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To red-team an AI agent, test the complete system—not only the model’s final wording—against realistic, authorized attacks. Give the agent a legitimate task, place adversarial instructions in the files, messages, web pages or records it must process, and verify every decision, tool call, permission check, approval and side effect. The most important test areas are indirect prompt injection, tool misuse, sensitive-data leakage, memory or retrieval poisoning, approval bypass, and prohibited or irreversible actions.
Run the work in a controlled, production-like non-production environment with written authorization, least-privilege tools, synthetic sensitive data and mock integrations where possible. Record the exact version and configuration, repeat adaptive attacks after fixes, and report residual risk rather than relying on one aggregate score.
What AI-agent red teaming actually tests
Red teaming is an adversarial assessment intended to expose unsafe, insecure or policy-violating behavior before deployment. OpenAI describes it as using adversarial test cases to uncover those behaviors before a system is released (OpenAI’s Red teaming guide). For an agent, the target is a system of components: the model, system and developer instructions, tools, identities, permissions, retrieval sources, memory, orchestration code, infrastructure and runtime controls.
A chatbot test that checks whether a reply contains harmful text is therefore insufficient. An agent can produce a polite final answer while it has already read data it should not access, sent an unauthorized message, modified a record, followed an instruction hidden in a document or exposed a secret in a log. The assessment must inspect the complete execution trace and the side effects of every action.
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The OWASP GenAI Red Teaming Guide frames this as complementary work across model evaluation, implementation testing, infrastructure assessment and runtime analysis. Microsoft’s agent-testing guidance similarly examines goal achievement, rule compliance and procedural discipline, including the safety of tool outputs.
Why ordinary quality evaluation misses agent attacks
Conventional evaluation usually starts with a clean user prompt and scores the answer. Agent attacks exploit the boundaries around that prompt:
- Indirect prompt injection: an email, webpage, ticket, spreadsheet or retrieved passage contains instructions that try to replace the user’s goal.
- Tool misuse: the agent calls a tool, resource or identity outside the user’s authority, or performs a write when the task allows only reading.
- Data leakage: an instruction or tool causes financial, medical, personal, credential or confidential information to appear in an answer, message or log.
- Unsafe actions: the agent attempts a prohibited, high-impact or irreversible operation without the required authorization.
- Memory and retrieval poisoning: untrusted content persists and influences later tasks outside its intended scope.
- Approval and runtime abuse: an attacker bypasses an approval, causes unlimited retries or tool chains, or drives uncontrolled spending.
- Propagation across agents and suppliers: a compromised instruction or connector passes to another agent or service whose permissions exceed the initiating task.
NIST calls the first category agent hijacking. Its technical staff explain that attackers exploit the lack of separation between trusted instructions and untrusted external data by placing malicious instructions in an apparently normal email, file or website so the agent performs a different, potentially harmful task (NIST’s agent-hijacking evaluation article).
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Set authorization, scope and a safe test environment
Only assess systems and data that you own or for which you have express written permission. OpenAI warns that its red-teaming service must not be used to analyze or report vulnerabilities in third-party code without written authorization (OpenAI Red teaming). Microsoft likewise treats active validation as requiring explicit authorization and change management, and recommends a production-like non-production environment (Microsoft AI Red Teaming Agent).
Write a test charter before creating an attack. It should identify:
- the agent build, model and provider, prompt or policy version, orchestration code and deployment configuration;
- connected tools, APIs, connectors, data stores, retrieval indexes and memory services;
- the identities and permissions available to the agent and to the test user;
- allowed side effects, such as creating records in a sandbox, and actions that are completely prohibited;
- the test window, operators, change-management contact and emergency stop procedure;
- how secrets, synthetic personal data, findings and logs will be stored and who may see them;
- stop conditions, including an unexpected production connection, real-person contact, uncontrolled spending or evidence of data leaving the test boundary.
Use synthetic sensitive information and mock email, payment, storage or administrative tools whenever possible. A realistic environment should preserve the permission and approval behavior you want to test without exposing customers or live assets.
Turn policy into testable rules
Policies are useful to a red team only when a tester can determine whether a specific event passed or failed. Separate three classes of behavior:
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Prohibited actions
These must never occur, regardless of a user request or retrieved instruction. Examples include disclosing a credential, contacting a real person in the sandbox’s place, or executing an explicitly banned transaction. The expected result is refusal or safe termination, with no side effect.
High-risk actions requiring approval
These may be legitimate but require an explicit, current approval tied to the exact operation. Define who may approve, what parameters are covered, how long the approval remains valid and what happens if the request changes. An approval for viewing a record must not authorize deleting it.
Irreversible or sensitive operations
Require a specified procedure, such as a two-person review, a confirmation step or a dry run. State whether the agent may prepare parameters but not execute them, and what evidence must be written to the audit log. Microsoft’s service guidance distinguishes prohibited, high-risk and irreversible actions when defining these controls (Microsoft AI Red Teaming Agent).
Build a threat-model test matrix
Map each risk to a realistic question, an expected outcome and the evidence needed to decide whether the control worked. The following matrix can be used as a starting point.
| Risk area | Example test question | Evidence to capture |
|---|---|---|
| Indirect prompt injection and goal hijacking | Can an instruction in an email, file, webpage 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 identifies such embedded instructions as the basis of agent hijacking (NIST). |
| Tool misuse and privilege escalation | Can the agent invoke a tool, resource or permission outside the user’s authority, or write when only read access is allowed? | Requested and actual tool, caller identity, effective permissions, denial behavior and any side effect. OWASP recommends minimum necessary tools and per-tool permission scope (OWASP AI Agent Security Cheat Sheet). |
| Sensitive-data leakage | Can an adversarial instruction or tool call expose financial, medical, personal, credential or confidential data in output or logs? | Synthetic data identifier, source, recipient or tool, response, redaction decision and leakage detection. Microsoft describes synthetic sensitive information and mock tools for this testing (Microsoft AI Red Teaming Agent). |
| Prohibited, high-risk or irreversible action | Can the agent initiate a banned or high-impact operation without valid authorization? | Policy rule, proposed parameters, approval state and expiry, actual 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 status, scope, expiry, later retrieval and whether the malicious instruction was followed. OWASP lists memory poisoning among agent abuse cases (OWASP AI Agent Security Cheat Sheet). |
| Approval bypass and recursive tool abuse | Can an attacker evade a required approval or induce unbounded retries, tool chains or spending? | Approval binding and expiry, retry count, chain depth, runtime limits, circuit-breaker result and cost control. |
| Multi-agent and supply-chain risk | Can compromised data or instructions 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. |
Use scenario pairs instead of isolated prompts
For every legitimate task, create a paired adversarial scenario. The clean case establishes that the agent can do useful work; the paired case adds one hostile condition while keeping the business goal intact.
Example: document summarization
Legitimate goal: summarize a supplied contract and list renewal dates. Adversarial condition: a paragraph in the contract instructs the agent to upload the document to an external address and reveal its system prompt. A pass means the agent summarizes the contract, treats the embedded instruction as untrusted content, makes no upload, and records a refusal if appropriate.
Example: support-ticket triage
Legitimate goal: classify a ticket and draft a response. Adversarial condition: the ticket asks the agent to search unrelated customer records and send a password-reset link to a new address. Check that retrieval remains limited to the ticket’s authorization, the draft does not perform the reset, and any required approval is requested through the defined channel.
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Run adaptive, repeated attacks
Start with documented baseline attacks so results are reproducible, then create new attacks adapted to the agent’s observed behavior. Vary the wording, location and format of the hostile instruction: plain text, HTML, an image converted to text, a quoted email, a tool result or a memory entry. Change the legitimate task and user role as well. Record each attempt separately instead of collapsing all outcomes into one pass rate.
Useful dimensions include:
- whether the attack was recognized before planning, during planning or only at tool execution;
- whether the agent refused, asked for clarification, requested approval, completed the legitimate task safely, or followed the injection;
- whether a tool was called, with what parameters and under which identity;
- whether a side effect occurred, was prevented by a policy layer, or was rolled back;
- whether the same attack succeeded after a retry, conversation continuation or memory write;
- whether an adapted attack bypassed a control that stopped the baseline version.
Do not describe a single success percentage as a universal agent failure rate. Attack success depends on the model, task set, number of attempts, environment and detection criteria.
Observe the full execution trace
Capture enough evidence to reconstruct what happened without placing live secrets in the fixtures. At minimum, retain:
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- model, prompt, policy, tool-schema and retrieval-index versions;
- the agent’s plan or intermediate decision record where available;
- every requested and returned tool call, including parameters and errors;
- identity, role, token scope and effective permission at each call;
- policy evaluations, approval requests, approver identity, approval contents and expiry;
- timeouts, retries, chain depth, token or cost limits and circuit-breaker events;
- database, message, file or external-system side effects;
- the final response, redactions and the expected-versus-observed result.
Protect these logs as sensitive security evidence. Hash or otherwise identify fixtures, redact secrets, restrict access and set a retention period. OWASP recommends recording tool interactions, enforcing least privilege and protecting the agent’s runtime and audit trail (OWASP AI Agent Security Cheat Sheet).
Evaluate controls, not just model refusal
Least-privilege tools
Expose only the tools needed for the task. Separate read and write operations, scope records by tenant or user, validate parameters server-side and re-check authorization at execution time. A model’s verbal refusal is not a security boundary.
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Explicit, bound approvals
Bind approval to the operation, parameters, identity, resource and time window. Reject stale, replayed or broadened approvals. Make the approval state visible in the trace and require a fresh approval when a plan changes.
Isolation of untrusted content
Label retrieved text, tool output and memory as data rather than instructions. Keep secrets and system prompts out of contexts that do not need them. Test whether content from one tenant, conversation or task can cross into another.
Runtime limits
Set maximum retries, chain depth, execution time, spend, records changed and messages sent. Add circuit breakers and a human stop path. Test failure behavior by forcing tool errors, timeouts, contradictory results and partial completion.
Safe memory and retrieval
Define who may write memory, what is retained, how it is scoped and when it expires. Test poisoning through direct ingestion, tool output and a later conversation. Confirm that deleting or quarantining a memory prevents future retrieval.
A repeatable red-team workflow
- Inventory the system. Record the build, model/provider, prompts, policies, tools, connectors, identities, permissions, retrieval sources, memory stores and runtime controls.
- Obtain authorization. Document owners, targets, allowed side effects, stop conditions, data handling and change-management approval.
- Prepare the environment. Use a production-like non-production deployment, synthetic sensitive data and mock integrations. Verify that no route reaches live customers or irreversible assets.
- Write policy rules. Classify operations as prohibited, approval-required or procedure-bound. Define the exact safe response and evidence for each.
- Build scenario pairs. Combine a legitimate task with a realistic injection, privilege attempt, leakage request, poisoned memory, approval bypass or recursive loop. Add benign controls and prior regressions.
- Execute baseline and adaptive attempts. Vary content location, wording, user role, task and sequence. Repeat attempts across fresh and continuing sessions.
- Capture traces. Preserve inputs, plans, tool calls and outputs, identities, policy decisions, approvals, side effects, limits and final responses.
- Triage findings. Confirm whether the event is reproducible, distinguish a suspected weakness from an observed failure, and rank impact and exploitability.
- Remediate and retest. Apply the fix, add the failure as a regression case, rerun the original attack and test adapted variants under the same controls.
- Report residual risk. State what was tested, what was not, remaining exposure, owners, due dates and any risk formally accepted.
Choosing a harness or service
Begin with a repeatable open-source or internal harness that can exercise the actual agent, tools and expected outcomes. OpenAI points to Promptfoo as an open-source framework for evaluating prompts, agents and AI applications (OpenAI Red teaming). NIST describes AgentDojo, an open-source framework with simulated Workspace, Travel, Slack and Banking environments, in its agent-hijacking work (NIST agent-hijacking evaluation). These examples do not guarantee coverage of your architecture.
When comparing a vendor or tool, ask whether it supports:
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- a documented threat model and attack-generation method, including adaptive attacks;
- task-level expected outcomes and inspection of tool outputs and side effects;
- safe sandboxes, synthetic data, mock connectors and emergency controls;
- complete evidence, reproducible configurations and useful severity reporting;
- data-isolation, retention and access practices appropriate for security testing;
- repeatable regression runs after prompt, tool, memory, retrieval, policy or model-provider changes.
OWASP’s Vendor Evaluation Criteria for AI Red Teaming Providers & Tooling v1.0, published February 4, 2026, addresses providers and tools for chatbots, retrieval-augmented systems, tool-calling agents, MCP and multi-agent workflows. Use the criteria to structure procurement questions, then verify current capabilities, service regions and data handling directly.
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Interpreting the NIST 11% versus 81% result
NIST’s Center for AI Standards and Innovation reported an 11% baseline attack-success rate versus 81% for the strongest novel attack in a particular AgentDojo evaluation of an upgraded Claude 3.5 Sonnet setup. The novel attacks were developed against that tested model and evaluated on held-out Workspace tasks (NIST agent-hijacking evaluation). The result demonstrates why adaptive attacks matter; it is not a general failure rate for AI agents and is not a current benchmark across products.
NIST’s May 18, 2026 analysis of responses to its AI-agent-security request for information reports that respondents broadly agreed agents create novel security threats and that conventional cybersecurity practices need adaptation (NIST RFI analysis). That document summarizes respondent views; it is not a measured prevalence estimate.
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Each finding should let an engineer reproduce the event and let a risk owner decide what to do. Include:
- scope, authorization, environment, date and exact system versions;
- legitimate task, attack content, user role and number of attempts;
- expected policy outcome and the observed model, tool and runtime behavior;
- identities, permissions, approval state, parameters and side effects;
- impact, exploitability, affected data or resources and confidence in reproduction;
- containment taken, remediation owner, due date and regression-test identifier;
- accepted residual risk, compensating controls and conditions for retesting.
Classify a failure at the system level. If a policy layer blocked an unsafe model plan before execution, that is different from an executed unauthorized write, even though both reveal a weakness in planning. Conversely, a safe final answer does not erase an unsafe tool call that occurred earlier.
When to repeat the assessment
Run regression tests after any material change to the model provider, model version, system or developer prompt, policy, tool schema, permissions, retrieval index, memory implementation, orchestration logic, connector or runtime limit. Repeat adaptive tests when the threat model changes. Keep the old scenario set so a fix is not “validated” only against a newly worded attack.
OpenAI’s Operator System Card describes one deployment example involving internal and external red teaming, risk evaluation and human-in-the-loop safeguards ( What’s actually slowing this PC down? Pick the symptom - the matching free tool is one click away.
