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What an AI red-team assessment checks
The exact target matters. An assessment might examine a model by itself, an application built around it, a deployed system, or several of those together. The report should say which one was tested.
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Model behavior and safeguards
Testers try adversarial prompts, jailbreaks, and other inputs to see whether a model gives responses it should refuse or otherwise behaves unsafely. They also examine whether the particular safeguards in scope withstand those inputs. In a joint U.S. and U.K. AI Safety Institute evaluation of an upgraded Claude 3.5 Sonnet, machine-learning experts attempted to develop jailbreaks and other adversarial inputs that would elicit answers to malicious requests. NIST reported that most publicly available jailbreaks tested by the U.S. institute circumvented the built-in safeguards examined in that exercise. That result applies to the tested model version, jailbreak set, and safeguards—not to every AI system or later version. Read NIST’s account of the evaluation.
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With suitable access and scope, a red team can probe more than model responses: it may test interfaces, connected tools, infrastructure, and interactions among system components. Microsoft’s response to NIST describes red teaming as probing for harmful capabilities and outputs as well as infrastructure threats, with examples across responsible-AI and cybersecurity concerns. This is Microsoft’s practitioner perspective, not a binding NIST standard. Read Microsoft’s submission to NIST.
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What a successful finding—and a clean test—mean
A validated finding is evidence that the tested target failed in a particular way under the test’s conditions. It can identify a weakness worth fixing, but it does not by itself show how often the weakness will occur in ordinary use or how much harm it will cause.
A test that finds no issue has a narrower meaning: the team did not surface one with its selected prompts, scenarios, access, tools, time, and expertise. Red teaming is meant to discover weaknesses, including novel ones; it is not a complete census of capabilities or risks. Tests are scoped and conducted at a point in time, while systems, safeguards, and attack methods can change.
NIST’s account of the joint U.S. and U.K. evaluation says that “the results of this evaluation cannot on their own determine the model’s risks.” The report describes that evaluation’s safeguard results and cautions that the work took place over a limited period with finite resources. It also notes that judgments about harmfulness can be subjective and jurisdiction-dependent. NIST’s report sets out the qualifications.
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A red-team exercise is not a substitute for every other kind of evaluation. On its own, it does not establish the prevalence of a known risk, continuously measure behavior in production, detect or prevent malicious activity after launch, or supply every sector-specific impact assessment. Those questions need complementary evidence:
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- Prevalence: use systematic measurement designed to estimate how often a defined behavior occurs.
- Production behavior: use monitoring and auditing to observe the deployed system over time.
- Domain-specific effects: involve relevant impact assessment and subject-matter review.
- Unanticipated weaknesses: use red teaming to actively search for failures and attack paths that may not be captured by a fixed test suite.
How to compare red-team reports
Two reports are not directly comparable just because they both use the term “red teaming.” Before interpreting their results side by side, check:
- Target and version: Was the subject a model, an application, or a deployed system? Which version and configuration were tested?
- Scope and access: Which interfaces, tools, permissions, and system components could testers reach? Were access limits or rate limits in place, or did the team have special access?
- Threats and harms: Which adversaries and attack goals were considered, and how did the evaluation define harmful output?
- Test design: Did the team use public or private cases, manual exploration or a repeatable suite? Which domains were covered or excluded?
- Evidence and outcomes: What counted as a successful exploit? How were findings validated, severity assessed, and mitigations tested?
- Timing and uncertainty: When did testing occur, what resources were available, and what uncertainty or margins of error did the evaluators report? The U.S. and U.K. institutes cautioned that smaller performance differences in their evaluation might fall within test margins of error.
How published evaluations illustrate the limits
Specific figures help describe what an evaluation tested, but they are not universal measures of system security or red-team effectiveness. NIST’s reports illustrate why the target, challenge set, and task level must travel with each number.
| Evaluation | Reported result | What the figure describes |
|---|---|---|
| NIST ARIA 0.1 pilot, reported November 13, 2025 | Five organizations submitted seven AI applications | The pilot used model testing, red teaming, and field testing as three evaluation levels. NIST ARIA 0.1 pilot report. |
| U.S. AI Safety Institute, as reported by NIST in 2024 | 32.5% task success | Upgraded Claude 3.5 Sonnet on the institute’s suite of 40 public cybersecurity challenges—not a general measure of red-team performance or system security. NIST’s evaluation account. |
| U.K. AI Safety Institute, as reported by NIST in 2024 | 36% success on apprentice-level tasks | The institute’s suite of 47 cybersecurity challenges: 15 public and 32 privately developed. This result is specific to that evaluation and task level. NIST’s evaluation account. |
How red teaming fits into a broader evaluation
NIST’s ARIA materials distinguish model testing, red teaming, and field or user testing. They are complementary evidence streams, not interchangeable names for the same exercise. The ARIA pilot report describes its three levels as model testing, red teaming, and field testing; NIST’s September 18, 2026 planning manual describes a holistic approach combining Model Testing, Red Teaming, and User Testing. See NIST’s ARIA program overview and the evaluation planning manual.
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For an organization deciding whether an AI system is ready for use, red-team findings should therefore sit alongside systematic measurement, impact assessment, threat modeling, user or field testing, and monitoring after deployment. Each answers a different question: what can be made to fail, how reliably a defined behavior occurs, who may be affected, and what happens once the system meets real users and conditions.
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