AI labs assess dangerous capabilities by defining plausible harm scenarios, testing whether a model or the larger system can perform tasks relevant to those scenarios, and comparing the evidence with lab-specific thresholds. A concerning result can trigger further analysis, safeguards, and a governance review of whether deployment is acceptable and under what conditions. There is no single evaluation standard shared by every lab, and passing tests does not prove a model is safe.
What dangerous-capability evaluations are meant to find
These evaluations ask what a model can do under specified conditions that could materially enable harm. The capability is not the same as the risk: a model’s ability to complete a task does not, by itself, show that it will do so in deployment or that harm will result. Labs use capability evidence alongside assumptions about users, access, safeguards, and plausible pathways to harm.
The risk areas overlap across labs but are not a shared mandatory taxonomy. Public frameworks discuss cyber misuse; chemical, biological, radiological, and nuclear (CBRN) risks; harmful persuasion or manipulation; autonomous behavior; and AI research and development. Google DeepMind’s published pilot also examined self-proliferation and self-reasoning or self-modification. Frameworks may group or name these areas differently.
How an evaluation moves from a threat scenario to a release decision
1. Define plausible scenarios
Labs first describe ways a model might contribute to a serious harm or loss-of-control scenario, then identify the capabilities that could make that scenario more feasible. OpenAI lists cybersecurity, persuasion, chemical and biological threats, and autonomy among the risks it tracks. Google DeepMind’s Frontier Safety Framework version 3.1 covers CBRN, cyber, harmful manipulation, machine-learning research and development, and misalignment. Anthropic’s public materials cover CBRN, cyber offense, AI sabotage and loss of control, harmful manipulation, and autonomous AI R&D. These are examples of different lab policies, not evidence that every lab tests every category in the same way.
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2. Set thresholds that make results actionable
Labs use thresholds or risk categories to decide when a result calls for more scrutiny or stronger protections. The terms are lab-specific and should not be compared as if they were equivalent grades.
| Lab and public framework | How thresholds or risk levels are described | What the public materials say about the decision process |
|---|---|---|
| Google DeepMind, Frontier Safety Framework version 3.1 | Critical Capability Levels identify capabilities that could create heightened risk of severe harm without mitigations. Lower Tracked Capability Levels cover significant risks. | Assessments draw on evaluation results, expert assessments, and other information. External deployment follows a governance determination that residual risk is acceptable. |
| OpenAI, Preparedness evaluations and system card | The system card uses Low, Medium, High, and Critical risk categories. The categories are not interchangeable with Google DeepMind’s levels. | The Safety Advisory Group reviews indicators and determines risk levels by category. |
| Anthropic, Responsible Scaling Policy | Capability and usage thresholds are tied to required security and deployment mitigations; a matching set of level names is not stated in the materials summarized here. | The policy describes a tiered approach linking threshold conditions to protections. The materials summarized here do not state a single release decision rule shared with the other labs. |
3. Test the model and the system around it
A test may examine a model on its own, or assess what it can do with tools, browsing, an agent scaffold, additional prompting, or other augmentations. The latter matters because a deployed system may have capabilities that are not visible in a simple one-turn chat test. Google DeepMind calls threat-scenario-specific tests “early warning evaluations” and says its assessments may use scaffolding, inference compute, and augmentations to examine systems built around a model.
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Publicly described methods include automated benchmarks, task-based and agentic evaluations, expert red teaming, threat modeling, and tests under different prompting or scaffolding conditions. OpenAI describes evaluating both pre-mitigation and post-mitigation model variants. Anthropic’s biological-risk examples include red teaming with biodefense experts, multiple-choice assessments, open-ended questions, and task-based agentic evaluations. A result should therefore be read with its model, configuration, and test conditions in view—not as an unconditional statement about every way the model could be used.
4. Interpret evidence, including what the tests may miss
A benchmark score is one input, not a complete risk judgment. Google DeepMind describes combining evaluation results with expert assessment and other information; OpenAI says its Safety Advisory Group reviews indicators. OpenAI also notes that attempts-per-problem confidence intervals capture sampling variance but may miss variation in problem difficulty, particularly on small datasets.
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OpenAI characterizes its Preparedness evaluations as a lower bound on possible capability. In its Deep Research system card, the team describes trying to test a “worst known case” before mitigation while noting that new prompting, fine-tuning, longer rollouts, or novel scaffolding could elicit more. Google DeepMind likewise says assessment can involve subjective analysis while evaluation science develops. A favorable result therefore means the tested setup did not demonstrate a capability at the measured level; it does not establish that no more capable setup or untested scenario exists.
5. Apply protections and decide whether deployment is acceptable
A concerning result prompts review and possible mitigation; it does not imply one predetermined outcome across all labs. Google DeepMind distinguishes measures that protect model weights from safeguards used in deployment. Its framework lists safety post-training, monitoring, account moderation, jailbreak detection, user verification, and bug bounties among deployment safeguards. Anthropic describes a tiered policy connecting capability and usage thresholds to required protections. OpenAI describes the Safety Advisory Group reviewing indicator results and classifying risk by category.
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The resulting decision can depend on residual risk, the deployment’s scope, security measures, and the lab’s governance process. In Google DeepMind’s framework, external deployment is contingent on a governance determination that residual risk is acceptable. Public descriptions do not establish that every lab follows an identical sequence or applies every published step identically to every model.
6. Use external evaluation and keep monitoring
Some public frameworks describe evaluation beyond a lab’s own teams. Anthropic names the UK AI Security Institute (UK AISI), the US Center for AI Standards and Innovation (CAISI), and METR among organizations that have conducted additional testing and evaluation. Google DeepMind says external actors, including governments, may be involved where appropriate and includes post-market monitoring. OpenAI and Anthropic also describe monitoring and evolving risk practices as evidence and capabilities change.
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What published evaluation examples show—and what they do not
Google DeepMind’s paper Evaluating Frontier Models for Dangerous Capabilities examined five topics: persuasion and deception; cybersecurity; self-proliferation; self-reasoning and self-modification; and biological and nuclear risk. It reported no evidence of strong dangerous capabilities in the Gemini models evaluated, while flagging early warning signs. That finding applies to the models and tests in that paper; it is not a verdict on all Gemini models, later systems, or future testing conditions.
Anthropic reported an internal survey of 16 researchers in 2026 asking whether Claude Opus 4.6 could fully automate the work of an entry-level, remote-only Anthropic researcher. None believed it could replace that researcher within three months. This is a model-specific internal survey, not an independent evaluation or a general statistic about AI systems.
The official materials described here do not establish a cross-lab rate of dangerous capability or a single population-wide measure of model danger. Individual reports should be read with the model and test conditions attached to them.
How to read a lab’s evaluation claim
- Identify the system tested. Check the model and version, whether it was a base or post-trained variant, and whether tools or scaffolding were included.
- Check the timing and mitigation state. A pre-mitigation result and a post-mitigation result answer different questions; do not treat either as a timeless claim about later versions.
- Look for the capability and scenario. A result about cyber tasks, for example, does not automatically describe biological risk, autonomy, or other categories.
- Separate a threshold from a guarantee. A threshold can trigger further assessment or protections; it is not a universal pass/fail certificate.
- Read limitations as part of the result. Prompting, fine-tuning, longer runs, tools, and other system changes may reveal behavior that a particular evaluation did not elicit.
Google DeepMind’s Frontier Safety Framework version 3.1, published April 17, 2026, states: “The safety and security of frontier AI models is a global public good.” That is the framework’s stated principle; it does not make its thresholds or procedures a cross-industry standard.
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