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AI red teaming is one adversarial method inside a broader safety-evaluation program. A red team deliberately tries to make a model or application produce harmful, misleading, insecure, or otherwise unwanted results. A broader evaluation can also test stated capabilities, benchmark safeguards under defined conditions, and observe people using the application in realistic or field-like settings.
A successful red-team attack is concrete evidence that a tested weakness can be triggered. A clean red-team exercise is not proof that the system is safe: it says nothing certain about untested prompts, users, tools, contexts, model updates, or downstream effects. Trustworthy deployment decisions therefore combine adversarial testing with model tests and user or field testing, then connect the findings to mitigation and governance.
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What “AI red teaming” means
NIST’s Generative AI Profile, released July 26, 2024, defines the practice as follows: “AI red-teaming is an evolving practice that references exercises often conducted in a controlled environment and in collaboration with AI developers building AI models to identify potential adverse behavior or outcomes of a GAI model or system, how they could occur, and stress test safeguards”.
In practical terms, a red team acts like an informed adversary. Testers probe prompts, interfaces, tools, permissions, retrieval sources, and workflows to find ways around safeguards or to expose behavior that ordinary quality testing might miss. They may begin with known hazards—such as disallowed content, privacy leakage, insecure code, or unsafe recommendations—or explore for unexpected failure modes.
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The object under test must be stated precisely. A campaign may target the base model, a fine-tuned model, a complete chatbot, an agent with tools, or a production application with authentication, retrieval, moderation, and logging. Results from one boundary do not automatically transfer to another. A model that resists a prompt in a laboratory harness may behave differently when wrapped in an application that supplies additional context or grants tool access.
What red teaming is designed to find
- Adverse outputs and the conditions that trigger them.
- Attack paths that bypass refusal, filtering, monitoring, or access controls.
- Interactions among the model, tools, retrieved documents, users, and application logic.
- Unanticipated risks that were not represented in a fixed benchmark.
- Evidence about whether a proposed safeguard holds under pressure.
Red teaming is therefore diagnostic and adversarial. It is useful for discovering and reproducing problems, but it is not a universal score or a certification label.
What a safety evaluation covers
“Safety evaluation” describes the larger activity of gathering evidence about whether an AI system behaves acceptably for specified risks, users, and settings. It can include red teaming, but it may also include capability and benchmark tests, robustness checks, human review, simulated deployment, and field-style observation.
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Model testing and benchmark evaluation
Model tests run predetermined prompts, datasets, or scenarios and measure outcomes against declared criteria. They can quantify performance, consistency, calibration, refusal behavior, or other guardrail properties under those conditions. Because the test set and scoring rules are fixed, results can be compared across versions when the procedure is held constant.
A benchmark remains bounded by its content and measurement choices. A high score means the model performed well on the specified tests; it does not establish safe behavior for every user, prompt, tool, language, or future update.
User and field testing
User testing places people in structured tasks that resemble intended use. Field-style testing goes further by observing interactions in settings that approximate deployment, with realistic interfaces, workflows, and constraints. When large and appropriately sampled groups participate, field-style data can support estimates of the likelihood of risks and impacts during real-world interactions.
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These methods reveal usability problems, confusing refusals, workarounds, over-reliance, and effects that a model-only test cannot see. They still depend on the sample, scenario, duration, and setting; no field study covers every future deployment condition or every subgroup.
Red teaming and safety evaluation compared
| Approach | Evidence it can produce | What it does not establish alone |
|---|---|---|
| Model testing or benchmark evaluation | Performance on specified capabilities, hazards, or guardrails under defined conditions. | Real-world safety across all users and contexts unless the design and evidence justify that inference. |
| Red teaming | Potentially adverse behavior, attack paths, safeguard weaknesses, and unanticipated risks elicited during adversarial probing. | That risks are absent, that a finding represents its population-wide prevalence, or that downstream impacts have been fully measured. |
| User or field testing | Behavior, experience, and impacts in structured or deployment-like settings; with suitable large groups, estimates of risk likelihood in those interactions. | Every future condition or every subgroup’s experience. |
| Combined evaluation | A rounded view of technical performance, adversarial robustness, and contextual or user effects. | Absolute assurance. Conclusions remain limited by selected risks, scenarios, metrics, access, system version, and sampled contexts. |
How a red-team campaign works
A credible campaign is more than asking a model a few provocative questions. The team defines the boundary and objectives, gives testers appropriate access, records reproducible interactions, and routes findings into remediation and retesting.
- Define the system and threat scope. Record the model version, application components, tools, retrieval sources, user roles, languages, and environments included. State whether the target is a model or the deployed application.
- Set risk objectives. Identify hazards to probe, such as unsafe advice, privacy disclosure, cyber abuse, discrimination, misinformation, or tool misuse. Separate known risks from exploratory discovery.
- Assemble an appropriate team. Combine technical security or machine-learning expertise with domain, demographic, and contextual knowledge relevant to the application. NIST discusses expert, general-public, mixed, and human/AI teams; the right mix depends on the risk.
- Design scenarios and access. Specify prompts, conversation histories, accounts, tools, rate limits, and environmental conditions. Controlled access improves repeatability; realistic access reveals application-level weaknesses.
- Probe and document. Save prompts, model outputs, tool calls, screenshots or logs, timestamps, model identifiers, and the exact steps needed to reproduce each behavior. Record near misses as well as clear failures.
- Classify and validate findings. Apply outcome labels and severity criteria consistently. Have another evaluator reproduce important findings and distinguish a model defect from an application, policy, or configuration problem.
- Mitigate and retest. Patch prompts, policies, filters, permissions, training data, or tool boundaries as appropriate. Re-run the original attack and related variants to check whether the fix generalizes.
- Analyze before making a governance claim. NIST cautions that “AI red-teaming results should be given additional analysis before they are incorporated into organizational governance and decision making, policy and procedural updates, and AI risk management efforts.”
Why a clean campaign is limited evidence
Red-team output depends on who tested, what they could access, how long they tested, which languages and modalities they used, and how success was labeled. NIST describes red teaming as an evolving practice and notes that team expertise affects output quality. A team may fail to find a vulnerability because it lacked the relevant skill, never reached the triggering state, or tested the wrong system boundary.
How broader safety evaluations answer different questions
Start with claims, not a favorite test
Write the deployment claims first: for example, that a system will not disclose protected data to an authenticated user, will provide safe meal-planning advice, or will route high-risk requests to a human. Each claim needs an observable outcome, an acceptance rule, and a test population or scenario. Red teaming can challenge the claim, while benchmarks and user tests can measure other parts of it.
Use model tests for repeatable measurements
Run fixed suites against the exact model and configuration intended for release. Track version, temperature or sampling settings, system instructions, tool permissions, and scoring rules. Repeat tests after updates so a mitigation in one area does not create regression elsewhere.
Use field-style tests for context and impact
Expose representative tasks through the intended interface, with realistic latency, error handling, retrieval, and escalation paths. Gather outcome data and user feedback, not only model-text ratings. Analyze whether people understand warnings, follow unsafe suggestions, abandon tasks, or develop workarounds.
Use red teaming to search the space between planned cases
Adversarial testers can chain harmless-looking actions, exploit ambiguous instructions, combine languages or modalities, and target interactions among components. Their findings should expand the fixed test suite rather than remain isolated anecdotes.
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NIST’s ARIA approach in practice
NIST’s Artificial Intelligence Risk and Impact Assessment (ARIA) materials explicitly combine testing levels instead of treating red teaming as the entire assessment. The ARIA Pilot Evaluation Report, published November 13, 2025, describes three levels: model testing, red teaming, and field testing.
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The pilot involved five organizations that submitted seven AI applications. Its scenarios were named TV Spoilers, Meal Planner, and Pathfinder. The report describes dialogue annotation, tester questionnaires, and measurement trees as parts of the assessment approach. These details illustrate how technical outputs, adversarial interactions, and user-facing experience can be examined together.
NIST’s ARIA Evaluation Planning Manual, published September 18, 2026, presents a way to plan customized evaluations that combine model testing, red teaming, and user testing. It is planning guidance, not a universal pass/fail certification. NIST’s AI Risk Management Framework is voluntary and under revision, so organizations should verify the status and version before describing it as the latest applicable framework.
Six comparison axes for an evaluation plan
When reviewing an internal report, outside assessment, or proposed test plan, use these questions.
1. What is the system boundary?
Was the underlying model tested, the complete application, or both? A chatbot’s prompt policy, retrieval layer, plug-ins, identity controls, and logging can introduce risks that a model-only harness cannot reveal.
2. What purpose and risks were covered?
Look for a distinction between capability measurement, a known hazard, safeguard robustness, and exploratory discovery. A campaign optimized for jailbreaks may say little about discrimination, reliability, or user over-reliance.
3. What setting and access did testers have?
Controlled tests improve repeatability. Field-like tests expose operational behavior. Record model, tools, interfaces, accounts, rate limits, and other conditions because conclusions transfer only when those conditions are comparable.
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4. Who participated?
Relevant technical, domain, demographic, and contextual expertise affects what testers notice and how they interpret harm. A narrow expert team and a broad public panel answer different questions.
5. Can another team interpret and repeat it?
Reports should document prompts or scenarios, outcome labels, metrics, sampling, annotation rules, missing data, and limitations. ARIA’s pilot use of dialogue annotation, tester questionnaires, and measurement trees shows the value of making measurement choices visible.
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Strong programs link findings to a mitigation, a changed test, a residual-risk assessment, or a deployment restriction. A red-team event with no owner, deadline, or retest provides weak support for a safety claim.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to combine methods before deployment
- Map the intended use and harms. Describe users, tasks, data, tools, affected third parties, and credible failure consequences.
- Choose measurable claims. Define what acceptable behavior looks like and what evidence would trigger blocking, escalation, or additional controls.
- Run baseline model and application tests. Establish repeatable performance and guardrail measurements on the release candidate.
- Conduct targeted red teaming. Attack the highest-consequence assumptions and probe component interactions, privilege boundaries, and known failure patterns.
- Conduct user or field-style testing. Observe realistic workflows, comprehension, reliance, accessibility, and differences among relevant user groups.
- Triangulate findings. Compare benchmark failures, red-team exploits, and user-impact signals. A failure seen by several methods deserves priority, but a result seen by one method should not be discarded.
- Mitigate and repeat. Re-run fixed tests and adversarial variants after changes. Track regressions across model, prompt, policy, tool, and interface updates.
- Make a bounded decision. State the version, tested settings, residual risks, monitoring plan, rollback trigger, and conditions under which the decision no longer applies.
Common mistakes and their fixes
Calling a red-team pass a safety certificate
Problem: “No attack succeeded” is treated as “the system is safe.” Fix: Report the tested scope, access, duration, team composition, and untested risks; combine the result with model and user evidence.
Testing only the base model
Problem: Application logic, retrieval, tools, or permissions are ignored. Fix: Test the complete user-facing system and preserve a separate model-level suite for regression tracking.
Using a fixed benchmark as a proxy for prevalence
Problem: A benchmark score is presented as the rate of safe behavior in the world. Fix: Limit the claim to the benchmark conditions unless sampling and design support a broader estimate.
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Problem: A technically strong team misses domain or demographic harms, or a public panel cannot investigate technical exploit chains. Fix: Match expertise and participant mix to the risk, and document what each group could and could not assess.
Publishing findings without retesting
Problem: A patch blocks the reported prompt but leaves variants open, or creates a new usability failure. Fix: Reproduce the original issue, test related variants, and run regression suites after every material change.
Ignoring negative evidence limits
Problem: A short or narrow campaign is interpreted as evidence that no failure exists. Fix: Treat non-discovery as “not observed under these conditions,” then expand scenarios, teams, languages, and time where the risk warrants it.
What a responsible report should say
A useful report lets a reader distinguish observation from inference. It should identify the system version and boundary; objectives; participants and expertise; access and setting; scenarios and sampling; metrics and labels; confirmed findings; unresolved questions; mitigations; retest results; and residual risks.
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Bottom line for deployment decisions
Red teaming answers, “Can informed adversaries trigger an undesirable behavior here, and how?” Model testing answers, “How does the system perform on these specified cases?” User and field testing answer, “What happens when people use it in realistic settings, and what risks or impacts appear?” None of those answers substitutes for the others.
The defensible claim is therefore conditional: a system has been evaluated for named risks, with named methods, on a named version and boundary, and the remaining uncertainty is documented. Combining methods improves coverage and decision quality, but it never turns finite tests into absolute assurance.
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