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What is the difference between AI safety testing and red teaming?
“AI safety testing” is used here as a broad term for planned evaluation of an AI system against risks, trustworthiness goals, and intended conditions of use. NIST’s guidance separates several evaluation approaches rather than defining one exhaustive method under that phrase. Red teaming is a structured exercise within this wider work: testers probe a system, often adversarially, to find flaws, vulnerabilities, harmful behavior, or ways safeguards can fail.
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NIST’s AI-specific glossary defines red teaming as “a structured testing effort, often adopting adversarial methods, to find flaws and vulnerabilities in an AI system, including unforeseen or undesirable system behaviors or potential risks associated with the misuse of the system.” NIST CSRC glossary attributes the definition to its 2025 adversarial machine learning terminology. The AI-specific meaning matters: it is not simply interchangeable with a conventional cybersecurity red team exercise against an organization’s networks.
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How the main evaluation methods fit together
NIST’s Generative AI Profile, ARIA program materials, and 2026 evaluation-planning manual distinguish model testing, red teaming, and field or user testing. They answer different questions and can contribute to one evaluation plan.
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| Approach | Main question | How it works | What it contributes | Key limitation |
|---|---|---|---|---|
| Model testing | Does the system meet defined behavioral criteria? | Structured scenarios and measurements. | Repeatable evidence about specified properties. | Can miss risks outside the selected tests. |
| Red teaming | Can an adversarial or harmful interaction expose a weakness? | Exploratory, adversarial probing. | Can reveal unexpected failure modes and gaps in safeguards. | Does not provide comprehensive capability or risk measurement on its own. |
| Field or user testing | What behavior and impacts appear in realistic use or user interaction? | Deployment-like conditions or user studies. | Context about use, impacts, and user experience. | Requires careful design to reflect context and representative use. |
This distinction is reflected in NIST’s ARIA program, which describes model testing, red teaming, and field testing and considers technical as well as contextual robustness. Its September 18, 2026 evaluation planning manual describes a holistic evaluation combining model testing, red teaming, and user testing.
What red teaming can—and cannot—tell you
A red-team exercise is designed to uncover weaknesses that may not appear in routine or narrowly specified tests. For generative AI, that can include adverse behavior, misuse risks, unexpected outputs, or attempts to defeat safeguards. NIST describes the practice as evolving and says exercises are often conducted in a controlled setting in collaboration with developers; they may take place before or after public availability. NIST AI 600-1, the Generative AI Profile, also emphasizes that tester background and expertise affect the quality of red-team work, and recommends domain knowledge and awareness of sociocultural context.
A finding is evidence of a weakness to investigate, not a complete measure of how often it will occur or what impact it will have in every real-world setting. Conversely, failure to uncover a problem during an exercise does not show that no such problem exists. Findings need analysis and follow-up before they inform governance or risk decisions; NIST’s guidance does not present red teaming as a guarantee of safety.
When should you use red teaming versus other testing?
Choose methods based on the risk question and the system’s intended context, rather than treating them as competing alternatives.
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- Use model testing when you need repeatable measurements against defined requirements or scenarios.
- Use red teaming when you need to probe for vulnerabilities, safeguard bypasses, or unexpected behavior—especially where adversarial or harmful use is plausible.
- Use field or user testing when you need evidence about behavior, experience, or impacts in realistic use or through interaction with intended users.
- Combine methods when the stakes or deployment context warrant multiple lenses. A structured test may measure known risks, red teaming may explore less predictable ones, and contextual testing may show how a system is used and experienced.
For useful red-team results, the exercise needs testers with relevant technical and domain expertise, a clear scope, and a process for analyzing and acting on findings. NIST’s profile discusses different participant types; the appropriate team and test design depend on the system and the risks being evaluated.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How NIST guidance fits into an AI safety program
NIST’s AI Risk Management Framework (AI RMF) treats trustworthiness as relevant throughout design, development, deployment, use, and test and evaluation. NIST describes the framework as voluntary, and its current AI RMF page reports that version 1.0 is under revision. NIST AI Risk Management Framework is a reference for organizing risk management, not a legal requirement to conduct a particular red-team exercise.
For security terminology, NIST AI 100-2 E2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, was published in March 2025; NIST says a corrected PDF was uploaded April 1, 2025. It helps clarify adversarial machine-learning terms, but it is not a complete general-purpose safety-testing plan.
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