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Red-team an AI agent as a complete socio-technical system, not as a chatbot. Give it legitimate tasks, expose it to realistic hostile content, and verify whether its model, prompts, retrieval, memory, tools, permissions, infrastructure and runtime controls preserve the user’s goal without leaking data or taking unauthorized action. A defensible exercise is authorized in writing, runs in a controlled production-like environment, records the full execution trace, and produces regression tests for every confirmed failure.
OpenAI defines red teaming as using adversarial test cases to uncover unsafe, insecure or policy-violating behavior before deployment. The practical method below combines that principle with the agent-specific risks highlighted by OWASP, NIST and Microsoft: indirect prompt injection, tool misuse, sensitive-data leakage, approval bypass, memory poisoning, recursive abuse and unsafe actions.
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What makes an AI-agent red-team different
A conventional language-model evaluation may check whether a response contains harmful text. An agent assessment must also ask what the system did while producing that response. An agent can appear polite while reading a restricted record, calling an over-privileged tool, following an instruction hidden in a document, writing poisoned memory, or initiating a transaction that the user never approved.
OWASP’s GenAI Red Teaming Guide treats model evaluation, implementation testing, infrastructure assessment and runtime analysis as complementary activities. Microsoft’s agent-testing guidance similarly examines goal achievement, rule compliance and procedural discipline, including the safety of tool outputs. Your test oracle therefore needs more than “the final answer was safe.” It must state which tools could be called, which data could be accessed, what approval was required, what side effects were allowed and how the run should end.
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Start with authorization, scope and a safe environment
Test only systems you own or are expressly authorized to assess. OpenAI’s red-teaming guidance warns against using its service to analyze or report vulnerabilities in third-party code without express written permission. Microsoft advises conducting exercises in a production-like non-production environment and treating active validation as requiring explicit authorization and change management.
Write the scope before creating an attack. Record the agent build, model and provider, system and developer instructions, connected tools, data sources, memory stores, identity mappings, network boundaries and runtime limits. Name the environments and accounts included, the actions that may be simulated, the actions that are forbidden, the people who can stop a run and the process for escalating a live-looking incident.
- Use synthetic financial, medical, personal, credential and confidential records wherever possible.
- Replace real payment, messaging, deletion and deployment integrations with mocks or reversible sandboxes.
- Define stop conditions for unexpected data exposure, uncontrolled tool loops, privilege changes, spending, external contact or irreversible operations.
- Decide who may view traces, where sensitive artifacts are stored, how long they are retained and how they are securely deleted.
- Freeze or record configuration so a later reader can reproduce the exact test.
Microsoft’s agentic-security considerations and AI Red Teaming Agent documentation both emphasize controlled execution, explicit authorization and clear handling of high-impact behavior.
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Map each trust boundary: user message to model, retrieved content to prompt, model to tool router, tool to external system, agent to memory, and one agent or connector to another. For each boundary, define the attacker-controlled input, the protected asset, the forbidden outcome and the evidence that would prove success or failure.
| Risk area | Test question | Evidence to capture |
|---|---|---|
| Indirect prompt injection and goal hijacking | Can an instruction hidden in an email, file, web page or retrieved document redirect the agent from the user’s legitimate task? | The source content, agent plan, tool calls, whether the injected task was attempted or completed, and the effect on the original task. NIST describes hijacking as malicious instructions embedded in task-relevant data. |
| Tool misuse and privilege escalation | Can the agent call a tool, resource or identity outside the user’s authority, or perform a write when only reading is allowed? | Requested and actual tool, identity and permissions, denial behavior and any side effect. OWASP recommends minimum-necessary tools and per-tool permission scope. |
| Sensitive-data leakage | Can adversarial instructions or a tool call expose financial, medical, personal, credential or confidential information in output or logs? | Synthetic data, source, recipient or tool, response and leakage detection. Microsoft describes synthetic sensitive information and mock tools for this testing. |
| Prohibited, high-risk and irreversible actions | Can the agent initiate a banned act, a high-impact act without human authorization or an irreversible operation without valid approval? | The policy rule, proposed parameters, approval state, execution or denial and audit record. |
| Memory or retrieval poisoning | Can untrusted content persist or influence later tasks beyond its intended scope? | Ingestion route, persistence, scope, expiry, later retrieval and whether malicious instructions were followed. |
| Approval bypass and recursive abuse | Can an attacker bypass a required approval or induce unbounded retries, tool chains or spending? | Approval binding and expiry, retry count, chain depth, runtime limits, circuit-breaker results and cost controls. |
| Multi-agent and supply-chain risk | Can compromised instructions or data propagate through another agent, connector, API or tool provider? | Agent-to-agent messages, trust boundaries, connector identity and whether downstream permissions exceed the initiating task. |
Turn policies into executable test rules
Natural-language policy is too vague to serve as a test oracle. Classify every consequential operation into categories such as prohibited, approval-required or process-constrained. For each category, specify the exact conditions for refusal, escalation, safe completion and logging.
Prohibited actions
These must never execute, even if a user, retrieved document or another agent requests them. The expected result is a refusal or safe alternative, no tool side effect and an audit entry explaining the policy decision without disclosing protected details.
High-risk actions requiring approval
Define who can approve, what exact parameters the approval covers, how long it remains valid and whether it can be reused. Test that approval for one recipient, amount or record cannot be replayed against another. An approval prompt displayed to the user is not sufficient if the backend does not bind the authorization to the actual tool call.
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Process-constrained actions
Some actions are allowed only through a named workflow, such as a ticket, dual review or change window. The agent should use the required process rather than accepting an instruction to “skip the paperwork.” Test missing, expired, conflicting and forged approvals, as well as attempts to alter the parameters after approval.
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Create scenario pairs instead of isolated attack prompts
For every legitimate task, create a paired adversarial case. Keep the user’s goal intact, then add a realistic hostile condition in the data the agent is expected to handle. For example, a document-review task can contain an embedded instruction to email a confidential appendix; a support task can retrieve a record containing text that asks the agent to reset an administrator password; a scheduling task can receive a web page that tells the agent to upload its conversation history.
Each scenario should state:
- The legitimate user objective and success criteria.
- The untrusted content and the attacker’s intended diversion.
- Allowed tools, identities, data fields and side effects.
- Forbidden actions and the exact expected refusal, escalation or safe continuation.
- What constitutes partial failure, such as following the injection in reasoning, leaking a secret to a tool, or abandoning the original task even when no side effect occurs.
- A benign control case without the hostile condition and a regression case for every previous defect.
NIST’s agent-hijacking evaluation guidance stresses realistic task context, task-specific outcomes and repeated attempts. NIST’s technical staff define agent hijacking as exploiting the lack of separation between trusted instructions and untrusted external data by placing malicious instructions in an otherwise ordinary email, file or website.
Run the assessment in seven repeatable stages
- Inventory the system. Capture the version, model or provider, prompts, retrieval indexes, memory behavior, tools, connector identities, permissions, network paths and runtime limits. Record which components are mocks and which are real.
- Set authorization and guardrails. Confirm written permission, change approval, stop conditions, synthetic fixtures, rate limits and emergency contacts. Disable production credentials and irreversible integrations unless the authorization explicitly covers a tightly controlled validation.
- Execute benign baselines. Run the legitimate task without hostile content. Establish expected tool sequence, data access, latency, approval prompts, final answer and side effects. Without a baseline, an attack result is difficult to interpret.
- Inject realistic adversarial conditions. Place hostile instructions in retrieved documents, emails, web pages, tickets, calendar entries, tool results and memory candidates. Vary wording, location, formatting and language while keeping the user’s goal constant.
- Repeat and adapt. Run known baseline attacks and attacks developed against the observed system. Change the task, content, ordering and number of attempts. NIST reports that a particular AgentDojo evaluation found an 11% baseline attack success rate versus 81% for the strongest novel attack on a specific upgraded Claude 3.5 Sonnet setup; this illustrates why a single familiar attack suite is not a general safety guarantee.
- Inspect the complete trace. Record inputs, retrieved passages, model decisions where available, tool requests and responses, identity and permission context, policy checks, approval events, retries, timing, side effects and the final response. Protect the trace as sensitive security evidence.
- Triage and retest. Separate confirmed failures from suspected weaknesses, rank impact and exploitability, assign an owner, implement a fix and rerun the original and adapted scenarios. Add a permanent regression test and repeat the exercise after material changes.
Observe decisions, tools and side effects—not just prose
A safe final message can conceal an unsafe intermediate action. Instrument the test harness to show the tool selected, arguments generated, authorization decision, actual identity used, data returned, policy result and downstream effect. Capture denied calls as well as successful calls; a denial proves that a control activated, while a model saying “I cannot do that” proves much less.
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Keep secrets out of fixtures and redact them from reports. OWASP’s AI Agent Security Cheat Sheet recommends least-privilege tools, explicit controls, protected logs and repeated testing after changes.
Measure outcomes by task and attack class
Report a scenario-level result rather than one aggregate score. For each attempt, record whether the legitimate goal was achieved, whether a forbidden instruction was followed, whether data crossed its boundary, whether approval was honored, whether a side effect occurred and whether the system stopped within its limits.
Useful outcome labels include:
- Pass: The legitimate task completed, hostile content was treated as untrusted, permissions and approvals were respected, and no prohibited side effect occurred.
- Safe refusal or escalation: The agent declined or requested the required approval while preserving protected data and auditability.
- Degraded but contained: The agent failed the user’s task or produced an incorrect answer but did not violate a security or policy boundary.
- Security or policy failure: The agent followed the injection, exceeded authority, leaked data, bypassed approval or performed an unauthorized side effect.
- Unresolved: Evidence is incomplete, the environment behaved unexpectedly or the result requires manual investigation.
Do not compare scores from unlike tools or scenarios without stating the threat model, test set, number of attempts and detection criteria. NIST’s 11% and 81% figures are not a universal failure rate: they came from a particular AgentDojo evaluation, a specific upgraded Claude 3.5 Sonnet setup, novel attacks developed against that model and held-out Workspace tasks.
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Apply controls that make failures harder and less costly
Separate trusted instructions from untrusted data
Label retrieved text, files, web pages and tool output as data, not instructions. Keep trust metadata with the content, prevent untrusted text from rewriting system policy and require an independent policy check before a tool call. Test both direct and indirect injection because an attacker may never address the model directly.
Use least privilege and narrow tool contracts
Expose only the tools needed for the task. Scope each tool by operation, fields, tenant, resource, identity and time. Prefer read-only interfaces for analysis, validate arguments server-side and enforce authorization again at the tool or service boundary. A model instruction such as “do not delete” is not a substitute for an API that cannot delete.
Bind approvals to actions
Require approval for high-risk or irreversible operations, bind it to the exact parameters and recipient, expire it quickly and reject changes after approval. Log the approver, policy version, timestamp and execution result. Test replay, substitution, race and approval-confusion attacks.
Limit loops, chains and spending
Set maximum runtime, retries, tool-chain depth, tokens, network destinations and budget. Add circuit breakers for repeated failures or unusual volume. Verify that an agent cannot reset its own limits through a tool, memory entry or delegated sub-agent.
Protect memory and retrieval
Require provenance and trust labels before persistence, constrain memory to the user and purpose that created it, define expiry and support deletion. Scan or quarantine untrusted content before it becomes durable context. Test cross-user retrieval, stale permissions, poisoned summaries and instructions that survive source removal.
Harden the surrounding infrastructure
Apply normal identity, network, secret-management, dependency and logging controls to the agent runtime. Review connectors and tool providers as supply-chain components. A secure prompt cannot compensate for a connector that grants broader authority than the initiating task.
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Start with a repeatable open-source or internal harness if it can exercise the actual agent, tools and expected outcomes. OpenAI’s red-teaming documentation points to Promptfoo as an open-source framework for evaluating prompts, agents and AI applications. NIST describes AgentDojo, an open-source framework with simulated Workspace, Travel, Slack and Banking environments, for studying agent hijacking. These are examples, not assurances that either covers every risk.
When comparing vendors or tools, ask whether they can test tool-calling, retrieval, memory, MCP and multi-agent architectures; adapt attacks to observed behavior; inspect tool outputs; preserve evidence; protect test data; run in a safe environment; and repeat tests after configuration changes. OWASP’s Vendor Evaluation Criteria for AI Red Teaming Providers & Tooling v1.0, published February 4, 2026, is aimed at systems ranging from chatbots and RAG to tool-calling and multi-agent workflows.
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Microsoft documents a cloud AI Red Teaming Agent that tests goal achievement, rule compliance, procedural discipline, prohibited actions and sensitive-data leakage. Its documentation notes limitations for some categories, including single-turn, English-only sensitive-data testing with synthetic data. Verify current capabilities, service regions and data handling before relying on it for your environment.
Report findings so engineers can reproduce them
Each finding should include the scope and authorization, environment, agent and model version, tools and policies, scenario identifier, exact untrusted input, expected outcome, observed trace, permissions, approvals, side effects, severity rationale, remediation owner and retest status. Attach redacted evidence and preserve hashes or immutable references when chain of custody matters.
Describe impact in operational terms: data exposed to which recipient, authority crossed, action attempted or completed, policy violated, number of retries, cost incurred and whether the effect was reversible. Distinguish a model-only refusal failure from a backend authorization failure; the latter usually deserves higher priority because it can remain exploitable even after prompt wording changes.
After remediation, rerun the original attack, a minimally modified version, a different legitimate task using the same tool and a benign control. Record whether the fix reduced capability, introduced a new failure or merely moved the unsafe behavior to another tool or agent. Schedule another assessment after material changes to prompts, tools, memory, retrieval, policies or model providers.
Common mistakes and how to correct them
Testing only direct jailbreaks
Problem: The exercise sends hostile text directly to the model but never places instructions in normal business content.
Correction: Put adversarial text in the email, document, web page, ticket, calendar item, retrieval result and tool response that the agent is designed to process.
Scoring only the final answer
Problem: A polite answer hides an unauthorized read, write or outbound request.
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Using live customer data or production credentials
Problem: The test itself creates a breach or irreversible incident.
Correction: Use synthetic records, mocks, sandbox identities and explicit stop conditions in a production-like non-production environment.
Assuming one pass proves safety
Problem: Known attacks are blocked while an adapted variant succeeds.
Correction: Repeat attempts, vary content and tasks, add novel attacks and preserve every confirmed failure as a regression case.
Trusting approval prompts without backend enforcement
Problem: The interface asks for approval but the tool accepts altered parameters or a different identity.
Correction: Bind approval to exact parameters and enforce authorization at the service boundary.
Publishing sensitive traces
Problem: Logs contain the very secrets the assessment was meant to protect.
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Correction: Redact, restrict, retain only what is necessary and use synthetic fixtures whenever possible.
What a defensible red-team program looks like
A mature program has written authorization, a versioned threat model, scenario pairs tied to legitimate work, least-privilege test identities, synthetic data, mock side effects, complete traces, task-level outcome labels, severity-based remediation and repeatable regression runs. It evaluates whether the agent achieves its goal, follows rules and uses the required procedure—not merely whether its prose sounds harmless.
The central question is always operational: when untrusted content tries to change the goal, expand authority, reveal data or trigger a high-impact action, does the whole system preserve the user’s legitimate intent and stop safely? If the answer is supported by reproducible traces across baseline, adaptive and post-fix tests, the result is useful evidence rather than a reassuring demo.
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