Prompt injection becomes a serious problem when the AI system it targets can do something, not just say something. An agent that can read web pages, open email, or call a write-capable API can be steered by instructions hidden in that content into a tool call the user never requested. The core defense is structural: the model may propose an action, but a separate execution layer must decide whether that specific actor may perform that specific action, and the agent should hold only the data and tools its current task needs.
What prompt injection is
OpenAI defines prompt injection as a third party misleading a model by inserting malicious instructions into the conversation context. Its safety guidance, Understanding prompt injections, puts it this way: “Prompt injection is a type of social engineering attack specific to conversational AI.” The attack targets the model’s willingness to follow text, not a bug in the software that runs around it.
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Where the malicious instruction arrives
Many people assume the attacker must type into the chat. For agents, that is not the only route. NIST describes agent hijacking as a form of indirect prompt injection, in which malicious instructions are placed in data the agent ingests and lead it toward unintended, harmful actions. The data can come from internet pages, documents, or email that the user has asked the agent to process.
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Consider a simplified case. A user asks an agent to summarize a supplier’s web page. Hidden in the page is text telling the agent to forward the user’s recent files to an outside address. The agent reads the page as part of its job and treats the text as part of the material. Whether that text becomes a real action depends entirely on what the agent can do next:
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- Read-only agent, no outbound tools. A successful injection can distort the summary or leak what the model has already seen into the answer, but it cannot send anything on its own.
- Agent with an email send tool and no checks. The injected instruction can influence a send call, and the user may see nothing until after the message has gone.
- Agent with an email send tool and an execution-layer check plus user confirmation. The model can still propose the send, but the action is blocked or paused until a request that is bound to the user and the exact recipient and content is approved.
Not every injection leads to tool misuse, and not every injected instruction is hidden from view. The point of the example is that the consequence is set by the tool pathway, not by the wording of the injection.
The three boundaries to keep separate
Most confusion about agent security comes from blurring three things:
- Untrusted content. Anything the agent reads from outside the user’s direct request: web pages, files, emails, tool outputs, and retrieved documents. None of it carries authority just because it is text.
- The model’s decision. The model interprets the content and proposes a response, which may include a tool call with arguments. This is where instruction-following happens, and it can be manipulated.
- The authorized action. The tool execution component decides whether the proposed call may run, for this actor, on this resource, under the current policy. This decision should not be derived from the model’s output.
The risk grows when the boundary between the second and third items disappears, for example when a tool simply runs whatever arguments the model produces.
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Why tools change the stakes
A text-only model that is manipulated gives a bad answer. A tool-using agent that is manipulated gives bad arguments to a function that acts on real systems. OWASP’s agent security guidance lists prompt injection, tool abuse or privilege escalation, and data exfiltration among the main risks, and its LLM06:2025 Excessive Agency entry addresses the case where an agent holds more tool permissions than it needs. Excessive agency gives a compromised or manipulated agent more room to act.
When reviewing a tool, ask the following questions. None of them produces a score; each one identifies where a manipulated agent could cause harm.
| Review question | Why it matters |
|---|---|
| What data can the agent reach, and does the task need all of it? | Data the agent cannot read cannot be leaked through its answers or its outbound calls. |
| Can the tool read, write, execute code, or transmit data outside the system? | Write, execute, and transmit actions are the ones that produce changes outside the conversation. |
| Is authorization enforced independently at execution time? | If the tool trusts the model’s request as permission, a manipulated request is treated as legitimate. |
| Does a sensitive action require confirmation from the user? | A confirmation shows the user the exact action before it runs, which can catch an action the user did not intend. |
| Does the tool run inside a sandbox? | A sandbox limits what generated code or a tool can change on the host and connected systems if it is misused. |
The Model Context Protocol risk catalog maintained by OWASP (MCP05:2025, Command Injection and Execution) covers a related failure: untrusted input reaching a command or code execution path. Its suggested mitigation is an allowlist of permitted commands and operations, rather than trying to filter every dangerous string.
Put authorization in the execution layer
OWASP’s AI Agent Security Cheat Sheet places authorization checks at execution. In practice, a tool implementation should do the following before running any action:
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Identify the actor. Use the identity of the user or service on whose behalf the call is made, not a shared agent identity with broad rights.
- Identify the action and target. Record the tool name, operation, and resource, such as a specific mailbox folder, record, or recipient.
- Check policy in code outside the model. Compare the request against permissions for that actor and action. A model-generated justification, a classifier score, or a “this looks safe” label should not count as permission.
- Require confirmation for sensitive operations. Show the user the exact parameters, such as recipient, amount, or file set, and run the action only after an explicit approval tied to that request.
- Log the decision. Keep the requested action, the check result, and any approval, so that unexpected calls can be traced and reviewed.
OpenAI’s guidance on prompt injection, published in 2025 as Understanding prompt injections: a frontier security challenge, gives examples of sandboxing and user confirmation for consequential actions, and it also stresses limiting access and giving the model explicit task instructions. These are complementary layers, and none replaces the execution check.
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Practical controls for a deployed agent
- Grant only the data and tools needed for a clearly scoped task. A summarization agent rarely needs send rights. Use read-only credentials where the platform allows them.
- Separate read access from write, execute, or transmit actions. Where the architecture permits, put consequential actions in a separate tool with its own checks, rather than one general-purpose tool.
- Require human review before sensitive actions. Typical examples include sending information to an outside party or completing a purchase.
- Sandbox code and tools that could make harmful changes. Run generated code in an isolated environment without production credentials.
- Allowlist commands and operations. Enumerate what the tool may do rather than relying on pattern filters that attackers can rephrase around.
Testing indirect injection safely
Tests should reproduce the path that untrusted content takes into the agent, not only a direct message typed into the chat. OWASP’s LLM Prompt Injection Prevention guidance recommends safe test setups that use dummy data and sandboxed tool substitutes. In practice:
- Plant test instructions in the same channels the agent reads in production, such as a page it fetches, a document it opens, or an email in a test mailbox.
- Use dummy records and fake recipients. Replace real send, write, and payment tools with substitutes that record the call and return a harmless result.
- Write test cases around the application’s own tasks and permissions. A test that asks an agent to exfiltrate data it cannot access proves little; a test that targets the tools it actually holds does.
- Re-run the suite after any change to tool permissions, new data sources, or model updates.
What these controls do not guarantee
The steps above reduce the chance that an injection succeeds and limit how much damage a successful one can do. They do not make prompt injection impossible. No single prompt wording, classifier, or confirmation dialog eliminates the problem, and the cited guidance does not claim complete prevention. That is why the controls are layered: scoped permissions, an independent authorization check, confirmation for consequential actions, sandboxing, and testing through the real input channels each cover a different failure. If one layer fails, the others limit what the manipulated agent can reach.
Keep in mind the limits of the evidence as well. The guidance above describes the risk and the controls; it does not provide a measured prevalence of successful injections in deployed systems, so any estimate of how often this happens in a given product should come from testing that product.
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