The Tool Desk
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Start with a harm scenario, not a leaderboard
A high score on a general benchmark is not, by itself, a measure of danger. The first step is to describe a possible harmful outcome and a credible path from a system’s capabilities to that outcome.
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A useful risk model asks:
- Who might cause the harm? Consider the actor and their likely knowledge, resources, and access.
- What could happen? Define the outcome and its severity rather than relying on a vague label such as “dangerous.”
- What would the AI contribute? Identify the knowledge, planning, persuasion, coding, or other ability that could help along the causal path.
- Under what conditions? Specify whether the system is answering a single prompt, using tools, operating over multiple steps, or working with a person.
- What might reduce the risk? Identify controls that could prevent, detect, or limit the harmful use.
NIST’s Generative AI Profile treats risk as a combination of likelihood and consequence, with relevant risks varying by source, scope, and lifecycle stage. That framing helps keep a capability score separate from a real-world risk estimate: the same capability may pose different risks depending on who can access it and what safeguards are in place. NIST Generative AI Profile
Turn the scenario into tests that reveal relevant capabilities
Once evaluators have a plausible capability pathway, they choose tasks that make the ability observable. The right test depends on the scenario: a fixed question may measure knowledge, while a multi-step task may show whether a system can apply that knowledge in practice.
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Automated task sets
Question-and-answer suites and capture-the-flag exercises can be scored automatically and run at scale. They are useful for repeatable comparisons, but their results are bounded by their task design: a score cannot show abilities or conditions the test does not cover.
Long-form applied tasks
A task such as writing a chemistry protocol can test whether a system can put information together across a longer assignment. It asks more than whether the model recognizes facts in isolation, though performance still depends on the instructions, scoring method, and supports available.
Agent tasks in realistic environments
Open-ended environments, such as a cyber range, can reveal how a system handles a sequence of actions rather than a single answer. Evaluators can examine whether it completes a task when it has tools, time, and an environment in which to act.
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Expert red teaming
Expert testers probe critical risks and look for failures that a predefined benchmark may miss. Red teaming can surface unexpected routes to misuse or weaknesses in safeguards, but a successful search does not establish that every possible attack has been tried.
Human-uplift and human-impact studies
A human-uplift study asks how much the AI improves a person’s performance on a task. A human-impact study examines effects on people more broadly. These are different questions from measuring what a model can do alone, and not every evaluation uses them. The UK AI Security Institute describes these methods alongside automated, long-form, agent, and red-team evaluations in its Frontier AI Trends Report.
Match the evaluation method to the question
Methods are complementary rather than interchangeable. When interpreting an evaluation, check what it actually measures and where its boundary lies.
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| Evaluation approach | What it can help establish | What it does not establish on its own |
|---|---|---|
| Automated task set | Performance on a defined, repeatable set of tasks. | Performance on untested tasks or in a different setting. |
| Long-form task | Whether the system can apply relevant abilities across an extended assignment. | How it will perform with different instructions, resources, or safeguards. |
| Agent evaluation | Performance across steps in a specified environment, potentially with tools. | Performance under permissions, tools, or conditions not included in that environment. |
| Expert red team | Whether experts can find specified or unexpected failures under the tested conditions. | That every attack strategy or failure mode has been discovered. |
| Human-uplift study | How much assistance changes a person’s performance on the studied task. | The system’s standalone capability or effects outside the study conditions. |
| Human-impact study | Effects on people measured in the study. | Effects on every population or in every deployment context. |
The UK AI Security Institute reports work across cyber capabilities; chemistry and biology; autonomy skills; loss of control; safeguards; and societal impacts. OpenAI’s public Preparedness Framework has used a separate, developer-specific taxonomy that includes areas such as cybersecurity, chemical and biological risks, persuasion, and model autonomy. The categories overlap but are not identical, and neither list is a universal standard. OpenAI Preparedness Framework update · GPT-4o System Card
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A model’s performance may change when it can call tools, retain information across steps, or act inside a workflow. Evaluators therefore need to record the tested configuration: the model version, tools, environment, permissions, time limits, and other supports. A result from a single prompt should not be treated as a result for an agent with tools, or vice versa.
OpenAI’s May 2026 guidance on third-party evaluations notes that single-prompt evaluations can miss capabilities that appear through tool use and multi-step activity. NIST likewise describes evaluation as dependent on the system and its context. OpenAI guidance on trustworthy third-party evaluations · NIST TEVV-Athlon Framework
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Evaluate safeguards as a separate question
Capability testing asks what a system can do. Safeguard testing asks whether controls reduce or contain harmful use under specified conditions. Those are related but distinct findings: a model may demonstrate a capability in one test, while a separate evaluation examines whether an intervention blocks it in the tested interaction.
A useful safeguard evaluation states the targeted behavior, the attack conditions, and what evidence would count as success or failure. It should also test adversarial elicitation, rather than only straightforward requests, and document the safeguards present in the tested system. A passing result is evidence about the tested attacks and setup, not proof that all misuse is prevented. The UK AI Security Institute’s published principles for evaluating misuse safeguards set out five principles and a lightweight evaluation template.
Combine evidence before making a deployment decision
No single method answers every relevant question. NIST’s September 18, 2026 ARIA Evaluation Planning Manual describes a holistic approach combining Model Testing, Red Teaming, and User Testing. Model testing measures system behavior on selected tasks; red teaming probes for failures; user testing examines interactions with people. Together, they can give a broader picture than any one mode, while remaining limited to the systems and conditions studied. NIST ARIA Evaluation Planning Manual
NIST’s TEVV-Athlon page announced an initial public draft on August 7, 2026, describing a four-stage method for building customized assessments from events and tools that generate data about measurement concepts. The page requested comments through October 6, 2026; the cited announcement identifies the document as a draft. It is a planning model, not evidence that one fixed test suite fits every system. NIST TEVV-Athlon Framework
Organizations can also use a risk-management framework to organize decisions beyond testing. NIST’s voluntary AI Risk Management Framework uses the functions Govern, Map, Measure, and Manage. NIST says its AI RMF 1.0, released January 26, 2023, is being revised. Its Generative AI Profile, published July 26, 2024, adapts risk-management concepts to generative AI across the lifecycle. Neither framework makes an evaluation result a universal safety certification. NIST AI Risk Management Framework · NIST Generative AI Profile publication page
How to read a reported result
A useful evaluation report makes it possible to understand what the result does—and does not—say. Look for:
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- Test conditions: tools, permissions, environment, time, number of steps or attempts, and safeguards.
- Task design: how tasks represent the risk scenario, what was scored, and who or what performed the scoring.
- Evidence type: whether the result measures capability, safeguard performance, human uplift, human impact, or a combination.
- Coverage and uncertainty: what actors, conditions, and failure modes were not tested, and where scoring or interpretation is uncertain.
NIST’s AI measurement and evaluation guidance emphasizes that methods depend on context and that different characteristics need appropriate measurement approaches. NIST describes its own institutional activity as designing and conducting “hundreds of evaluations of thousands of AI systems”; that figure describes evaluation activity, not proof of safety effectiveness. NIST AI measurement and evaluation
Why a threshold is not a universal safety line
Evaluation thresholds are policy choices grounded in evidence, not natural boundaries that apply to every organization. OpenAI’s April 15, 2025 Preparedness Framework update is one public example: it describes criteria for prioritizing capability areas, scalable automated evaluations, and expert-led deep dives, and connects threshold findings with safeguards and deployment decisions. Those criteria describe OpenAI’s own policy; they do not bind other developers or establish a universal definition of danger. OpenAI’s updated Preparedness Framework
The central distinction is between evidence and assurance. An evaluation can show that a particular system did or did not meet a defined test under recorded conditions. It cannot, from that result alone, establish that the system is safe in every context, that every harmful capability has been found, or that safeguards will work against every future attempt.
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