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AI safety testing and red teaming can prove that a particular system failed—or resisted a defined attack—under documented conditions. They cannot prove that an AI system is safe in every situation. A meaningful result is bounded evidence: it applies to a specific version, threat model, interface, test method, participants, and date. Sound assurance combines adversarial exercises with model benchmarks, user and field testing, threat modeling, impact assessment, monitoring, and domain review.
What AI safety testing covers
AI safety testing is an umbrella term for evaluations of an AI system’s capabilities, behavior, risks, and effects on people. It can include automated benchmarks, manual reviews, adversarial probing, usability studies, and observation after deployment. The object may be a base model, a model with safety filters, or a complete application that adds prompts, retrieval, tools, data access, interface controls, and operational procedures.
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That distinction matters. A model can behave acceptably in a laboratory prompt test while the surrounding application exposes a dangerous tool, leaks private data through retrieval, or creates a misleading workflow. Conversely, a weakness in an unguarded base model may be blocked by controls in a deployed product. Every claim should identify exactly what was evaluated.
Red teaming is one method within that umbrella
Red teaming is adversarial testing with a stated purpose and scope. Evaluators act as attackers, abusers, or otherwise challenging users and try to elicit failures under a defined threat model. They may search for unsafe outputs, bypass safeguards, manipulate tools, induce privacy violations, or expose weaknesses in a particular workflow. The term is used broadly, and practices are not yet standardized, so a report should define the method instead of treating “red team” as a universal test.
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What a well-run red-team test can show
A concrete failure under stated conditions
The strongest finding documents the system version, setup, attack or scenario, evaluator access, observed behavior, and the criterion that made the behavior a failure. This establishes that the vulnerability occurred in that configuration. It does not establish how often it occurs outside the test or whether every related attack works.
Differences across scenarios or versions
A structured evaluation can compare behavior across defined cases or product versions when the prompts, access, scoring rules, and failure definitions are consistent. Repeatable methods make a change in results more interpretable: a mitigation may reduce a measured failure rate, or an update may introduce a regression. Anthropic has noted that inconsistent red-team practices make objective comparisons difficult, so published comparisons need methodological detail.
Evidence from different kinds of testers
Expert testers can investigate specialized threats with technical or domain knowledge. Crowd participants can generate a wide range of unexpected interactions. Community testers may bring lived experience of harms that an internal team overlooks. Language-model-assisted approaches can expand the number of probes, but they introduce their own coverage and reliability questions. These approaches reveal different portions of the risk surface; none covers every risk by itself.
Where mitigations need work
Findings can identify a weak refusal pattern, an unsafe tool permission, an ambiguous interface, or a missing operational control. Teams can then turn the finding into a measurable test, apply a mitigation, and run a targeted retest. Microsoft’s comments hosted by NIST describe red teaming as one part of a broader process that maps risks and feeds measurement and management.
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How behavior looks in a broader application context
NIST’s ARIA approach combines model testing, red teaming, and user or field testing to examine both technical and contextual robustness. Testing the application with representative users can expose failures that do not appear when a model is queried in isolation, such as confusing instructions, unsafe defaults, or harmful interactions with connected tools.
What red teaming cannot establish by itself
It is not an exhaustive search
A red-team exercise samples attacks and scenarios. The absence of a discovered failure means only that the chosen participants did not find one within the available time, access, and cases. They may have missed another prompt, language, attack path, user behavior, integration, or deployment condition. A failure not found is not proof that it does not exist.
It is not a complete risk assessment
Microsoft’s 2024 comments characterize red teaming as scoped and point-in-time. Red teaming alone does not provide a comprehensive assessment of all capabilities and risks, continuous measurement, detection and prevention of malicious activity, rapid feedback about every known risk, or sector-specific impact assessment. Threat modeling, systematic measurement, monitoring, and domain expertise are needed for those functions.
It does not automatically describe the deployed system
A test of a model in isolation may omit retrieval data, external tools, permissions, moderation layers, interface behavior, human review, logging, and other operational processes. The complete application can therefore behave differently from the tested model. Reports should state whether they tested the base model, a safeguarded product, or the full system and its surrounding services.
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A score is not automatically comparable
Two teams can report different failure rates while measuring different things. Threat model, participant expertise, system access, tools, sampling, languages, scenario design, and pass/fail criteria all affect results. Anthropic’s discussions of AI red teaming emphasize the current lack of standardization. A number without its method is not a fair cross-system comparison.
A passing result is not permanent
Models, safeguards, attack techniques, connected services, and user populations change. A passing test describes behavior in a particular evaluation context and period. Repeated evaluation and post-deployment monitoring are required after model updates, policy changes, new tools, or changes in the operating environment.
How NIST’s ARIA framework separates evaluation questions
NIST presents model testing, red teaming, and user or field testing as complementary levels rather than interchangeable names. The level determines what evidence is collected and what conclusions are reasonable.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute| Evaluation level | Primary question | Typical object or setting | What it adds |
|---|---|---|---|
| Model testing | How does the model behave on defined capabilities, safety, or robustness measures? | A model evaluated with controlled inputs and scoring | Repeatable measurements that can be tracked across versions |
| Red teaming | Can adversarial evaluators expose failures under a specified threat model? | A model or application with defined attacker access | Concrete weaknesses and attack paths that scripted tests may miss |
| User testing | How do representative people understand and use the system? | Participants interacting with an application and its interface | Evidence about usability, misunderstanding, and context-dependent harms |
| Field testing | What happens under real or realistically simulated deployment conditions? | An application operating with its intended workflows, data, and controls | Contextual and operational evidence beyond laboratory behavior |
NIST’s ARIA program overview describes model testing, red teaming, and field testing as three evaluation levels aimed at technical and contextual robustness. The ARIA Evaluation Planning Manual, published September 18, 2026, provides planning guidance for combining these elements.
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How to design a red-team exercise that produces usable evidence
- Define the decision and threat model. State which harm or capability is under investigation, who the attacker or affected user is, what resources they have, and what counts as success for the attack. A threat model that says only “find unsafe behavior” is too vague to support interpretation.
- Name the system boundary and version. Record the model identifier, application build, safety controls, tools, data sources, permissions, interface, and test date. Say whether evaluators can call the model directly, use the public product, or interact with the complete deployment.
- Choose participants and access deliberately. Match expertise to the threat. Technical specialists may be necessary for tool or security attacks; domain experts may be needed for medical, legal, financial, or other high-consequence contexts; broader participants can reveal varied language and user behavior. Document what each group could see and do.
- Specify scenarios and coverage. Include the languages, user groups, deployment conditions, connected components, and attack families that matter to the decision. List exclusions as clearly as inclusions so readers know what the result does not cover.
- Set reproducible failure criteria. Define the output, action, or impact that constitutes a failure before testing begins. Use a consistent recording format for prompts, tool calls, screenshots or logs where appropriate, severity, and evaluator judgment. Open-ended exploration is useful for discovery, but structured cases are needed for measurement.
- Separate discovery from measurement. Exploratory testers can find novel attacks; a second pass can convert confirmed cases into repeatable tests. Report how many cases were attempted, how many were reproduced, and how severity was assigned, without implying that the sample represents every possible attack.
- Feed findings into mitigation and retesting. Assign owners, change the relevant control, and rerun the original case plus related variants. Keep the original result so a lower post-mitigation rate is not mistaken for proof that the risk has disappeared.
- Publish boundaries and residual risk. State the date, environment, participant mix, methods, limitations, unresolved findings, and monitoring plan. A transparent limitation is more informative than a broad safety claim.
How to read an AI safety or red-team report
- Question: Does the report identify a precise harm, capability, or threat model?
- System: Is the tested object the base model, a product with safeguards, or the full application?
- Access: What tools, accounts, data, rate limits, and permissions did testers have?
- Participants: Who tested it, what expertise did they have, and how were cases sampled?
- Method: Are scenarios, prompts, scoring, and failure criteria documented well enough to repeat?
- Coverage: Which languages, user groups, integrations, and deployment settings were included or excluded?
- Evidence: Are individual findings tied to a version, setup, and observed behavior rather than only a headline score?
- Follow-through: Were findings converted into measurements, mitigations, retests, monitoring, and domain-specific review?
If several of these answers are missing, treat the report as an indication of selected behavior, not a broad safety certification.
Expert, crowd, community, and model-assisted red teaming
Anthropic describes several approaches, each suited to different questions. Expert exercises provide depth in a specialized threat area but may cover fewer perspectives. Crowd testing can produce diverse and unexpected interactions at larger scale, while participant expertise and case quality may vary. Community testing can surface harms experienced by particular groups, provided recruitment and consent are handled responsibly. Language-model-assisted probing can generate many candidate attacks quickly, yet automated probes still need validation because they may repeat narrow patterns or misjudge impact.
The practical choice is not which method is universally best. Match the participants and tools to the threat model, use consistent scoring when comparison matters, and combine methods when the risk spans technical, social, and domain-specific conditions.
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NIST’s ARIA 0.1 pilot evaluation, reported November 13, 2025, involved five organizations that submitted seven AI applications. The pilot used three scenarios—TV Spoilers, Meal Planner, and Pathfinder—and examined three testing levels: model testing, red teaming, and field testing. Those figures describe the design and participation of that pilot; they are not a measure of evaluation effectiveness across the AI industry.
The pilot is useful as an illustration of layered evaluation. A model-level result can be checked against adversarial findings, while field evidence can show whether people encounter problems in realistic use. The layers answer different questions and should not be collapsed into one “safety score.”
Evidence that should accompany red teaming
Threat modeling
Map assets, affected people, attack capabilities, incentives, and plausible failure paths before choosing tests. This helps reveal risks that an ad hoc prompt hunt may never attempt.
Systematic measurement
Turn important findings into repeatable cases, severity rules, and version-to-version metrics. Measurement makes regressions visible, while exploratory testing remains valuable for discovering new cases.
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Estimate who could be harmed, how severe the consequences could be, and whether harms are concentrated among particular groups or domains. A technically rare failure may still deserve priority if its impact is high.
Monitoring and incident response
After deployment, monitor relevant behavior, abuse signals, user reports, and changes in connected systems. Define escalation and rollback procedures so a newly observed failure can lead to action rather than merely another report.
Domain-specific review
People with appropriate professional or community knowledge can judge consequences that generic safety labels miss. Their review should cover the actual workflow and affected population, not only isolated model outputs.
The practical conclusion
Use red teaming to find and document weaknesses, test whether mitigations work, and challenge assumptions in a defined threat model. Use model benchmarks for repeatable measures, user and field testing for context, and ongoing monitoring for change over time. The defensible claim is always specific: this system version behaved this way under these conditions, with these methods and limitations. Anything broader requires additional, clearly identified evidence.
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