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Test prompt injection by tracing what an attacker-controlled input can make your AI application reveal, change, or do—not just whether the model refuses a malicious prompt. Separate direct user prompts from instructions embedded in retrieved or uploaded content, run each case in an authorized sandbox with synthetic data, and inspect the application’s retrieval, tool enforcement, approvals, logs, and data egress.
Map the AI app’s trust boundaries
Start with the application as deployed, not the model in isolation. OWASP describes red teaming as systematic probing of both the model and the surrounding systems across the application lifecycle. The boundary map identifies where untrusted instructions can enter and what the model can reach.
- Build and configuration: record the application build, model and provider configuration, prompts, filters, retrieval settings, and relevant versions.
- Inputs: list user messages and every external source the app processes, such as webpages, uploaded files, email, code, or images. Note which parsers or modalities make their contents visible to the model.
- Assets and permissions: identify sensitive data, retrieval stores, APIs, tools, and actions available to the model or the user. Record the application-code authorization checks and approval gates that should restrict them.
- Environment: document test accounts, data stores, tool substitutes, and permitted actions. Test only systems for which you have authorization.
OWASP’s LLM01:2025, Prompt Injection covers direct and indirect injection. A direct attack comes from a user prompt; an indirect one arrives in content the application retrieves or processes. Hidden content can matter if the app’s parser or modality exposes it to the model.
Write test cases around specific security objectives
Decide what each test is intended to violate and what observable result would count as failure before running it. Keep the entry channel and security objective explicit so a refusal in one context does not obscure a failure in another.
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| Case field | What to record |
|---|---|
| Entry channel | Direct chat, retrieved webpage, uploaded file, email, image, or another supported input. |
| Protected asset or behavior | The synthetic record, output requirement, tool action, or decision the case is meant to protect. |
| Attack objective | For example, disclose dummy data, distort an answer, or trigger a restricted tool action. |
| Setup and permissions | Relevant user authorization, retrieved content, tool scope, and approval configuration. |
| Benign control | A legitimate in-scope request that should still work, helping reveal whether a defense simply blocks the task. |
| Observable pass/fail result | The output, retrieval, attempted call, enforcement decision, approval event, or egress that establishes the outcome. |
OWASP’s Prompt Injection Prevention Cheat Sheet specifically warns that placing an indirect payload in a user message tests a different boundary. To test retrieved-content defenses, put the test instruction in the retrieved content channel under evaluation.
Test direct and indirect prompt injection separately
Build cases for the channels the product actually supports. OWASP’s examples are illustrative: adapt them to the application’s tasks, permissions, and parsers rather than treating a fixed set of strings as comprehensive coverage.
Direct user prompts
Try plain attempts to override instructions or induce an out-of-scope disclosure or action. Record whether the app exposes synthetic protected data, changes a constrained output, attempts a restricted tool call, or correctly blocks the request at an application-enforced boundary.
Retrieved and uploaded content
Place test instructions in representative webpages, files, emails, code comments, or documentation that the application retrieves or processes. Test retrieval and upload paths separately when they use different parsers, prompts, permissions, or controls. Do not substitute a chat message for an indirect-content case.
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Hidden, split, obfuscated, multilingual, and multimodal content
Include these only where the app’s actual parsers and supported modalities can expose them to the model. For example, evaluate image-embedded instructions only if images are accepted and interpreted. Consider interactions between modalities when the product combines them. A payload that the application cannot ingest does not test its model-facing boundary.
Tools, data access, and consequential decisions
Probe whether untrusted content can lead to access beyond the user’s authorization, a connected-system command without required approval, or a distorted critical decision. Security impact depends on the application context: OWASP identifies sensitive-information disclosure, manipulated outputs, unauthorized function access, connected-system actions, and distorted decisions as possible consequences.
Run tests with synthetic data and sandboxed tools
Use test accounts and dummy records, and replace real integrations with restricted substitutes wherever possible. Before running a case, verify that it cannot send real email, alter production records, execute privileged commands, or expose real secrets. Keep the model’s permissions minimal and ensure authorization is enforced in application code, not left to the model’s judgment.
Capture more than the final text response. Inspect retrieval context, tool-call attempts and enforcement, API authorization, approval gates, logs, and data egress. A model may refuse in its visible answer while a tool call or other integration boundary still fails; conversely, objectionable text alone does not establish that a protected system boundary was crossed.
Measure and report what the attack actually changed
Model outputs can vary, so rerun cases and report outcomes by objective rather than merging different kinds of failures into one score. For each rate, preserve its numerator and denominator, number of repetitions, corpus source, model and defense versions, and settings. Retain case-level results so another tester can reproduce the finding.
OWASP cautions: “Use the examples below as a smoke test, not a security benchmark.” A hand-picked set of examples is useful for a quick check, but a pass does not prove the application is secure or support a generalized attack-success rate. The cited OWASP materials provide examples and mitigations, not a generalizable prompt-injection success-rate statistic.
In a finding, state the tested build and configuration, entry channel, attack objective, setup, repetitions, observed application-level effect, and the control that did or did not stop it. Distinguish an attempted attack from a confirmed disclosure or action, and report separate results for confidentiality, output integrity, and unauthorized actions where relevant.
Validate defenses and retest after changes
Test system controls alongside model behavior. OWASP’s guidance supports separating untrusted content from trusted instructions, least-privilege tool access, deterministic output checks, and human approval for high-risk actions. Validate that retrieval is relevant and answers remain grounded and on-task where those properties matter to the application.
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- Enforce tool authorization and data access in application code, rather than relying on a prompt to deny unsafe requests.
- Keep untrusted external content distinguishable from trusted instructions in the application’s processing path.
- Use deterministic code to validate required output formats and action parameters.
- Require human approval before high-impact actions.
- Do not treat a second LLM guardrail as a complete security boundary; OWASP notes that guardrail models can themselves be prompt-injected.
After changing a prompt, parser, retrieval path, tool scope, filter, or approval control, rerun the same case set and add cases for any new input channel. Compare results against the recorded configuration so a change in model or defense version is not mistaken for a security improvement.
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