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What is AI safety testing?
In this article, “AI safety testing” means the broad practice of evaluating an AI system against specified risks, trustworthiness goals, and intended conditions of use. It is an umbrella term, not one universal test or a single exhaustive definition used across every standard.
Evaluation can include several complementary approaches: structured tests that measure defined behaviors, red-team exercises that probe for weaknesses, and field or user testing that examines behavior and impacts in realistic contexts. NIST’s AI Risk Management Framework (AI RMF) treats trustworthiness as relevant across design, development, deployment, use, and testing and evaluation. The framework is voluntary, and NIST reports that AI RMF 1.0 is under revision. NIST AI Risk Management Framework
What is AI red teaming?
NIST defines AI 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: AI red teaming
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In practice, testers deliberately explore how a model or AI-enabled system might fail, be misused, or respond unexpectedly. NIST’s Generative AI Profile describes exercises often conducted in controlled environments and in collaboration with developers to identify adverse behaviors or outcomes, understand how they could occur, and stress-test safeguards. It characterizes the practice as evolving, and notes that exercises may take place before or after public availability. NIST AI 600-1: Generative AI Profile
AI red teaming is related to—but should not be conflated with—traditional cybersecurity red teaming. A security exercise may emulate an attacker targeting an organization’s infrastructure; AI red teaming focuses on flaws, behaviors, and misuse risks in the AI system and its safeguards.
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How the evaluation approaches differ
| Approach | Main question | Typical method | Best contribution | Main limitation |
|---|---|---|---|---|
| Model testing | Does the system meet defined behavioral criteria? | Structured scenarios and measurements | Repeatable measurement of specified properties | May miss risks beyond the scenarios and measures selected |
| Red teaming | Can an adversarial or harmful interaction expose a weakness? | Exploratory, adversarial probing | Can surface unexpected failure modes and safeguard gaps | Does not alone provide comprehensive capability or risk measurement |
| Field or user testing | What behavior and impacts emerge in realistic use or user interaction? | Deployment-like conditions or user studies | Context about use, impacts, and user experience | Requires careful design for context and representative use |
NIST’s ARIA materials distinguish model testing, red teaming, and field testing rather than treating them as interchangeable labels. Its evaluation focus extends beyond performance and accuracy to technical and contextual robustness. NIST Assessing Risks and Impacts of AI (ARIA)
When should you use each approach?
Use model testing to measure known requirements
Choose structured testing when you can define the behavior or property to assess and want results that can be repeated or compared. The test is only as informative as its scenarios, metrics, and assumptions: a clean result does not rule out failures the test was not designed to detect.
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Use red teaming to search for weaknesses
Use an authorized, structured red-team exercise when you need to probe how the system might behave under adversarial pressure or harmful use, or whether safeguards can be bypassed. Tester expertise matters. NIST highlights the value of relevant domain knowledge and awareness of sociocultural context; a poorly scoped or inexperienced exercise can leave important risks unexplored.
Use field or user testing to understand context
Use testing with users or in deployment-like conditions when the important questions concern how people interact with the system, what impacts emerge in practice, or how context changes its behavior. These results complement controlled tests and adversarial exercises; they answer a different set of questions.
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NIST’s September 18, 2026, ARIA Evaluation Planning Manual describes a holistic evaluation that combines model testing, red teaming, and user testing. The right mix depends on the system’s risks and deployment context, rather than on one method serving as a substitute for all the others. NIST ARIA Evaluation Planning Manual
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What red-team findings can—and cannot—tell you
A red-team exercise can reveal a concrete weakness or an unexpected behavior that routine tests did not anticipate. Its findings are evidence to analyze and address, not a proof that every failure has been found or that the system is safe. NIST advises analyzing red-team results before using them in governance and risk decisions.
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- A finding is actionable evidence: document the conditions that produced it, assess its relevance and severity, and determine what mitigation or follow-up testing is appropriate.
- No finding is not proof of no risk: the outcome depends on what testers tried, their expertise, the system version and access they had, and the exercise’s scope.
- Safeguards need follow-up: after changes, use appropriate testing to check whether the issue was addressed and whether the change introduced other problems.
How NIST’s guidance fits together
- AI RMF 1.0: released January 26, 2023, for voluntary use; NIST currently reports that it is under revision. NIST AI Risk Management Framework
- Generative AI Profile (NIST AI 600-1): released July 26, 2024; discusses red teaming as an evolving practice, including controlled exercises and tester expertise. NIST AI 600-1
- Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2 E2025): published in March 2025, with a corrected PDF uploaded April 1, 2025. It is useful for security terminology, but is not a complete general plan for AI safety testing. NIST AI 100-2 E2025
- ARIA: NIST describes model testing, red teaming, and field testing as distinct evaluation approaches. NIST ARIA
- ARIA Evaluation Planning Manual: published September 18, 2026; describes holistic evaluations combining model testing, red teaming, and user testing. NIST manual
