The Tool Desk
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What prompt injection is—and why the application matters
Direct and indirect attacks
Prompt injection is an attempt to manipulate a large language model through crafted input so that it follows an attacker’s intentions. In a direct attack, the attacker puts the instructions in a user message. In an indirect attack, the instructions arrive inside material the application asks the model to process: for example, a webpage, uploaded document, retrieved passage, or tool result.
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Indirect attacks are easy to overlook because the malicious text may look like ordinary content to a person—or may not be shown to a person at all. If the model reads it, it can still influence the model’s response or proposed actions. A document might contain text telling the assistant to disregard its task, reveal information, or use a connected capability. The risk is not limited to whether the text is visible in the chat.
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An injection does not automatically grant an attacker access to every system. Its possible impact depends on what the application exposes to the model: the data in its context, tools it can invoke, and permissions those tools carry. A model that can only summarize a public page has a different potential blast radius from an agent that can read private records or make changes in another system. Design security around the whole application, not just the wording of the prompt.
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Many LLM systems process instructions and data as natural language in the same context. That makes a delimiter or a carefully written system prompt useful as guidance, but unsuitable as the only security boundary. OWASP’s LLM01:2025 guidance states that “there is no fool-proof prevention within the LLM.” Treat prompt injection as a risk to manage through layered controls, not a string to block once and forget.
Start by mapping what the model can read and do
Inventory inputs, tools, data, and side effects
Before changing prompts or adding filters, trace the information and authority that flow through each model-powered workflow. Make an inventory for each feature, not just for the application as a whole:
- Inputs: user messages, uploaded files, retrieved documents, webpages, memory, and outputs returned by tools.
- Data: what information enters the model’s context, which records it can retrieve, and whether any of that information is sensitive.
- Tools: each capability available to the workflow and what it can do—not merely its display name.
- Consequences: actions that expose information, change a record, communicate externally, publish content, spend money, or are difficult to reverse.
- Authorization: which end user is allowed to access which data or request which operation, and where that decision is actually enforced.
For each route from input to action, ask what would happen if a hostile instruction appeared in that input. Could it cause the model to request data outside the user’s authority? Could it trigger a tool call? Could the application execute that call without independently checking the user, target, and operation? This exercise turns the abstract threat into specific trust boundaries and failure cases.
Classify tools by the authority they carry
Group capabilities by their real consequences. Reading public information is not equivalent to reading private information; drafting a message is not equivalent to sending it; proposing a deletion is not equivalent to carrying it out. The distinction should be reflected in the application’s permissions and execution path. If two tools have different risks, do not give them the same broad authority simply because one model workflow uses both.
This inventory is also a practical scope for testing. A workflow’s risk is shaped by its particular data, tools, and permission rules, so a generic “prompt injection test” is not enough to show that its important boundaries hold.
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Enforce least privilege outside the model
Make authorization a software decision
Give each workflow only the data access and permissions needed for its task. OWASP recommends restricting an LLM to the minimum access necessary for its intended operations. In practice, the model may help interpret a request or propose an action, but application or tool code should decide whether the current user is authorized to perform it.
Do not make a natural-language instruction such as “never access another user’s records” the mechanism that protects those records. Check access at the data or tool boundary, where the application can evaluate the user, requested operation, and target. A model’s explanation that a request is authorized is not authorization.
Scope access to the user and operation
Where possible, scope a request to the end user and the particular operation, rather than relying on broad access shared across unrelated tasks. A tool that retrieves information should not quietly inherit authority to change it. A workflow that needs to draft an action should not automatically receive the ability to execute it. Keep credentials and permission checks in the application or tool implementation; do not place secrets in a prompt and expect the model to protect them.
When the application has tools with different risk levels, enforce the applicable policy at each tool boundary. The model can select or suggest a tool, but that should not let it expand what the user or workflow is allowed to do. If an authorization check fails, the operation should not proceed merely because the model retries, changes its explanation, or presents the request as urgent.
Separate trusted instructions from untrusted content
Mark and handle outside material as data
Retrieved passages, uploaded files, webpages, and tool outputs should be treated as untrusted input. Keep them distinguishable from trusted application instructions in the workflow, and make clear in the system design that the content is material to analyze—not a source of authority to change permissions or redefine the task. This separation can help the model interpret the content, but it does not reliably neutralize hostile instructions by itself.
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RAG does not remove prompt-injection risk: retrieving content still places that content in a model workflow. Fine-tuning does not establish a security boundary either. Filtering input or output may be useful as one layer, but an attacker’s wording can vary and a filter cannot substitute for permission checks at the point where sensitive data is accessed or actions are executed.
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Consider quarantined processing for higher-risk content
For a workflow that must inspect potentially hostile material, one option is to process or classify it in a quarantined stage that has no tool access, then pass a constrained result to a more privileged stage. The purpose is to reduce the authority exposed while the content is being interpreted. This separation is a supporting measure, not a guarantee: the later stage still needs to treat derived content cautiously and enforce authorization independently.
Use such separation where it fits the architecture rather than assuming every application needs the same pipeline. The relevant question is whether it reduces the authority available to a model while untrusted content is being handled, and whether the later steps retain their own checks.
Put human approval at the action boundary
Require review before consequential operations
Require a human decision before privileged, high-impact, destructive, or irreversible operations. Depending on the application, examples might include sending or deleting messages, publishing material, making a purchase, or changing important records. These examples illustrate the control; the right approval threshold depends on what the specific workflow can do.
The reviewer should see the proposed action and its actual arguments: what will happen, to which target, and with what content or values. A vague notice that “the assistant wants to continue” is not a meaningful review if the person cannot inspect the operation they are approving.
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Make approval impossible to bypass through model output
Place the approval check in the code path that performs the operation. The application should not execute a consequential tool call until the required approval has been recorded and associated with the action being executed. If approval is only described in the prompt, a model can ignore or misunderstand the instruction. If the model can claim that approval was granted, that claim must not stand in for the application’s record of an actual approval.
Approval is a human control, not a replacement for least privilege. It is most useful when tied to a clearly presented action and enforced at execution. Routine actions that do not warrant a checkpoint can still require user-scoped permissions and other application-level checks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test the complete path with adversarial simulations
Cover direct, indirect, and tool-output injection
Test more than the visible chat box. Include hostile instructions in direct user input and in the external material the application processes: files, webpages, retrieved content, and tool results. Test the workflow from the point where content enters through the point where data is returned or an operation is executed. OWASP’s DevSecOps guidance specifically includes indirect injection through tool outputs in testing.
Use harmless test data and instrumented tool substitutes for simulations. The objective is to verify boundaries without accidentally exposing real data or performing real-world actions. For each scenario, check whether the application:
- keeps the model within the permissions intended for that workflow and end user;
- prevents unapproved access to sensitive or out-of-scope data;
- blocks tools or operations that the user is not authorized to invoke;
- requires the configured human approval before consequential execution; and
- continues to enforce those rules when malicious instructions arrive through external content or a tool output.
Test enforcement, not just the model’s stated intention
A model response saying “I will ignore those instructions” is not proof that the application is secure. Observe what the application actually retrieves, which tool calls it permits, and whether an action boundary rejects an unauthorized operation. A useful test demonstrates that an injected instruction cannot widen permissions or bypass approval, even if the model’s answer is confused or adversarial content influences its plan.
Repeat relevant tests when prompts, tools, data sources, or authorization rules change. Changes to any of these can alter the path from untrusted content to an action. OWASP recommends adversarial testing and simulations, but the guidance does not provide a universal success rate or measured efficacy comparison for these controls.
Choose controls by the boundary they protect
These measures address different failure points. Use them together rather than treating one as a substitute for the others.
| Control | What it addresses | Where it must hold | What it cannot guarantee alone |
|---|---|---|---|
| Content marking and separation | Helps distinguish retrieved, uploaded, or tool-returned material from trusted instructions. | In the workflow that passes external content to the model. | That the model will ignore every hostile instruction in the content. |
| Input/output filtering | Can identify or limit some risky content or responses as one layer. | At the points where content enters or leaves the model workflow. | Complete detection or protection from attacks phrased in other ways. |
| Least privilege | Limits the data and capabilities available to a workflow. | In application and tool permissions. | That an allowed capability will always be used appropriately. |
| Application authorization | Checks whether a particular user may access particular data or perform an operation. | At the data-access or tool-execution boundary. | That model instructions or explanations are trustworthy. |
| Human approval | Provides review of selected consequential actions before execution. | In the code path that performs the action, with its arguments visible to the reviewer. | Protection for actions that are not covered by the approval rule, or for checks implemented only in a prompt. |
| Adversarial testing | Checks how the deployed workflow behaves against direct and indirect attacks. | Across content ingestion, model handling, tools, authorization, and action execution. | A permanent guarantee when the system or its dependencies change. |
There is no quantitative comparison in the OWASP guidance establishing how much risk each control removes or which one is most effective in every deployment. The practical trade-off in review effort and operational burden also depends on the application. Prioritize boundaries according to the authority exposed and the consequences of an error, then verify those boundaries in the running system.
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- Trace one workflow end to end. Record every user input and external source the model reads, every data source it can reach, every tool it can request, and every operation with a side effect.
- Identify authority and sensitive data. For each tool and data source, determine what access is necessary for the task and what must remain unavailable to that workflow or user.
- Move security decisions into code. Implement user-scoped authorization and operation checks in the application or tool implementation. Do not rely on the model to police its own access.
- Reduce unnecessary capabilities. Remove tools and data access the workflow does not need. Separate read, draft, and execute capabilities where doing so limits the authority available at each stage.
- Mark outside content as untrusted. Keep retrieved, uploaded, and tool-returned material distinguishable from trusted instructions. Consider a no-tool quarantine stage for higher-risk content, without treating it as a complete defense.
- Gate consequential actions. Decide which operations need human review, show the reviewer the exact proposed action and arguments, and enforce approval in the code path that executes it.
- Simulate attacks safely. Use benign fixtures and instrumented tool substitutes to test direct prompts, external content, and tool outputs. Verify access checks and approval behavior by observing actual system actions, not only model text.
- Retest after relevant changes. Re-run scenarios when prompts, tools, data sources, or authorization rules change, since each can affect the trust boundaries.
What not to treat as a complete fix
- A stronger system prompt: useful for expressing intended behavior, but not an enforcement boundary.
- Delimiters around retrieved text: useful for signaling content boundaries, but not a guarantee that the model will treat the content as inert data.
- A regex, classifier, or second model: potentially useful as a filtering layer, but not proof that every injection will be found or that actions are authorized.
- RAG or fine-tuning: neither removes the risk created when the model processes untrusted content.
- Secrets in the prompt: do not depend on the model to keep credentials or sensitive information confidential; keep secrets out of model context where possible and protect access in software.
- A refusal in the transcript: verify that the backend did not retrieve protected data or execute a tool despite what the model says.
These approaches can have a place in a layered design. The failure is treating any one of them as a substitute for limited authority, independent authorization, action-boundary approval where needed, and testing.
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