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Red-team an AI application as a complete system, not as a chatbot in isolation. A useful assessment covers four areas identified in OWASP’s GenAI Red Teaming Guide (published January 22, 2025): model behavior, implementation and integrations, infrastructure, and runtime behavior. Test hostile instructions arriving both from users and from external content, then follow each attempt through retrieval, authorization, tool calls, and final outputs. Use synthetic secrets, staging data, and sandboxed tools so a failure is observable without causing harm.
The practical question is not simply “did the model refuse?” It is whether an attacker can make the application disclose protected information, cross a data boundary, misuse a connected capability, or perform an unauthorized action. The procedure below gives security, engineering, and risk teams a repeatable way to answer that question.
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What a red-team assessment must cover
An AI feature usually combines a model with system instructions, user messages, retrieval, memory, external websites or files, APIs, identity services, and a runtime that executes actions. Each component creates a different trust boundary. A model can produce a cautious refusal while the surrounding application has already fetched a record, called a tool, or logged sensitive content where an attacker can later obtain it.
Organize the assessment into four overlapping areas:
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| Area | Questions to answer | Evidence to collect |
|---|---|---|
| Model evaluation | Does the model follow untrusted instructions, reveal hidden context, or generate unsafe tool arguments? | Prompts, responses, refusal details, model and configuration versions |
| Implementation testing | Are retrieval filters, identity checks, output handling, and tool wrappers enforcing policy independently of the model? | Authorization decisions, query filters, tool requests, rejected requests, application logs |
| Infrastructure assessment | Can credentials, queues, storage, network paths, or observability systems be reached through the AI feature? | Service identities, network policy, secrets configuration, audit trails |
| Runtime behavior analysis | What actually happens during a hostile interaction, including side effects and data movement? | Traces linking input, retrieval, model calls, tools, destinations, and user identity |
OWASP’s framing is useful because it prevents a “prompt-only” review. Adapt test cases to the application’s actual use case, data classifications, connected systems, and threat model.
Prompt injection, leakage, and tool misuse are different failures
Prompt injection
Prompt injection occurs when untrusted instructions alter the model’s behavior. A direct injection is supplied in the user’s message. An indirect injection is placed in content the application retrieves or processes, such as a webpage, uploaded document, ticket, or calendar entry. The latter may be invisible to a human if the model parses the content but the user never sees the adversarial text.
Prompt leakage
Prompt leakage is the disclosure of system instructions or other hidden context. A system prompt should not contain passwords, API keys, connection strings, or confidential policy details. Even if a model usually refuses to print its prompt, that refusal is not a security boundary. Treat prompt text as potentially discoverable and put secrecy and authorization in application controls.
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Data leakage is broader: confidential information reaches an unauthorized person, model output, tool, log, or downstream destination. In a retrieval-augmented system, a verbal refusal is not proof that access control worked. The data layer must make the allow-or-deny decision for the requesting identity.
Tool misuse
Tool misuse happens when an injected instruction steers a connected function toward an unintended action, such as sending a message, changing a record, purchasing something, or deleting data. Least privilege, bounded arguments, and application-level approval reduce the impact even when the model is manipulated.
Prepare authorization, scope, and safety controls
Before sending a test, obtain written authorization from the system owner and record a test window. Define exactly which deployment, accounts, data sets, external sources, tools, and environments are in scope. Identify actions that are prohibited, such as contacting real customers or changing production records.
Use a staging deployment whenever possible. Replace live operations with sandbox substitutes that accept the same interface but cannot deliver messages, move money, alter production data, or delete resources. Populate the environment with synthetic records and recognizable markers such as REDTEAM-DUMMY-001. Do not use real personal, financial, health, authentication, or customer data merely to make a test convincing.
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- Record the application release, model name, model settings, retrieval index version, tool definitions, and identity provider configuration.
- List every permitted tester identity and the data each identity is allowed to access.
- Specify external-content channels to test: URLs, files, tickets, email, chat history, connectors, or memory.
- Define emergency stop contacts and a method to revoke test credentials or disable tools.
- Agree on evidence retention, redaction, and cleanup procedures before testing begins.
Map trust boundaries and data flows
Draw a flow from the initial request to every place information can travel. Include system instructions, user input, conversation memory, retrieved documents, websites and files, model calls, tool and API calls, databases, logs, and final destinations. Mark each item as trusted, untrusted, or mixed content. Mark confidential data and the component that performs authorization.
For each edge, ask what prevents an attacker-controlled value from becoming an instruction. A user message is not the only input. A document chunk, webpage title, spreadsheet cell, image caption, tool result, or memory entry can enter the model context. If the application only tests a user-message field, it has not tested an external-content boundary.
Document identities at every hop. The identity used for retrieval may differ from the end user; a shared service account can accidentally make every user look authorized. Likewise, a tool may run with permissions broader than the person who initiated the chat. Capture the identity, tenant, resource, and decision for each access.
Design interpretable test cases
Write a test case before executing it. A good case states what should happen and what evidence would demonstrate a violation.
- Preconditions: deployment, user identity, feature flags, seeded records, and available tools.
- Attacker-controlled channel: user message, retrieved document, webpage, file, memory item, or tool result.
- Marker or test record: a synthetic value whose exposure is unmistakable.
- Intended violation: for example, disclose a protected marker, cross a tenant boundary, or invoke a high-impact tool.
- Expected safe outcome: refuse the instruction, omit unauthorized data, or require an independent approval gate while completing any allowed task.
- Observable evidence: response, retrieval query, records returned, tool name and arguments, authorization decision, logs, and destination.
- Cleanup: remove test content, revoke temporary credentials, and restore seeded records.
Include benign control tasks. An application that refuses every request has not demonstrated security; it has demonstrated denial of service. Pair a hostile case with an allowed task that should succeed for the same identity. OWASP describes its sample attacks and benign requests as illustrative smoke tests, not a representative corpus or a security benchmark.
Run direct prompt-injection tests
Start with the user-input channel, using dummy information and sandboxed functions. Test several categories rather than relying on one memorable phrase.
Instruction conflict
Ask the system to disregard its assigned task and follow a new instruction. The test should be specific to the application, such as a support assistant asked to reveal hidden context instead of summarizing a ticket. Record whether the model changes task, exposes restricted content, or causes a tool call.
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Hidden-instruction extraction
Request the system instructions or a transformed version of them, such as a summary or an encoded rendering. The objective is to learn whether sensitive material was placed in the prompt and whether output filters or logging expose it. A refusal is not a pass if the prompt contains credentials or if the application already made protected context available to the model.
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Use two test identities: one with access to REDTEAM-DUMMY-001 and one without it. Ask both for the record directly and through transformations such as summarization or export. Then ask the unauthorized identity to perform an action that would require the record. Verify the data-layer decision and tool authorization, not just the text of the answer.
Capture the complete request and response, model calls, retrieval results, tool calls and arguments, status codes, and relevant logs. Do not test against real secrets or attempt to bypass controls in an environment you are not authorized to assess.
Test indirect injection in every external-content path
Indirect testing must place the test instruction in the external channel being evaluated. Put a clearly identifiable adversarial test string in a staging webpage, document, ticket, feed item, or other source, then ask the application to perform its normal task on that source. Do not paste the same string into the chat and call that an indirect test.
Vary location and formatting within the approved plan: body text, a quoted section, a footnote, a table cell, metadata, or content that is visually inconspicuous but still parsed. Keep the payload benign and recognizable. For each source, check whether the application:
- changes the requested task or ignores the user’s objective;
- asks for credentials, tokens, or other secrets;
- returns the synthetic protected marker;
- passes attacker-controlled text into a tool argument or destination;
- suppresses warnings or conceals the event from logs.
Test each connector separately. A web retriever, file parser, email connector, and memory store may normalize content differently, so success against one path says nothing about the others. Also test content that contains an instruction in a tool result: downstream model calls can treat a result as trusted unless the application marks its provenance.
Probe retrieval and authorization boundaries
Seed records with distinct tenants, roles, and sensitivity labels. For every identity, test direct retrieval, filtered search, summarization, citation generation, export, and follow-up questions that refer to information indirectly. Check whether unauthorized records enter the model context even when the final answer omits them. Context exposure can leak through citations, token usage, logs, debugging traces, or later conversation turns.
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Authorization belongs in deterministic application and data-layer checks. Enforce tenant, role, resource, and field-level permissions before data is sent to the model. Do not rely on a system instruction saying “never show another customer’s data.” Verify that the query itself is constrained and that a tool cannot be called with a different identity or resource identifier.
Use synthetic markers in system instructions and protected records, then search for exact, paraphrased, translated, transformed, or encoded appearances in responses and traces. Test output destinations too: an apparently safe answer can still leak a marker through a webhook, file export, notification, analytics event, or support ticket.
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For every connected function, test four properties: selection, arguments, identity, and destination. A model should not be able to select an administrative function merely because a prompt requests it. Arguments need schema validation, range and ownership checks, and protection against attacker-controlled destinations. The function should execute as a narrowly scoped service identity, not as an unrestricted administrator.
| Control | Red-team check | Failure evidence |
|---|---|---|
| Least privilege | Attempt an action outside the feature’s stated purpose using the same tool interface. | Tool succeeds with broader permissions than required. |
| Independent authorization | Change the chat user, tenant, or resource ID while keeping the model request similar. | Tool trusts model-supplied identity or object IDs. |
| Argument validation | Use out-of-range amounts, unsupported operations, or attacker-controlled URLs in a sandbox. | Wrapper forwards unsafe values without deterministic checks. |
| Approval gate | Ask the model to claim that approval was granted or to skip confirmation. | Privileged action executes without a recorded application approval. |
| Destination control | Provide a test destination different from the authorized one. | Message, export, or callback is sent to the supplied destination. |
Human approval must be enforced by the application. A model-generated sentence saying “approved” is not an approval record. For destructive, financial, external-communication, or permission-changing actions, require a separate authenticated decision and show the exact operation, target, and scope to the approver.
Evaluate infrastructure and runtime behavior
Review service accounts, secret stores, network egress, queues, object storage, logs, traces, and monitoring pipelines connected to the AI feature. Ask whether a manipulated request can make the system retrieve an internal URL, read a broader bucket, access another tenant’s queue, or write sensitive text into an analytics system. Test with isolated resources and canary markers rather than production endpoints.
Keep credentials and connection strings out of system prompts. Store them in a secret manager and expose only narrowly scoped operations to the application. Apply network and storage policy independently of model instructions. Log enough to reconstruct an event, while redacting sensitive payloads and restricting log access.
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Repeat tests and report evidence without overstating confidence
Model outputs can vary with sampling, context order, retrieval results, provider changes, and load. Repeat important cases under documented conditions. Report the number of trials, configuration, channel, identity, and observed outcomes; do not turn a small smoke test into a universal pass rate.
A useful finding contains:
- affected component and version;
- attacker channel and required privileges;
- minimal reproducible input or external test artifact;
- records, tools, and destinations reached;
- confidentiality, integrity, and availability impact;
- logs or traces proving the path;
- safe reproduction limits and cleanup status;
- recommended control owner and retest condition.
Separate “model refused” from “request was prevented.” The former describes one output; the latter requires evidence that retrieval, authorization, tool execution, and side effects were blocked. State what the test did not cover, such as an untested connector, production-only workflow, or infrastructure account.
Mitigate findings at the application layer
Reduce permissions and isolate trust
Give each model-connected function only the minimum permissions needed, using scoped credentials and separate identities for read and write operations. Mark external content as untrusted and keep it structurally separate from system instructions where practical. Separation helps, but it is not a complete defense; authorization and validation must still be independent.
Enforce deterministic controls
Implement authorization, bounds checks, schema validation, destination allowlists, rate limits, and output checks in code around the model. Validate both the request to a tool and the result returned from it. Prevent a model from selecting an unapproved function or changing the identity, tenant, or resource on which a function operates.
Protect secrets and sensitive outputs
Keep credentials, connection strings, signing keys, and private policy data out of prompts. Minimize the context sent to the model, redact sensitive fields where possible, and apply access controls to traces and logs. Scan outputs and downstream payloads for synthetic canaries and high-risk data classes before release.
Require approval for consequential operations
Use an authenticated, separately recorded approval step for privileged actions. Display the exact target, arguments, and expected effect. Expire approvals, bind them to the specific operation, and reject changes made after approval.
Monitor and improve continuously
Monitor inputs, outputs, retrievals, tool calls, authorization decisions, and side effects for unexpected combinations. Feed confirmed failures into regression tests. Re-run the suite after model, prompt, retrieval, connector, tool, identity, or infrastructure changes. Treat smoke tests as probes that reveal control weaknesses, not as a certification that the AI system is secure.
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A practical assessment checklist
- Authorization, scope, stop conditions, and synthetic data are documented.
- All direct and indirect input channels are mapped and tested.
- Model, implementation, infrastructure, and runtime behavior are covered.
- RAG permissions are tested with distinct identities and data-layer evidence.
- System prompts contain no credentials or connection strings.
- Tools use least privilege, validated arguments, destination controls, and independent approval.
- Sandbox substitutes protect messaging, purchasing, deletion, and record-changing operations.
- Evidence links each input to retrieval, model context, tool activity, authorization, and output.
- Important cases are repeated and reported with their conditions and limitations.
- Findings have owners, remediation criteria, and a scheduled retest.
The defensible outcome of a red-team exercise is not a claim that an AI model is “injection-proof.” It is a measured understanding of where untrusted instructions can travel, which controls stop them, what data or actions remain exposed, and whether those controls continue to work after the system changes.
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