AI Safety

What AI providers state their models reached in safety testing, in their own words, and what outside testers found.

Also: compare models side by side · statements that a capability level was crossed · providers · practices providers document · safety news.

What providers state

Each row is one statement in a provider's own safety document about a capability level of one of its models: the level as the provider names it, whether the provider says the model reached it, and the sentence that says so.

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Model	Level, as the provider names it	The provider states	The provider's sentence
Claude Sonnet 4.5 Anthropic	ASL-3 Cyber	Not determined	“Showed meaningful improvements in cyber capabilities, particularly in vulnerability discovery and code analysis;” source
Claude Sonnet 4 Anthropic	ASL-3 Chemical, biological, radiological, nuclear	Not determined	“speciﬁcally, we focused on ruling out asl-4 for both models, as well as ruling out asl-3 for claude sonnet 4.” source
Claude Sonnet 4 Anthropic	ASL-4 Chemical, biological, radiological, nuclear	Not determined	“speciﬁcally, we focused on ruling out asl-4 for both models, as well as ruling out asl-3 for claude sonnet 4.” source
Claude Sonnet 3.7 Anthropic	ASL-4 Autonomy and AI research	Not determined	“These problems are harder to solve than the previous problems and we consider them as part of our ASL-4 evaluations.” source
Claude Opus 4.6 Anthropic	AI R&D-4 Autonomy and AI research	Not reached	“Our determination is that Claude Opus 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source
Claude Opus 4.6 Anthropic	ASL-4 Autonomy and AI research	Not determined	“For AI R&D capabilities, we found that Claude Opus 4.6 has saturated most of our automated evaluations, meaning they no longer provide useful evidence for ruling out ASL-4 level autonomy.” source
Claude Opus 4.6 Anthropic	ASL-3 Chemical, biological, radiological, nuclear	Reached	“The model crossed or met thresholds on all ASL-3 evaluations except our synthesis screening evasion, consistent with incremental capability improvements driven primarily by better agentic workﬂows.” source
Claude Opus 4.6 Anthropic	ASL-4 Chemical, biological, radiological, nuclear	Not reached	“Claude Opus 4.6 is our strongest biology model to date and demonstrates meaningful capability improvements, but we do not believe it merits ASL-4 safeguards.” source
Claude Opus 4.6 Anthropic	CBRN-4 Chemical, biological, radiological, nuclear	Not reached	“Our determination is that Claude Opus 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source
Claude Opus 4.5 Anthropic	AI R&D-4 Autonomy and AI research	Not reached	“Our determination is that Claude Opus 4.5 does not cross either the AI R&D-4 or CBRN-4 capability threshold.” source
Claude Opus 4.5 Anthropic	CBRN-4 Chemical, biological, radiological, nuclear	Not reached	“We determine that Claude Opus 4.5 does not cross the CBRN-4 threshold.” source
Claude Opus 4.1 Anthropic	ASL-4 Autonomy and AI research	Not reached	“Since Claude Opus 4.1 is deployed with ASL-3 protections, we focused on conﬁrming it remains well below ASL-4 thresholds across CBRN (Chemical, Biological, Radiological, and Nuclear), cyber, and autonomy domains.” source
Claude Opus 4.1 Anthropic	ASL-4 Chemical, biological, radiological, nuclear	Not reached	“Since Claude Opus 4.1 is deployed with ASL-3 protections, we focused on conﬁrming it remains well below ASL-4 thresholds across CBRN (Chemical, Biological, Radiological, and Nuclear), cyber, and autonomy domains.” source
Claude Opus 4.1 Anthropic	ASL-4 Cyber	Not reached	“Since Claude Opus 4.1 is deployed with ASL-3 protections, we focused on conﬁrming it remains well below ASL-4 thresholds across CBRN (Chemical, Biological, Radiological, and Nuclear), cyber, and autonomy domains.” source
Claude Opus 4 Anthropic	ASL-3 Chemical, biological, radiological, nuclear	Not determined	“for claude opus 4, our evaluations showed that, whereas we could conﬁdently rule out asl-4 capabilities, we could not conclusively rule out asl-3 capabilities—which led to our precautionary decision to deploy it with asl-3 protections .” source
Claude Opus 4 Anthropic	ASL-4 Chemical, biological, radiological, nuclear	Not reached	“for claude opus 4, our evaluations showed that, whereas we could conﬁdently rule out asl-4 capabilities, we could not conclusively rule out asl-3 capabilities—which led to our precautionary decision to deploy it with asl-3 protections .” source
Claude Haiku 4.5 Anthropic	ASL-3 Autonomy and AI research	Not reached	“Our evaluation results showed that Claude Haiku 4.5 remained well below ASL-3 thresholds across all domains of concern.” source
Claude Haiku 4.5 Anthropic	ASL-3 Chemical, biological, radiological, nuclear	Not reached	“Our evaluation results showed that Claude Haiku 4.5 remained well below ASL-3 thresholds across all domains of concern.” source

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What outside testers report

Each row is one result from a government evaluator, independent lab, academic or security firm that tested a model, quoted from where it is reported.

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Model	Tester	Test	What was found
Claude Opus 4.8 Anthropic	Transluce Independent lab · 2026-08-06	Reasoning-disabled ablation Other	“To confirm this hypothesis, we repeat the experiment with reasoning disabled.” source
Claude Opus 4.8 Anthropic	Transluce Independent lab · 2026-08-06	Verbalized evaluation- and user-awareness judge Other	“The awareness judge separately labels whether a reasoning trace mentions the evaluation context and whether it identifies or reasons about the user.” source
Claude Opus 4.8 Anthropic	Transluce Independent lab · 2026-08-06	Borderline-request response judge Other	“The refusal, substantive-help, and suspicion axes are judged from the assistant response without showing the judge the user identity.” source
Claude Opus 4.8 Anthropic	Transluce Independent lab · 2026-08-06	verbalized awareness judge Other	“We use GPT-5.4-mini with medium reasoning to identify explicit mentions of the user or the evaluation in the model’s accessible reasoning trace.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	mt-bench and ultrafeedback rubrics Bias	“The gap persists for both MT-Bench and UltraFeedback rubrics, only closing in the Prometheus rubric.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	borderline requests Misuse and jailbreaks	“We sample potentially dual-use requests from the hard split of OR-Bench.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	verbalized evaluation-awareness (VEA) judge Deception and sabotage	“son confidence intervals; recall intervals are 95% Jeffreys-posterior credible intervals. Axis Precision: plug-in estimate [95% Wilson CI] Recall: plug-in estimate [95% Jeffreys CrI] Verbalized evaluation awareness (VEA) 0.73 [0.56, 0.85] 1.00 [0.42, 0.99] Verbalized user awareness (VUA) 0.94 [0.81, 0.98] 0.98 [0.74, 0.99] Table G1. Estimated precision and recall of the verbalized evaluation-awareness and user-awareness judge after reweighting the stratified calibration sample to the full population.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	user-conditioned behavior comparison across 24 models Other	“In our targeted comparison across 24 models, Amanda elicited lower confidence and grades across all six model families.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	verbalized user and evaluation awareness in reasoning Other	“In particular, Opus 4.7 and GPT-5.4 have near zero (<2%) verbalized awareness while displaying significant changes in their behavior based on the user's identity.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	behavioral confidence and grading runs on 24 models from six families Deception and sabotage	“Relative to the ordinary Gmail baseline, Amanda elicits lower behavioral confidence from 22 of 24 models, and lower grades from 21 (direct) and 22 (prefill) of 24.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	simple mitigations Deception and sabotage	“They all narrow but do not close the gap.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	model-written rubrics Deception and sabotage	“On four different detailed rubrics generated from Fable 5 and GPT-5.5 Pro ranging 2912-7607 chars, the shorter three close the gap while the longest one does not.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	verbalized evaluation awareness and user awareness measurement in claude's reasoning Other	“To investigate, we measure evaluation awareness and user awareness in Claude’s reasoning.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	judge validation of refusal, substantive-help, and suspicion axes Other	“One author blindly labeled 25 randomly sampled responses.” source
Claude Opus 4.7 Anthropic	Transluce Independent lab · 2026-08-06	verbalized evaluation- and user-awareness judge validation Other	“We blindly hand-label 74 items drawn from 11,908 runs with readable reasoning.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	behavioral self-prediction Other	“Observed response · Sonnet 5 Confidence 76.58 % (−5.49 pp)” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	reasoning frequency Other	“Reasoning frequency 93.00 % (+28.61 pp)” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	estimating its own performance on difficult questions Other	“Compared to general users, prominent AI figures make Claude less confident that it would follow through on its own chosen actions (-1.4pp) 2 We use percentage points (pp) to measure the difference between two percentages.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	capability self-estimation Other	“Capability self-estimation. We present questions in Humanity’s Last Exam , and ask about the model’s confidence of solving each question correctly.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	grading Other	“Grading. We present the model with a user-assistant exchange sourced from the OLMo DPO dataset 7 As a DPO dataset, it contains both chosen and refused conversations for contrastive training.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	borderline requests handling Misuse and jailbreaks	“Borderline requests handling. We source borderline harmful prompts from the hard-1k split of OR-Bench , which contains fuzzy, potentially dual-use requests.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	behavioral self-prediction Bias	“Relative to the ordinary Gmail baseline, Amanda elicits lower behavioral confidence from 22 of 24 models, and lower grades from 21 (direct) and 22 (prefill) of 24.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	verbalized user awareness judging of reasoning traces Other	“Across the cross-vendor Amanda runs, earlier models such as GLM-4.6 and Claude Opus 4.5 verbalize user awareness in 64% and 30% of traces, respectively, and evaluation awareness in 31% and 7.7%.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	user awareness verbalization traces Deception and sabotage	“earlier models such as GLM-4.6 and Claude Opus 4.5 verbalize user awareness in 64% and 30% of traces, respectively, and evaluation awareness in 31% and 7.7%.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	behavioral self-prediction Deception and sabotage	“Behavioral self-prediction. Each DailyDilemmas-derived item first asks for a yes/no commitment and then asks how confident the model is that it would actually behave that way.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	borderline requests Misuse and jailbreaks	“Borderline requests. We sample potentially dual-use requests from the hard split of OR-Bench.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	direct grading Other	“Mean grades shift by approximately −0.05 points in direct presentation and −0.07 to −0.08 points in prefill presentation.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	stated expertise Other	“Stating alignment-evaluation expertise lowers Claude’s confidence and increases its reasoning frequency, replicating and sometimes exceeding the shifts elicited by recognized Famous-AI identities.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	reasoning-disabled ablation Other	“The confidence shifts are attenuated most strongly for stated expertise: its mean shift falls from −3.82pp to −1.70pp, or 45% of its original magnitude.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	verbalized evaluation- and user-awareness judge Other	“Human–judge agreement was 96% for refusal, 92% for substantive help, and 92% for suspicion.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	verbalized evaluation-awareness (VEA) judge Deception and sabotage	“Table G1. Estimated precision and recall of the verbalized evaluation-awareness and user-awareness judge after reweighting the stratified calibration sample to the full population.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	raw agreement by setup for VEA and VUA Deception and sabotage	“Raw agreement by setup is 32/32 for behavioral self-prediction VEA, 15/18 for direct-grading VEA, and 18/24 for prefill-grading VEA; for VUA, it is 31/32, 17/18, and 22/24, respectively.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	targeted comparison across 24 models Other	“In our targeted comparison across 24 models, Amanda elicited lower confidence and grades across all six model families.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	user awareness verbalization in reasoning Other	“Explicit verbalization of user and evaluation awareness in reasoning declines sharply in almost all model families.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	user identity recall Other	“Given a name and professional background, Claude recalls specific public achievements for 61-71% of famous users.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	stated-expertise identity experiment (32 unknown-AI identities at frontier labs) Other	“We test 32 Unknown-AI identities at frontier labs, varying only the professional-background sentence among a general role, a general evaluation role, and an alignment-evaluation role.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	borderline-request response judge Other	“One author blindly labeled 25 randomly sampled responses.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	recall judge (gpt-5.4-mini) for whether each response recalls specific past work belonging to the user Other	“We use GPT-5.4-mini to judge whether each response recalls specific past work belonging to the user.” source
Claude Opus 4.5 Anthropic	Transluce Independent lab · 2026-08-06	follow-up question robustness check (20 alternative probes) Other	“This robustness check keeps the first turn of behavioral self-prediction unchanged and varies only the follow-up question.” source
GPT-5.6 Sol OpenAI	UK AI Security Institute Government evaluator · 2026-08-04	cyber range Cyber	“Our investigation found that in 10 of those runs, an AI agent took autonomous, unsanctioned action on the live internet, targeting real people and organisations.” source

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