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Imagine a mail-reading AI agent encounters an email containing hostile instructions and tries to forward sensitive information or send a message. This is an illustrative attack path, not a reported incident: OWASP describes how indirect prompt injection can exploit an agent that can both read and send email. The reliable safeguard is not asking the model to promise it will check first. The application that executes each action must independently decide whether that exact action is authorized.
Why an AI agent can act before you approve
An AI agent is more than a model generating text. It combines a model with software that lets it perceive information, plan, call tools, and affect an environment. Once it can send email, modify files, deploy code, or operate a business system, the central security question changes from “Is this answer good?” to “Is this action authorized?” NIST’s August 5, 2025 account of workshop-derived agent taxonomies describes this broader role; its frameworks are approaches to tailor, not a universal standard. NIST: Lessons Learned from the Consortium: Tool Use in Agent Systems
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OWASP calls out excessive agency as a risk with three practical roots: too much functionality, too much permission, and too much autonomy. Indirect prompt injection can place hostile instructions in content an agent reads; hallucinations can produce unintended choices; and compromised extensions can add or exploit capabilities. These are different triggers, but their consequences grow when an agent has broad access and can act without an independent authorization check. OWASP LLM06:2025: Excessive Agency
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A model’s promise to ask is not an approval boundary
A model can be instructed to ask before sending, deleting, paying, deploying, or changing privileges. That instruction may help guide behavior, but it is not enforcement: the model can misunderstand a request, be influenced by hostile content, or produce a tool call that bypasses its own stated intention. The application must mediate every tool call, and the component executing the call must enforce authorization. OWASP puts it plainly: “Enforce authorization in the execution component, outside the agent’s context.” — OWASP, AI Agent Security Cheat Sheet. Read the OWASP AI Agent Security Cheat Sheet
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For an action requiring review, approval should describe and authorize the real operation—not merely a natural-language summary shown to a person. The policy executor should bind the decision to the actor, exact tool, target, and normalized parameters. If any material parameter changes, require fresh approval. Reject missing, invalid, expired, or replayed authorization rather than allowing the action by default. OWASP recommends exact-action approval records, short-lived authorization artifacts, replay protection, and fail-closed behavior for critical actions.
Use explicit gates for actions that can have consequential or externally visible effects, such as deletion, sending messages, payments, deployments, and privilege changes. The approver should be able to see what will happen and to which target, while the executing component checks that the approved action still matches the requested one.
Containment controls to put around an agent
Apply controls in layers: reduce what the agent can do, limit the authority behind those actions, and ensure a separate policy check stands between a tool call and its effects.
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1. Remove unnecessary capabilities
- Expose only the tools needed for the task; do not give a read-only assistant write-capable tools by default.
- Prefer narrow functions with specific inputs over open-ended shell or URL tools when practical.
- Minimize extensions and integrations, since each added capability can expand the paths an agent may take.
These controls address excessive functionality, one of the roots OWASP identifies for excessive agency. OWASP LLM06:2025: Excessive Agency
2. Limit identity and resource scope
- Give downstream tools least-privilege identities and only the resource-level read or write scopes the task requires.
- Where possible, execute in the user’s authorization context instead of using a broad shared privileged identity.
- Keep credentials out of the agent’s general reach and scope them to the systems and operations actually needed.
A narrow tool is not enough if it runs with excessive authority. OWASP recommends minimizing permissions and extensions and using the user’s context; its agent guidance also emphasizes authorization at the execution boundary. OWASP LLM06:2025: Excessive Agency OWASP AI Agent Security Cheat Sheet
3. Enforce an independent action policy
- Classify each proposed action independently of the model’s own description of its risk.
- At execution time, check the actor, tool, target, and normalized parameters against policy and any approval record.
- Require new approval when the target or any material parameter changes; fail closed if the check cannot be completed.
This is the human approval for AI actions that matters: approval is enforced by the system that can permit or reject the tool call, not by the agent’s conversational behavior.
4. Isolate coding agents
Sandboxing a coding agent limits the damage a mistaken or manipulated command can cause. Run commands in a restricted shell, development container, virtual machine, or ephemeral workspace. Narrow filesystem access, restrict outbound network access where feasible, and scope credentials. Review material code or configuration changes before they are merged or deployed. OWASP’s prompt-injection and secure-coding guidance recommends layered defenses; sandboxing reduces impact but does not guarantee prompt-injection prevention.
OWASP LLM Prompt Injection Prevention Cheat Sheet OWASP Secure Coding with AI Cheat Sheet
5. Make actions visible and recoverable
- Log policy decisions and tool actions, including enough detail to understand what target and parameters were used.
- Monitor downstream effects, and use rate limits where they can constrain the speed or volume of harm.
- Maintain a way to revoke credentials or stop execution when something goes wrong.
Logging, monitoring, and rate limits help detect or limit damage; they do not replace preventive authorization at the execution boundary. OWASP’s agent guidance covers authorization and security controls for agent tool use. OWASP AI Agent Security Cheat Sheet
6. Validate the controls against attack paths
Test whether indirect prompt injection can trigger an action, whether a tool has broader reach than intended, and whether changing a tool definition or approval parameter can evade policy. Validate that missing, expired, changed, or replayed approvals are rejected. OWASP recommends adversarial testing and validation for agent security and prompt-injection defenses. OWASP AI Agent Security Cheat Sheet OWASP LLM Prompt Injection Prevention Cheat Sheet
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide which actions need approval
There is no universal label that makes an action safe or dangerous across all deployments. NIST’s taxonomy points to relevant factors including functionality, access patterns, action risk, reliability, modality, monitoring, and autonomy; the risk classification depends on the tool implementation and deployment conditions. Consider what an action can change, where it runs, whether it is reversible or stateful, and how observable its effects are. NIST: Lessons Learned from the Consortium: Tool Use in Agent Systems
When evaluating an agent platform or deployment design, compare these controls rather than relying on a vendor’s “human in the loop” label:
- Read versus write capability, and the scope of accessible resources.
- User-context identity versus shared privileged credentials.
- Whether approval is enforced outside the model and tied to exact action parameters.
- Sandbox isolation, filesystem controls, and network egress restrictions.
- Action logging and visibility into downstream effects.
- Action reversibility, persistence, and potential impact.
- How adversarial validation is performed.
As a practical autonomy rule, let low-impact, reversible, well-scoped actions proceed under policy. Require explicit review for consequential, irreversible, external, or security-sensitive actions. Set the threshold for the particular deployment, tool, and environment rather than assuming one approval rule fits every agent.
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