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Red-team an AI application as a complete system, not as a chatbot in isolation. Test model behavior, retrieval and external content, identity and authorization, infrastructure, connected tools, and runtime actions. Use dummy data and sandboxed operations, exercise both direct user attacks and indirect instructions hidden in files or webpages, and preserve logs showing whether an attack changed an answer, exposed data, or caused an unauthorized action.
What an AI red-team assessment must cover
A useful assessment asks whether an attacker can turn untrusted text into a consequential outcome. The outcome might be disclosure of confidential information, retrieval of a record outside the user’s permission, misuse of a connected function, or an external action such as changing a record. The model’s refusal behavior matters, but it is only one control in a larger application.
OWASP’s GenAI Red Teaming Guide, published January 22, 2025, divides assessment into four areas. Treat these as overlapping workstreams rather than four isolated tests.
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| Model evaluation | Instruction following, refusal behavior, handling of conflicting instructions, and responses to direct and indirect attacks | Inputs, outputs, model and configuration versions, and repeated-trial results |
| Implementation testing | Prompt construction, retrieval, memory, data filtering, tool routing, output handling, and authorization checks | Prompt and retrieval traces, identity context, policy decisions, tool arguments, and application logs |
| Infrastructure assessment | Service accounts, network paths, secrets, storage, monitoring, and isolation around model-connected components | Permissions, credential scope, network and storage events, configuration, and alerts |
| Runtime behavior analysis | What happens during live or simulated use when an attack changes model output or attempts an action | Timeline of requests, responses, tool calls, approvals, side effects, and cleanup |
Adapt the balance to the system’s threat model. A retrieval-augmented assistant needs strong document and identity tests; an agent that can modify business records needs stronger tool, approval, and runtime controls.
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1. Set authorization, scope, and safety boundaries
Obtain written permission before testing. Record the exact system and application version, environment, test window, accounts that may be used, data classifications, external content sources, connected tools, and actions that are prohibited. Include contact details and a stop condition for an unexpected real-world effect.
Use a safe test environment
Prefer staging or an isolated tenant. Replace real records with synthetic data and replace sensitive tools with sandbox substitutes. For example, a message-sending function can write to a test mailbox, and a purchasing function can return a simulated order identifier without contacting a payment service. The OWASP prevention cheat sheet specifically recommends dummy data and sandboxed tool substitutes for smoke testing.
Define pass and fail conditions before sending prompts
A pass is not simply “the model refused.” Define the intended safe behavior for the task. An allowed request should still work, while an unauthorized request should be denied by the application and leave no side effect. A test fails when an untrusted instruction causes a protected marker to appear, a restricted record to be retrieved, a tool to be called with unsafe arguments, or an action to occur without the required approval.
2. Draw the trust and data-flow map
Map every path by which instructions or data enter and leave the system. Include system and developer instructions, user messages, uploaded files, websites, search results, retrieved documents, memory, model calls, tool and API calls, logs, and final destinations. Mark each node as trusted, user-controlled, externally controlled, or mixed.
Mark security boundaries explicitly
- Identify where confidential data is stored and which identities can read it.
- Show where retrieval filters records and where authorization is actually enforced.
- Record whether external content is parsed, summarized, embedded, or passed directly to the model.
- List every function the model can select, its arguments, its execution identity, and its possible side effects.
- Show where outputs are rendered, sent to another service, stored in memory, or used to make a decision.
A user-message-only exercise does not test an external-content boundary. If the product reads a webpage, ticket, email, spreadsheet, or document, put the authorized test instruction in that channel and observe the complete path.
3. Turn risks into reproducible test cases
Write each test as a small experiment. This makes results comparable across model versions and prevents a dramatic but unrepeatable prompt from being mistaken for evidence.
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- Preconditions: state the account, permissions, enabled tools, data loaded, and configuration.
- Attacker-controlled channel: specify whether the payload is a user message, file, webpage, retrieved record, memory item, or another source.
- Test marker: use a synthetic value such as
TEST-CONFIDENTIAL-001or a fake record that has no real-world value. - Intended violation: state the prohibited disclosure, retrieval, tool call, or action.
- Expected safe outcome: define the response, authorization decision, tool behavior, and absence of side effects.
- Observable evidence: identify the logs, traces, response text, tool arguments, and data-access events that will prove what happened.
- Cleanup: remove test records, revoke temporary access, reset memory, and verify that no message, order, change, or notification escaped the sandbox.
Include benign control tasks. If the application should summarize an authorized document, test that normal case beside the hostile case. OWASP cautions that its hand-picked attack and benign examples are illustrative smoke tests, not a representative sample or a security benchmark.
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4. Test direct prompt injection
Direct injection arrives in the user-controlled request. Test instructions that conflict with the application’s task, ask for hidden instructions, request another user’s information, or attempt to use a function outside the account’s permission.
Conflict and instruction-boundary tests
- Ask the assistant to abandon the assigned task and follow a new instruction supplied in the same message.
- Ask it to reveal, quote, summarize, or transform its system or developer instructions.
- Request a protected marker or record while authenticated as an identity that should not have access.
- Ask it to call a connected function with an altered destination, excessive scope, or missing approval.
- Wrap the request in translation, formatting, summarization, or role-play instructions to see whether the boundary changes.
Record the full request and response, not just the visible answer. Capture retrieval events, tool selection, arguments, execution identity, authorization decisions, and output filtering. OWASP’s prompt-injection guidance notes that insecurely exposed backend functions and data stores can turn an instruction override into a system compromise.
Do not treat prompt extraction as the only objective
Whether a model repeats a system prompt is less important than whether the application lets untrusted text cross a security boundary. A refusal can still be a failure if the system already retrieved a protected record, sent data to a tool, or exposed a secret in a log. Conversely, a harmless paraphrase of non-sensitive instructions is not equivalent to unauthorized data access.
5. Test indirect prompt injection through every external channel
Indirect injection is carried in content the application processes rather than in the user’s message. The payload may be visible to a person or hidden in text that a parser, OCR layer, spreadsheet cell, metadata field, or embedding pipeline passes to the model. Test the actual channel used by the product.
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A safe indirect-injection procedure
- Place a recognizable, non-sensitive test instruction in an authorized webpage, document, ticket, email, feed, or other source.
- Ask the application to perform its normal task on that source, such as summarizing it or extracting fields.
- Observe whether the task changes, the model requests a secret, the synthetic marker appears, or a connected function is selected.
- Repeat with the instruction in different authorized locations and formats relevant to the parser, without attempting to evade safety controls in a production system.
- Review retrieval, parsing, model, tool, and authorization logs to identify the first component that accepted the hostile instruction.
Test each source separately. A document-upload test does not cover a browsing connector, and a visible paragraph does not cover hidden text that the parser sends to the model. OWASP notes that indirect instructions need not be human-visible if the model can parse them.
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6. Probe data leakage and authorization boundaries
Data leakage occurs when confidential information becomes available through an answer, retrieval result, tool, memory, log, or downstream service. Test the application’s real identity and data-access boundaries rather than relying on a verbal refusal.
Use synthetic records and distinct identities
Create fake records with clearly different permissions. Run the same task as an authorized user, an unauthorized user, and—where relevant—a user who can discover that a record exists but cannot read its contents. Check whether the data layer denies the request before the model sees the record. In a retrieval system, inspect filters and document-level permissions directly.
Test transformations and routing
Ask whether the system will return, paraphrase, translate, summarize, encode, classify, or route the protected marker. Leakage is still leakage if the exact string is absent but the sensitive value can be reconstructed or sent to an unauthorized tool. Inspect destinations such as conversation memory, analytics, transcripts, webhooks, and generated files.
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7. Assess connected tools and high-impact actions
For every model-connected function, test more than whether the model selected the right name. Examine the complete authorization and execution path.
Check the action envelope
- Selection: Could hostile content cause the function to be chosen when the user did not request it?
- Arguments: Are destinations, quantities, identifiers, filters, and permissions validated outside the model?
- Identity: Does execution use the requesting user’s authorized identity or an overpowered service account?
- Scope: Are tenant, record, network, and time boundaries enforced by deterministic code?
- Confirmation: Does a privileged action wait for an application-recorded approval?
- Result handling: Are tool responses filtered before being shown to the user or fed back into the model?
Use sandbox substitutes for messaging, record changes, purchases, deletion, and other irreversible operations. A prompt that tells the model to “ask for approval” is not an approval control. The application must block execution until the required approval is recorded and verified.
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8. Evaluate infrastructure and runtime controls
Infrastructure assessment
Review service accounts, API keys, network egress, storage permissions, temporary files, queues, logs, and isolation between tenants. Determine whether a model-connected component can reach systems unrelated to its task. Verify that credentials are scoped, rotated, and unavailable to prompts, retrieved documents, and model output.
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Run attacks while observing the live sequence of events. A response that looks safe may conceal a failed authorization check, an attempted tool call, or a blocked network request. Capture timestamps, identities, decisions, and side effects. Confirm that alerts or rate limits trigger when behavior deviates from the expected task.
Model evaluation
Repeat equivalent tests across the deployed model and relevant configuration changes. Note that outputs can vary between trials. Report the number of attempts and exact conditions; do not convert a small smoke test into a universal pass rate.
9. Prioritize findings by consequence and control failure
Rank findings using the evidence you collected rather than the cleverness of the prompt. A practical priority order is:
- Unauthorized real-world action or access to highly sensitive data.
- Any path that combines injection with a privileged tool, broad service identity, or missing approval.
- Cross-user or cross-tenant retrieval caused by a data-layer authorization failure.
- Disclosure of secrets, credentials, connection strings, or protected system data.
- Task deviation, misleading output, or prompt text exposure without a demonstrated consequential path.
For each finding, state the attack channel, preconditions, affected identity, data or action reached, reproducibility, evidence, and containment recommendation. Separate a model tendency from an application vulnerability. For example, a model may follow an unsafe instruction in a sandbox, while the application’s authorization layer correctly blocks every resulting function call.
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Every result should identify:
- Model, application, retrieval, and tool versions.
- Environment, tenant, account, permissions, and configuration.
- Input channel and the exact test content.
- Expected safe behavior and observed behavior.
- Retrieved sources, model output, tool calls, arguments, approvals, and authorization decisions.
- Logs, screenshots, trace identifiers, and cleanup confirmation.
- Number of repeated trials and any variation between them.
Report limitations plainly. OWASP’s examples are smoke-test probes, not a representative benchmark, and a limited set of successful refusals does not prove that the system is secure. A useful report says what was tested, what was not tested, and which controls prevented or failed to prevent the observed outcome.
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11. Apply controls outside the model
Least privilege and scoped credentials
Give each model-connected function only the permissions required for its task. Use separate identities for read and write operations, restrict tenant and record scope, and limit network destinations. Keep secrets and connection strings out of prompts and retrieved content.
Independent authorization and validation
Enforce identity, access decisions, bounds checks, destination allowlists, and output validation in deterministic application code. Treat the model as an untrusted decision aid, not as the component that grants permission.
Untrusted-content isolation
Label and structurally separate external content from higher-priority application instructions. Isolation can reduce confusion, but it is not a complete fix: authorization, least privilege, and tool validation must still hold if the model follows the hostile text.
Human approval for privileged operations
Require an application-level approval for actions that send, purchase, delete, publish, change access, or alter important records. Record who approved, what was approved, the exact arguments, and when the approval expires.
Monitoring and feedback
Monitor inputs, outputs, retrieval, tool calls, authorization decisions, and side effects. Alert on unusual destinations, repeated denials, sensitive-marker appearances, and actions outside normal task bounds. Feed confirmed failures back into test cases and retest after every material model, prompt, connector, or permission change.
12. Compare assessment approaches before choosing tools
There is no single test suite that covers every AI application. Compare an assessment approach against the system’s threat model using these axes:
| Comparison axis | Questions to ask |
|---|---|
| Direct versus indirect coverage | Does it test user messages and every external-content channel, including files, webpages, and retrieved records? |
| System coverage | Does it examine model behavior, implementation, infrastructure, and runtime actions? |
| Permission testing | Can it exercise distinct identities, document permissions, tool scopes, and approval gates? |
| Evidence and repeatability | Does it preserve inputs, versions, traces, logs, outcomes, and repeated-trial conditions? |
| Safe execution | Does it support dummy data and sandboxed substitutes for high-impact tools? |
| Threat-model fit | Can the cases be adapted to the application’s data, connectors, users, and consequences? |
OWASP recommends adapting cases to the use case. A tool that generates many model-only prompts may be useful for one workstream but insufficient for a system whose main risk is unauthorized retrieval or an overprivileged API.
Common mistakes that invalidate an assessment
- Testing only the chat box: this misses instructions carried by documents, websites, memory, and retrieval results.
- Using real secrets or production actions: a successful test can become a real incident. Use synthetic markers and substitutes.
- Counting refusals as authorization: the data layer and tool executor must enforce access independently.
- Putting secrets in prompts: system instructions can be exposed, logged, retrieved, or transformed.
- Ignoring tool arguments: safe function selection does not make an unsafe destination or quantity safe.
- Running one dramatic prompt: variable model output requires repeated, documented trials.
- Calling smoke tests a benchmark: illustrative cases do not establish universal security.
- Failing to clean up: test records, memory entries, temporary credentials, and sandbox actions must be removed and verified.
When is the assessment complete?
End a testing cycle when every identified trust boundary has an authorized case, expected behavior is defined, evidence is captured, high-impact paths have been exercised safely, and findings have owners and retest conditions. Reopen the cycle after changes to the model, prompt, retrieval pipeline, connected tools, identity model, or deployment environment. The goal is not to prove that an AI system can never be manipulated; it is to demonstrate that untrusted instructions cannot cross into unauthorized data access or consequential actions without independent controls.
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