply to every tool-using agent
Allow only the tools the task requires
Use an explicit tool allowlist and expose the narrowest useful operations. If an agent only needs to look up a record, do not give it a general-purpose database writer, shell, or unrestricted URL-fetching capability. Prefer specific functions with bounded inputs over open-ended execution. This reduces excessive functionality and limits the damage a compromised or mistaken agent can cause. OWASP’s Excessive Agency guidance describes excessive functionality, permissions, and autonomy as common causes of risk.
Enforce authorization at the execution boundary
A prompt may describe what an agent should do, but it is not an authorization mechanism. Before executing a tool call, check the actor, tool, target, arguments, and permitted scope in application code or the downstream system. Apply this check to every side-effecting call, including calls made after another tool returns. OWASP’s AI Agent Security Cheat Sheet recommends complete mediation, while its prompt-injection guidance emphasizes controls outside the model.
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Use the user’s identity and narrow delegated scope
When an agent acts for a user, execute actions in that user’s authorization context and grant only the scopes needed for the task. Avoid routing user requests through a generic privileged identity: doing so can let the agent exercise permissions the user does not have.
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Require approval according to action risk
Do not interrupt users for every harmless read or routine operation. Add a human approval pause before actions that are high-impact, destructive, financial, administrative, externally visible, or difficult to reverse. Read-only inspection can often proceed unattended; a permission change such as granting an IAM role warrants stronger review. OWASP Cornucopia AAI9 recommends classifying configuration and permission changes by reversibility and applying change-management controls to agent actions.
Bind approval to the exact proposed action
An approval should authorize one specific operation, not give the agent general permission to act. Bind it to the actor, tool, target, normalized parameters, time, and expiry. If any material part of the proposed action changes after approval, require a new approval. This keeps a confirmation for one target or amount from being reused for another.
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Keep read and write capabilities separate
Where reading does not require mutation rights, provide separate read-only tools or credentials. Gate sensitive writes—especially configuration and permission changes—independently, rather than assuming that a read permission or an earlier approval covers them.
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Validate tool arguments before execution
Check arguments against a schema and destination-specific policy before calling the tool. Validate not only syntax but also whether the actor may perform the requested operation on that target, whether the scope is allowed, and whether values fall within permitted bounds. Keyword filtering or a well-written system prompt cannot replace these checks.
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Treat retrieved documents and tool results as untrusted
External documents and tool output can contain instructions designed to influence the agent. Treat that content as data, not as authorization. Keep permission decisions outside the model, and validate any action the agent proposes after processing retrieved material. OWASP describes filters and structured prompts as useful layers, not a complete defense against prompt injection.
Check generated output before it reaches another system
Validate structured output against its schema and the receiving system’s security rules. Apply appropriate filtering when output could expose sensitive information. A response that looks valid to the model may still be unsafe or inappropriate for its destination.
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Know where framework checks do—and do not—run
Framework guardrails can help with automatic checks and human review, but their coverage depends on where they are attached. OpenAI’s guardrails and human review documentation says input checks run only for the first agent in a chain, output checks only for the final-output agent, and tool checks only for function tools with checks attached. For custom tools and side effects, put validation at or immediately next to the tool execution boundary; do not assume an agent-level check covers every call.
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Set action and rate limits
Bound the number of calls and the scope of actions the agent can perform. Rate limits and scope limits can reduce the scale of repeated mistakes, but they do not decide whether an action is authorized.
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Audit decisions and outcomes
Record relevant authorization decisions, approvals, tool calls, and outcomes so operators can investigate what happened. In consequential workflows, provide a way to interrupt execution and roll back changes where the system supports it. Logging, interruption, and recovery are response controls; none substitutes for preventive authorization.
Fail closed when required checks are unavailable
If policy lookup, risk classification, required approval, or audit enforcement fails, do not execute the consequential action. Continuing because a check is temporarily unavailable turns an intended safeguard into a bypass.
Choose guardrails by impact, coverage, and operating cost
Assess each control against the action’s impact and reversibility, the agent’s permission scope, where enforcement runs, whether approval covers the exact action, what happens when a check fails, and the operational burden. Apply stronger checks to tool calls with side effects, external-content ingestion, and sensitive outputs than to routine low-risk paths. Checks add latency and cost, while frequent approval prompts can cause fatigue; tune the interruption threshold to risk instead of treating every action alike. Official guidance supports these design principles and threat scenarios, but does not establish a quantitative effectiveness ranking or show that any one control eliminates prompt injection.
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