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To test an LLM app safely, assess the complete application path—not just whether the model gives a sensible answer. In an authorized, isolated environment, check whether untrusted instructions can change behavior, sensitive test data can leak, unsafe output reaches another system, an agent exceeds its permissions, or retrieval and resource controls fail. Use synthetic data, mocked or read-only tools, strict limits, and a clear stop condition.
These are five practical testing themes drawn from OWASP examples, not a formal five-item OWASP taxonomy. OWASP’s guidance explains why conventional static and dynamic application tests and software-composition analysis are not designed to cover all of the behavior at issue: LLM apps process untrusted language and documents, behave probabilistically, and may pass outputs to tools or APIs. Human-led exploration can complement repeatable automated evaluations. The examples below are grounded in OWASP guidance associated with its 2025 LLM Top 10; a separate OWASP project page identifies a 2026 final release, so do not treat the categories discussed here as a definitive statement of the latest taxonomy.
What should a red-team test cover in an LLM app?
A useful test follows data and control through the application: from a user input, through the model and its instructions, into retrieval or tool use, and then through output handling to any downstream service. A model-only test can miss failures introduced by the surrounding application, such as an authorization bug in retrieval or unsafe handling of generated text.
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- Input: Where do user messages and other untrusted text enter?
- Model and instructions: Which model and application configuration are under test?
- Context: Can retrieval, memory, documents, emails, web pages, or tool results add content?
- Output and actions: Where does generated text go, and which tools or downstream systems can it affect?
- Authority: What can the test account and each connected tool read or change?
Write down the expected safe behavior for each path. “The model should ignore the attack” is not a sufficient control if a tool still performs an unauthorized action or an output sink executes unsafe content. A good test checks the application’s controls as well as the model’s response.
How can I prepare a safe, authorized test?
Use written authorization and a bounded environment before testing adversarial behavior. The precautions below are conservative implementation guidance; OWASP’s testing examples identify categories and scenarios, but do not prescribe a universal test protocol.
- Define written scope. Name the application and environment, the accounts and data in scope, the test window if relevant, and the actions you are permitted to attempt. Make clear what is out of bounds, including live customer data and external systems.
- Prefer isolated staging. Use test accounts and synthetic records. Do not put live credentials or real personal records in prompts, documents, logs, or test fixtures. Use harmless markers that you can recognize in a response or log.
- Map the application path. Trace the input, model and system prompt, retrieval, output validation, tool calls, downstream services, and permissions. Note which components are mocked, read-only, or capable of writes.
- Start with low-impact capabilities. Use mocked or read-only tools first. Leave write actions disabled unless the authorization explicitly covers them and you have reversible test fixtures and a reliable rollback.
- Set limits and stop conditions. Bound prompt repetitions, token use, recursion, and tool calls. Stop if the test touches real user data, reaches a live external system, or triggers an unexpected action. Do not turn a staging check into uncontrolled load testing.
- Capture evidence and retest. Record the test prompt or fixture, model and application version, configuration, expected and observed behavior, and relevant logs. After a fix, rerun the scenario and report what was in scope and what was not tested.
These controls make an unexpected result easier to contain and diagnose. If your team cannot confidently scope the connected tools, data, or downstream effects, get qualified security help before attempting tests that could change or disclose information.
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Test whether instructions in user input or untrusted content can redirect the task or influence tool use. A direct attack arrives in a user message; an indirect one is embedded in material the application retrieves or receives from another source.
Test direct and indirect paths
In staging, use harmless instructions that ask the app to disregard its assigned task or to follow a different instruction. For an indirect test, place a harmless conflicting instruction in a synthetic document, email, web page, or mocked tool result that the app is expected to process. The goal is not to collect a particular model response; it is to see whether untrusted content changes the intended behavior or reaches a tool action.
Observe the whole path. Did the app keep the requested task, treat the injected text as content rather than authority, and avoid an unintended tool call? Check logs and tool traces as well as the final answer: a refusal in the user-visible response does not establish that no action occurred behind the scenes.
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What a failure means
If untrusted content redirects the app or influences a tool call, the failure may involve more than the model’s instruction-following behavior. Review how retrieved and tool-provided text is separated from trusted instructions, what validation occurs before actions, and whether the tool has more authority than the user’s task requires. Retest each affected entry point, because direct input and retrieved content are distinct paths.
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Check whether the app exposes synthetic secrets, synthetic personal data, or another test account’s records in a controlled environment. Never seed a test with live credentials or real personal records merely to see whether the system reveals them.
Use synthetic markers and separate accounts
Create clearly artificial values and records for the test, then place them only in the context or account whose exposure you are authorized to assess. If checking retrieval boundaries, use separate test accounts with distinct synthetic records and verify whether one account can obtain the other’s data. Keep the test’s expected result explicit: for example, an account should only receive records authorized for that account.
Try plausible ways the application might surface information, such as asking for content from context or memory that should not be disclosed, while keeping the test within scope. Observe not only the answer but also retrieval results, tool traces, and logs. A model can avoid repeating a marker while the application still retrieves or logs it in an inappropriate place.
Interpret the result carefully
A successful disclosure of a synthetic marker demonstrates a boundary failure in the tested configuration; it does not by itself establish that a particular real user’s data was exposed. Preserve the fixture and relevant logs, identify whether the source was prompt context, memory, or retrieval, and avoid broad claims about data exposure beyond what the test actually observed.
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Can unsafe model output reach a browser, shell, SQL engine, or template?
Check whether generated text is validated and handled safely before another system interprets it. OWASP’s examples include unescaped output and downstream risks such as cross-site scripting, server-side request forgery, and command injection.
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Test the handoff, not just the response
Use harmless test markers in staging and trace the output into its destination. If the app renders content in a browser, check whether the output is treated as data or interpreted as markup or script. If output is passed to a query, shell, or template system, verify that the destination receives constrained, safely handled values rather than executable instructions. Use a staging sink and benign markers; do not probe live systems with payloads that could execute, retrieve external resources, or alter data.
Review the boundary where the application passes text onward. The key question is whether the downstream system can interpret model output as commands or active content. A model response that appears harmless when displayed as plain text may behave differently when embedded in a page or passed into another interpreter.
What to fix after a failure
Keep validation and safe handling at the application boundary rather than relying on the model to consistently produce safe output. Constrain what the destination accepts, encode or handle content appropriately for its context, and avoid passing free-form generated text directly into powerful interpreters. Then retest with the same harmless marker and verify the destination’s behavior, not only the text returned by the model.
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Can an agent take an action beyond the user’s authority?
Test whether an injected, ambiguous, or manipulated instruction can cause an agent to call a tool, send data, or modify a resource the test user is not entitled to affect. OWASP describes excessive agency as damaging actions in response to unexpected, ambiguous, or manipulated outputs.
Match tool authority to the test
Begin with tools mocked or restricted to read-only behavior. For an action-capable test, use an explicitly authorized test account and reversible fixtures, and disable writes unless the scope specifically permits them. Use a harmless test request that makes it possible to see whether the agent attempts an action it should not take; do not let an exploratory prompt send real messages, change live resources, or transfer sensitive data.
Check the action trace and the effective permissions, not just the agent’s explanation. A response that says it will not act is not proof that no tool was called. Conversely, a blocked tool call may show that an important authorization control worked, even if the model attempted the action.
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Diagnose the boundary
Separate the agent’s decision from the tool’s enforcement. Ask whether the request was authorized for the user, whether the app constrained the tool to that authority, and whether the connected service independently rejected disallowed actions. If the tool has broader privileges than the user, an instruction-following failure can have a larger impact than the same behavior in a read-only assistant.
Can retrieval or resource controls fail?
Test whether retrieval authorization keeps one test account from seeing another account’s synthetic records, then check resource-related behavior only in bounded staging scenarios. These are separate questions: one concerns which information can be retrieved, while the other concerns how much work the application can be made to perform.
Check retrieval authorization
Use the separate test accounts and synthetic records described above. Confirm that the application’s retrieval layer returns only records authorized for the requesting account; do not infer isolation from a model’s refusal if the underlying retrieval returned another account’s data. Inspect retrieval results or relevant traces when available and within your authorization.
Bound resource tests
In staging, use controlled checks for token floods, recursive agent loops, and expensive tool calls. Set strict quotas and a stop condition before starting; stop when the application reaches the agreed limit or behaves unexpectedly. Do not use production traffic or open-ended repetition to estimate how much load a system can withstand.
Record the configured limits and the observed stopping behavior. The purpose is to verify that the application has enforceable bounds and that the test itself remains controlled, not to establish a universal safe quota or performance benchmark.
How should I record and report findings?
A finding should let an owner reproduce the behavior safely and understand its limits without repeating an uncontrolled test. Capture the conditions that matter, then report the observed outcome rather than generalizing from one run.
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- Scope: The environment, account, data type, tools, actions, and paths tested.
- Configuration: Model and application version, relevant settings, and whether retrieval or tools were mocked, read-only, or write-enabled.
- Fixture: The harmless prompt or synthetic data used, with any sensitive test values handled according to the agreed procedure.
- Expected and observed behavior: What should have happened, what actually happened, and whether an unintended retrieval or tool action occurred.
- Evidence: Relevant logs or traces and enough context to connect the result to the tested path.
- Limits: What was not tested, including any excluded systems, accounts, actions, or environments.
After remediation, rerun the same scenario and record whether the specific behavior changed. A passing rerun supports a narrow conclusion about that scenario and configuration; it is not proof that every prompt, model behavior, integration, or authorization boundary is secure.
How should I combine automated evaluations and human red teaming?
Automated evaluations are useful for repeatable checks across known scenarios; human-led red teaming can explore unexpected combinations and application paths. OWASP’s guidance supports using both rather than treating one as a substitute for the other.
| Approach | Useful for | What it may miss | Environment and action scope |
|---|---|---|---|
| Automated evaluations | Repeatable checks against a defined set of prompts, expected behaviors, and configurations. | Unanticipated paths or interactions outside the cases the evaluation covers. | Can be run against a controlled setup; the appropriate data and tool capability depend on the test design. |
| Human-led exploratory testing | Creative probing of interactions among prompts, retrieved content, tools, output handling, and permissions. | Results may be less repeatable unless prompts, fixtures, configuration, and observations are recorded. | Should use the same authorization, synthetic-data, action limits, and stop conditions as other testing. |
| Combined approach | Repeat known checks after changes while using exploration to find cases the fixed set does not cover. | Neither method establishes comprehensive security merely by being performed. | Assess the full application path, with capability and environment matched to written scope. |
Keep the distinction clear in reports: automated results describe the cases evaluated, while exploratory results describe the paths a tester actually exercised. Together they provide broader coverage than a model-only prompt check, but neither justifies claiming that all possible failures have been found.
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Does a red-team test prove an LLM app is secure?
No. A test establishes what happened under the particular model, configuration, data, and application paths exercised. It cannot prove that untested prompts, integrations, or permission boundaries are safe.
Should I test a production LLM app?
Prefer isolated staging with synthetic data and mocked or read-only integrations. If a production assessment is necessary, its authorization and safeguards must explicitly cover the affected accounts, data, actions, and services; the examples here do not authorize production testing.
Is a prompt-injection test the same as a jailbreak test?
They can overlap, but they are not interchangeable labels for every failure. For application testing, focus on whether untrusted instructions—whether supplied directly or through retrieved content—change behavior, access, or tool use.
How often should I rerun LLM security tests?
Rerun relevant scenarios after changes to the model, prompts, retrieval sources, tools, permissions, or output handling. Keep the test configuration with the result so a later run can be compared on like terms.
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