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Why agents can take unintended actions
An AI agent may do more than generate text: its tools can let it call APIs, run code, access data, or change resources. OpenAI’s developer quickstart describes agents using built-in and custom tools, including functions that call APIs or run code. The more authority those tools grant, the more a mistaken decision or misleading input can affect.
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Prompt injection is one example: instructions embedded in material the agent reads may try to redirect its behavior. Google Cloud also identifies insecure tool chaining and naive error handling as risks. No single prompt-level instruction should be treated as a complete defense.
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Limit what the agent can access and do
Grant only task-specific permissions
Use an agent identity where appropriate, and grant it only the roles and permissions required for its task. Narrow both the tools it can call and the data or resources those tools can reach. OWASP’s AI Agent Security Cheat Sheet advises: “Grant agents the minimum tools required for their specific task.”
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For example, an agent summarizing documents does not necessarily need permission to edit or delete them. An agent preparing a change can often draft it without being able to apply it. OpenAI likewise advises limiting an agent’s access to the data it needs in its prompt injection guidance.
Separate read access from write access
When the task is analysis, prefer read-only tools. If the agent must make changes, use distinct write-capable tools and limit their scope. OpenAI’s Evals API reference documents MCP approval settings, including filters based on read-only annotations and tool names. These are configurable platform controls, not universal settings for every agent framework.
Enforce authorization before a tool runs
Do not rely on the model’s reasoning or a prompt to decide whether an action is allowed. A trusted policy service or execution component should independently check the requested tool, its parameters, target resource, permitted scope, and whether required approval has been granted. OWASP recommends that an agent may propose an action while a separate component validates it before execution.
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This matters because a model can propose an action that is mistaken, unnecessary, or prompted by hostile content. The component that actually authorizes execution—not merely the model’s explanation—must enforce the limits.
Require approval for consequential actions
Set an approval gate for high-impact or destructive operations, such as publishing or making consequential changes to resources. Before approving, a reviewer should be able to inspect the actual action: what will change, which target it affects, and the relevant parameters. An approval request that hides those details is difficult to evaluate.
Human review reduces risk but does not remove it. Google Cloud cautions that a reviewer may approve a destructive action without properly verifying that it is safe. Its AI security and safety guidance also notes that MCP servers can access data and perform actions that may cause non-reversible changes.
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Protect the agent from untrusted content
Assume documents, messages, web pages, and other material the agent processes could contain instructions intended to manipulate it. Limit the agent’s access to sensitive data and tools, and use sandboxing or other isolation appropriate to the task. Anthropic’s trustworthy-agent framework treats prompt injection as a risk that needs defenses at multiple levels, alongside principles such as human control, transparency, and privacy.
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Match safeguards to the possible consequences
Choose controls according to what the agent can reach and what could happen if it is wrong. An agent limited to read-only work generally presents a different level of risk from one that can alter production resources, accounts, or files. Consider these checks when configuring an agent:
- Tool and data scope: Can it reach only the tools and resources needed for this task?
- Independent enforcement: Does a trusted component validate the action and its target before execution?
- Approval: Which actions require review, and can the reviewer see the complete proposed change?
- Untrusted input: Are the agent’s access and execution environment constrained when it processes outside content?
- Impact and reversibility: How serious would an error be, and can the action be undone?
There is no universal configuration established by these sources; the appropriate controls depend on the agent, its tools, and the consequences of its actions. OWASP’s LLM06:2025 Excessive Agency guidance highlights unnecessary permissions and extensions as a source of risk and recommends approvals for high-impact actions.
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