Test prompt injection by tracing whether untrusted input can cross a real application boundary: expose dummy data or trigger a sandboxed capability it should not reach. Test direct user prompts separately from instructions embedded in retrieved or uploaded content, and inspect tool enforcement, authorization, approvals, logs and data egress—not just whether the model refused a prompt. A small set of examples is a smoke test, not proof that an AI app is secure.
What a prompt-injection test should establish
Prompt injection occurs when instructions supplied by a user or carried in content the AI processes influence the application in an unintended way. OWASP’s LLM01:2025 entry covers both direct and indirect injection, including content that may be hidden or otherwise difficult for a person to notice.
The meaningful question is not simply whether the model produced an alarming sentence. Determine whether the behavior affected a protected asset or control in this application. Possible impacts include disclosure of sensitive information, manipulated outputs, unauthorized function access, commands sent to connected systems, or distorted critical decisions. The relevant impact depends on what the app can access and what users rely on it to do.
A refusal to follow an injected instruction is useful evidence about that particular response, but it does not establish that retrieval, tool authorization, data access or other application controls are secure. Test the integrated system and the boundaries that are supposed to constrain it.
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Map the AI app’s trust boundaries
Before writing attack cases, identify the app build and configuration under test, the assets to protect, every route by which content reaches the model, and the actions the model can request. Include application controls as well as model behavior; OWASP describes red teaming as systematic probing of the model and surrounding systems over the application lifecycle in its AI red teaming guidance.
- Assets: List sensitive records, secrets, user data, business rules and decisions whose confidentiality or integrity matters. Use synthetic equivalents for testing.
- Input channels: Record user prompts, retrieved webpages, uploaded files, emails, code or documentation, and supported images or other media. Note which content is user-controlled or external.
- Retrieval and permissions: Establish what the model can retrieve, whose authorization governs that access, and whether retrieval is constrained by the requesting user’s permissions.
- Tools and downstream systems: Inventory functions, APIs, databases and connected services the model can invoke. Record the access checks and approval gates that should apply.
- Evidence: Determine which outputs, retrieval context, tool-call attempts, enforcement decisions, approvals, logs and data-egress events can be observed in the test environment.
This map turns a vague “did the model get tricked?” question into a testable one: which untrusted input could reach which capability, what constraint should stop it, and what would show that constraint failed?
Write cases around specific objectives
Define cases before running them. OWASP’s Prompt Injection Prevention Cheat Sheet recommends treating its examples as a smoke test, not a security benchmark. Adapt cases to the app’s supported channels and permissions rather than assuming every example applies.
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For each case, record:
- the input channel and location where the test instruction is introduced;
- the protected asset, action or output property at risk;
- the attack objective and required application setup;
- a benign, in-scope control case for comparison; and
- the expected pass/fail evidence, such as a blocked unauthorized tool call or no access to a synthetic secret.
Keep objectives separate. A case targeting disclosure does not by itself establish whether output integrity or tool authorization is safe. Likewise, a harmless manipulated answer and an unauthorized command are different outcomes and should not be collapsed into a single “attack succeeded” result.
Test direct and indirect prompt injection separately
Direct user input
Place the test instruction in the user’s own prompt. This probes how the application handles instructions supplied directly by the person using it, including attempts to override intended behavior or induce disclosure or unauthorized action. Record the model response and any downstream effect; a concerning answer alone is not the same as access to protected data or execution of a tool.
Retrieved, uploaded and external content
For an indirect-injection test, put the instruction in the external content channel being evaluated—for example, a test webpage the app retrieves or a synthetic file it processes. Then invoke the ordinary application workflow that consumes that content. Sending the same instruction as a chat message tests a different boundary, not indirect injection. OWASP’s cheat sheet makes this distinction explicit.
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Where relevant to the app, vary the content source: retrieved webpages, uploaded documents, emails, code comments or documentation. Test hidden, split, obfuscated or multilingual instructions only when the app’s parsing and model pipeline can expose those forms to the model. If image or other multimodal input is supported, include instructions in that media and test interactions between modalities. Do not report unsupported channels as covered.
Tools and data boundaries
Where the app has connected capabilities, test whether untrusted content can induce a tool request outside the user’s authorization or bypass a required approval. A safe test can use a restricted stub that records an attempted operation without performing it. Inspect both whether the model proposed the call and whether application code denied or allowed it; those are distinct observations.
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Run tests with synthetic data and sandboxed tools
Use test accounts, synthetic records and an isolated environment. Replace real integrations with restricted substitutes wherever possible. Before running a case, verify that it cannot send real email, change production records, execute privileged commands or expose real secrets. If the expected observation requires an action, make the substitute record or simulate that action rather than carrying it out.
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Keep the model’s permissions minimal for the test, but preserve the authorization rules the application is meant to enforce. Test the controls as deployed: separate untrusted content from trusted instructions, enforce tool authorization in application code, and require human approval before high-impact actions. Validate expected output formats with deterministic code where applicable. A second LLM guardrail can help identify unsafe behavior, but OWASP cautions that guardrail models can themselves be prompt-injected; do not treat one as the security boundary.
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Observe the full application path, not only the final chat transcript. Depending on the objective, inspect:
- the model’s response and whether it is relevant and grounded in permitted content;
- the content returned by retrieval and whether it was appropriate for that user;
- tool-call attempts, application authorization decisions and approval-gate behavior;
- API access, changes to synthetic records, logs and data leaving the application.
Report the outcome against the case’s stated objective. A prompt that produces a refusal, an unsafe answer, a denied tool attempt or a completed unauthorized action represents a different result in each instance. Distinguishing them tells the team whether the issue lies in model behavior, a system control, or both.
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Model outputs can vary between runs. Repeat cases and preserve case-level outcomes, the numerator and denominator for any reported rate, the corpus source, model and defense versions, relevant settings and run counts. Report separate rates by objective and channel rather than combining them into one security score. OWASP’s illustrative examples are not a representative corpus; a pass on them does not prove security, and their outcomes do not justify generalizing an attack-success rate to other apps or models. The cited OWASP materials do not establish a general prompt-injection success-rate statistic.
Retest after changes and keep results reproducible
When a prompt, parser, retrieval path, tool scope, filter or approval control changes, rerun the same cases so results can be compared against the earlier configuration. Add cases for any new channel or capability introduced by the change. Record what changed alongside the model, defense versions and settings; otherwise, a different result may be difficult to attribute. This repeat-and-compare approach applies OWASP’s guidance for recurring adversarial testing across the application lifecycle.
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