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AI safety evaluations identify dangerous capabilities by translating a plausible harm scenario into tasks a model or AI system can attempt, then testing how it performs under specified conditions. They assess misuse risk by combining that capability evidence with access, tools, safeguards, and the likely consequences of failure. A test result applies to the system and conditions actually evaluated; it cannot prove that every risk has been found or that a system is universally safe.
What an AI safety evaluation is trying to find out
An evaluation can measure a capability, test a safeguard, or study how people interact with an AI system. Those are related questions, but they are not interchangeable. A model’s ability to complete a task may matter to a risk; it does not, by itself, show that someone can or will use the model to cause harm.
Evaluators therefore start with a risk model rather than a leaderboard score. They describe a possible harmful outcome, who might cause it, what the AI could contribute, what access or resources would be needed, and which controls might prevent or limit the harm. NIST’s Generative AI Profile treats risk as involving both likelihood and consequence, and notes that risk varies with its source and the system’s lifecycle context. NIST Generative AI Profile
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- Capability evidence: Can the model perform tasks related to a hypothesized pathway to harm?
- Misuse-safeguard evidence: Do controls resist attempts to elicit harmful behavior under the tested conditions?
- Risk judgment: Given capability, access, likely users, safeguards, and possible consequences, what decisions are warranted?
That final judgment is contextual. A high score on a task can be a reason to investigate further, not a direct estimate of real-world harm.
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How evaluators turn a risk into a test
1. Define the scenario and capability pathway
First, specify a plausible severe outcome and the chain of events that could connect an AI system to it. The scenario should identify the actor, the resources and access they might have, the system’s potential contribution, and the severity of the outcome. It should also identify mitigations that could block, detect, or contain the activity. This keeps a test focused on a risk-relevant question instead of treating general intelligence or a benchmark score as a proxy for danger.
2. Choose observable tasks
Next, identify tasks that would provide evidence about the relevant capability. A test might ask whether a model can answer domain questions, complete a technical challenge, write a protocol, or carry out a multi-step task in an environment. The UK AI Security Institute (AISI) describes using auto-graded task sets, long-form assignments, and agent tasks in realistic settings, alongside expert red teaming and studies of human performance or impact. No single method is appropriate for every risk area. UK AISI Frontier AI Trends Report
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3. Test the configuration that matters
Record whether the system can use tools, how much time it has, what permissions it receives, and whether it can retain information or act across multiple steps. A single prompt may not reveal what a system can accomplish in a supported workflow. OpenAI’s May 2026 guidance on third-party evaluations calls attention to tool use and multi-step activity; NIST’s TEVV-Athlon draft likewise presents assessment as customized to the system and context. OpenAI third-party evaluation guidance · NIST TEVV-Athlon Framework
4. Assess safeguards against relevant attacks
Capability tests ask what a system can do; safeguard tests ask whether a control limits harmful use when challenged. Evaluators define the behavior being targeted, the attack conditions, and what evidence would count as success or failure. The UK AISI has published principles and a lightweight template for evaluating misuse safeguards. Passing a test is evidence about the attacks and conditions included, not proof that every bypass or deployment context has been covered. UK AISI principles for evaluating misuse safeguards
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5. Combine evidence for a decision
Results from task sets, applied tasks, red teams, and user studies answer different questions. Evaluators interpret them together with the threat model and safeguard evidence before recommending monitoring, mitigation, further testing, or a deployment decision. NIST’s AI Risk Management Framework organizes risk work around Govern, Map, Measure, and Manage; it is a voluntary framework, not a universal test suite. NIST says its 1.0 framework, released in January 2023, is being revised. NIST AI Risk Management Framework
What different evaluation methods can reveal
| Method | What it can help reveal | What it does not establish alone |
|---|---|---|
| Automated task set | Performance across many defined, scorable tasks, such as question-answer items or capture-the-flag challenges. | Whether the tasks fully represent real-world conditions or capture every relevant capability. |
| Long-form applied task | Whether the system can apply knowledge across a sustained assignment, such as producing a technical protocol. | Whether it can reliably carry out the work outside the tested assignment and supports. |
| Agent task in an environment | How the system performs in an open-ended setting with tools, actions, or multiple steps. | How it would perform with different tools, permissions, time limits, or monitoring. |
| Expert red team | Failure modes or safeguard weaknesses that evaluators did not anticipate in a fixed test set. | That all attack strategies or adversarial users have been considered. |
| Human-uplift study | How much AI assistance changes a person’s performance on a defined task. | That the same uplift will occur for other users, tasks, or settings. |
| User or human-impact study | How people interact with a system and what effects arise in the studied context. | That the results generalize to every population or deployment. |
NIST’s September 2026 ARIA Evaluation Planning Manual describes a holistic approach combining Model Testing, Red Teaming, and User Testing. The methods are complementary: benchmark tasks scale, while applied and human-centered approaches can expose conditions a benchmark misses. NIST ARIA Evaluation Planning Manual
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Which capability and misuse risks may be evaluated
Risk categories depend on the organization’s threat model. The UK AISI reports work across areas including cyber capabilities, chemistry and biology, autonomy skills, loss of control, safeguards, and societal impacts. OpenAI’s public Preparedness Framework is a separate, developer-specific example that has included cybersecurity, chemical, biological, radiological and nuclear risks, persuasion, and model autonomy. These taxonomies overlap, but neither is a universal classification for all evaluators. OpenAI Preparedness Framework update, April 15, 2025 · GPT-4o System Card
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- Capability or safeguard: Is the test measuring what the system can do, or whether a control limits it?
- Isolated task or workflow: Is it a fixed prompt, a sustained assignment, or an agent operating with tools?
- Automated or expert-led: Can the test scale, and has expert review checked whether it captures the intended risk?
- Model or complete system: Are tools, permissions, monitoring, and deployment context included?
- Knowledge, uplift, or impact: Does the study measure what the model knows, the help it gives a person, or effects on users?
How to read an evaluation result responsibly
A useful report makes the boundary of its evidence visible. Readers should be able to tell which model version was tested, how it was accessed, what tools and permissions were available, which tasks and scoring rules were used, which safeguards were active, and what uncertainty remains. Without that context, results from different evaluations may not be comparable.
Thresholds are also policy choices grounded in evidence, not universal laws. OpenAI’s April 2025 Preparedness Framework is one organization’s example: it describes criteria for prioritizing capability areas and connects findings to safeguards and deployment decisions. Its criteria do not bind other developers or establish a general threshold for safety. OpenAI Preparedness Framework update
NIST’s AI measurement guidance emphasizes that methods depend on context and that different system characteristics require different measurement approaches. A benchmark pass therefore supports only a bounded claim about performance in that evaluation; it does not establish universal safety. NIST AI measurement and evaluation
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Current public framework status
- NIST’s Generative AI Profile, published July 26, 2024, adapts AI Risk Management Framework concepts to generative AI and suggests actions across the lifecycle. NIST Generative AI Profile publication page
- NIST published its ARIA Evaluation Planning Manual on September 18, 2026. It describes Model Testing, Red Teaming, and User Testing as elements of evaluation planning. ARIA manual
- NIST announced an initial public draft of the TEVV-Athlon Framework on August 7, 2026, with a comment deadline of October 6, 2026. The cited page describes a draft; its status should not be presented as finalized based on that information. TEVV-Athlon Framework page
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