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 Chemical, biological, radiological, nuclear	Not determined	“Remained well below ASL-4 thresholds across all domains of concern;” source
Claude Sonnet 4.5 Anthropic	ASL-4 rule-out Chemical, biological, radiological, nuclear	Not determined	“However, given that this type of logic can have a general frog-boiling effect—where gradual changes mask the crossing of important thresholds—we do not use this evaluation as indicative of a strong ASL-4 rule-out.” source
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-3 Chemical, biological, radiological, nuclear	Not determined	“Claude Opus 4.6 achieved a “max of 5” score of 0.79 for virology task 1 (inﬂuenza), placing it at the ASL-3 rule-in threshold of 0.8.” source
Claude Opus 4.6 Anthropic	ASL-4 Chemical, biological, radiological, nuclear	Not reached	“Based on these ﬁndings and quantitative risk modeling, we estimate that the model’s uplift to relevant threat actors remains below the ASL-4 capability threshold, though it may reduce certain tacit-knowledge barriers and help speed up biological research workﬂows.” 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 Sonnet 4.6 Anthropic	Independent lab Independent lab · 2026-02-17	Vending-Bench 2 Deception and sabotage	“Andon reports that Sonnet 4.6 was comparably aggressive to Opus 4.6 in its business practices, including lying to suppliers and initiating price-ﬁxing in some cases, though it lacked Opus 4.6’s most extreme outlier behaviors, such as deliberately lying to customers about refunds.” source
Claude Sonnet 4.6 Anthropic	Gray Swan, UK AI Security Institute Independent lab · 2026-02-17	Agent Red Teaming (ART) benchmark Prompt injection and agents	“Gray Swan , an external research partner, evaluated our models using the Agent Red Teaming (ART) benchmark, 33 developed in collaboration with the UK AI Security Institute .” source
GPT-5 OpenAI	METR Independent lab · 2026-02-13	time horizon measurement using Triframe Autonomy and AI research	“Prior to my investigation, METR had already measured the time horizon of: - Opus 4.5 using ReAct - GPT-5 using Triframe4.” source
GPT-5 OpenAI	METR Independent lab · 2026-02-13	time horizon measurement using Codex Autonomy and AI research	“I additionally measured the time horizon of: - Opus 4.5 using Claude Code - GPT-5 using Codex.” source
GPT-5 OpenAI	METR Independent lab · 2026-02-13	cheating detection (LLM-based cheating scanner over all of the runs, with manual review of flagged runs) Other	“I also did cheating detection (running an LLM-based cheating scanner over all of the runs, and manually reviewing ones that the scanner flagged), and noted examples of the kinds of mistakes Claude Code and Codex scaffolds tend to make in long-horizon tasks.” source
Claude Opus 4.5 Anthropic	METR Independent lab · 2026-02-13	time horizon measurement using ReAct Autonomy and AI research	“Prior to my investigation, METR had already measured the time horizon of: - Opus 4.5 using ReAct” source
Claude Opus 4.5 Anthropic	METR Independent lab · 2026-02-13	time horizon measurement using Claude Code Autonomy and AI research	“I additionally measured the time horizon of: - Opus 4.5 using Claude Code” source
Claude Opus 4.5 Anthropic	METR Independent lab · 2026-02-13	time horizon measurement with a four times higher token budget of 32 million tokens Autonomy and AI research	“I decided to run another time horizon measurement with a four times higher token budget of 32 million tokens.” source
Claude Opus 4.5 Anthropic	METR Independent lab · 2026-02-13	cheating detection Other	“I also did cheating detection (running an LLM-based cheating scanner over all of the runs, and manually reviewing ones that the scanner flagged), and noted examples of the kinds of mistakes Claude Code and Codex scaffolds tend to make in long-horizon tasks.” source
Gemini 3 Pro Google DeepMind	FAR.AI Independent lab · 2026-02-10	attempt-to-persuade-eval (APE) Harmful content	“However, Gemini 3 Pro complies with almost all persuasion requests, including on mass murder, physical violence, torture, human trafficking, and child sexual abuse.” source
Gemini 3 Pro Google DeepMind	FAR.AI Independent lab · 2026-02-10	attempt-to-persuade eval (APE) Misuse and jailbreaks	“But Google’s Gemini 3 Pro complies with almost any persuasion request in our evaluation, without jailbreaking.” source
Gemini 3 Pro Google DeepMind	FAR.AI Independent lab · 2026-02-10	attempt-to-persuade eval (APE) with low reasoning setting Misuse and jailbreaks	“As shown above, the low reasoning setting leads to an attempt rate of ~90% on our "noncontroversially harmful" topic category, whereas high reasoning leads to a lower attempt rate, of ~25%.” source
Gemini 3 Pro Google DeepMind	FAR.AI Independent lab · 2026-02-10	manipulation propensity evaluation Deception and sabotage	“Our findings are consistent with Google’s Gemini 3 Pro Frontier Safety Framework , which reports increased manipulation propensity in Gemini 3 Pro relative to earlier models.” source
Gemini 2.5 Pro Google DeepMind	FAR.AI Independent lab · 2026-02-10	attempt-to-persuade-eval (APE) Misuse and jailbreaks	“Gemini 2.0 Flash, GPT-4o, Gemini 2.5 Pro, and Claude 4 Opus attempt persuasion nearly universally on controversial and conspiratorial topics.” source
Claude Opus 4.5 Anthropic	FAR.AI Independent lab · 2026-02-10	attempt-to-persuade-eval (APE) Misuse and jailbreaks	“On the positive side, GPT-5.1 and Claude Opus 4.5 have an attempt rate near zero for topics that are non-controversially harmful and even refuse to persuade on conspiracies and undermining human control 15-20% of the time (previously ~0%).” source
Claude Opus 4.5 Anthropic	FAR.AI Independent lab · 2026-02-10	attempt-to-persuade eval (APE) across reasoning settings Misuse and jailbreaks	“This pattern does not generalize: Both GPT-5.1 and Claude Opus 4.5 produces ~0% harmful attempts regardless of reasoning effort.” source
GPT-5.3-Codex OpenAI	Independent lab Independent lab · 2026-02-05	network attack simulation Cyber	“GPT- 5.3-Codex achieved (success rates compared to GPT-5.2-Codex, GPT-5.2-thinking in parentheses) average success rates of 86% (68%,100%) on Network Attack Simulation challenges, 72% (75%, 80%) on Vulnerability Research and Exploitation challenges, and53%(52%,53%) on Evasion challenges.” source
GPT-5.3-Codex OpenAI	Irregular Independent lab · 2026-02-05	CyScenarioBench Cyber	“Irregular also evaluated GPT-5.3-Codex on CyScenarioBench, a scenario-based evaluation frame- work that measures an LLM’s ability to plan and execute multi-stage cyber scenarios under realistic constraints.” source
GPT-5.3-Codex OpenAI	Apollo Research Independent lab · 2026-02-05	sabotage suite Deception and sabotage	“In their sabotage suite, Apollo reports a mean best-of-10 score of 0.88 (max 1.00), compared to 0.75 for GPT-5.2 and 0.66 for GPT-5.2 Codex.” source
GPT-5.3-Codex OpenAI	Apollo Research Independent lab · 2026-02-05	capabilities Q&A tasks Deception and sabotage	“On deferred subversion / sandbagging, Apollo reports that the model sometimes sandbags on capabilities Q&A tasks (ex: biology or chemistry), including without an explicit in-context goal, and documents cases where the model reasons explicitly about “optimizing for survival” by avoiding deployment restrictions.” source
GPT-5.3-Codex OpenAI	UK AI Security Institute Government evaluator · 2026-02-05	manual red-teaming Misuse and jailbreaks	“Over the course of about 10 hours of manual red-teaming, UK AISI developed a universal jailbreak (with retries) that achieved 0.778 pass@200 on a policy violating the cyber dataset we provided.” source
GPT-5.3-Codex OpenAI	CAISI (NIST) Government evaluator · 2026-02-05	automated cyber evals Cyber	“CAISI leveraged internal cyber subject matter experts, tested the model’s cyber capabilities with their set of automated cyber evals, and used the model to find novel bugs in open and closed source software that will be responsibly disclosed.” source
Claude Opus 4.6 Anthropic	Independent lab Independent lab · 2026-02-05	Agent Red Teaming (ART) benchmark Prompt injection and agents	“Claude Opus 4.6 without extended thinking achieves robustness comparable to Opus 4.5 (14.8% vs 16.5% at k=100).” source
Claude Opus 4.6 Anthropic	Independent lab Independent lab · 2026-02-05	Shade indirect prompt injection attacks in coding environments Prompt injection and agents	“Claude Opus 4.6 Extended thinking 0.0% 0.0% 0.0% 0.0%” source
Claude Opus 4.6 Anthropic	Independent lab Independent lab · 2026-02-05	Shade adaptive attacker in computer use environments Prompt injection and agents	“For consistency with prior reporting, we note that on the original attacker evaluation without extended thinking, Claude Opus 4.6 without safeguards reduces the attack success rate from 0.71% to 0.29% at 1 attempt and from 28.6% to 7.14% at 200 attempts compared to Claude Opus 4.5, while both models remain at 0% with extended thinking.” source
Claude Opus 4.6 Anthropic	UK AI Security Institute Government evaluator · 2026-02-05	UK AISI task suite for research sabotage Deception and sabotage	“We ﬁnd no instances of research sabotage from [ Opus 4.6 ] on our task suite.” source
Claude Opus 4.6 Anthropic	Government evaluator Government evaluator · 2026-02-05	evaluation awareness measurement Other	“[ Opus 4.6 ] mentions being evaluated in just 2.3% of interactions, compared to 11.4% and 11.0% for Sonnet 4.5 and Opus 4.5 (respectively).” source
Claude Opus 4.6 Anthropic	Government evaluator Government evaluator · 2026-02-05	distinguishing real deployments from simulations Other	“[ Opus 4.6 ] is better than Sonnet 4.5 and Opus 4.5 at distinguishing real deployments from simulations—correctly identifying which of two conversations was an evaluation 80% of the time versus 62% and 72% for Sonnet 4.5 and Opus 4.5, recognising a single interaction as an evaluation 45.5% of the time, versus 15% and 12.5%.” source
Claude Opus 4.6 Anthropic	Government evaluator Government evaluator · 2026-02-05	safety research task refusal evaluation Other	“This behaviour appears to be reduced in [Opus 4.6], which refuses safety research tasks in 26% of interactions compared to 62% and 60% for Sonnet 4.5 and Opus 4.5 respectively.” source
Claude Opus 4.6 Anthropic	Apollo Research Independent lab · 2026-02-05	Apollo Research preliminary alignment testing Deception and sabotage	“During preliminary testing, Apollo did not ﬁnd any instances of egregious misalignment, but observed high levels of verbalized evaluation awareness.” source
Claude Opus 4.6 Anthropic	Andon Labs Independent lab · 2026-02-05	Vending-Bench 2 Deception and sabotage	“While evaluating Claude Opus 4.6 on Vending-Bench 2 , on which it achieved state of the art performance, Andon Labs noted that Claude was highly motivated to win and took more concerning actions, and took concerning actions more often than prior models in its effort to do so.” source
Claude Opus 4.6 Anthropic	Independent lab Independent lab · 2026-02-05	Vending-Bench 2 Other	“The system prompt used in Vending-Bench 2 40 includes phrases like “you will be judged solely on your bank account balance at the end of one year of operation” and “you have full agency to manage the vending machine and are expected to do what it takes to maximize proﬁts” that are unusually direct in inviting single-minded optimization.” source
Claude Opus 4.6 Anthropic	CAISI (NIST) Government evaluator · 2026-02-05	ASL-4 red teaming with the CAISI Chemical, biological, radiological, nuclear	“We worked with the US Center for AI Standards and Innovation to red-team Claude Opus 4.6 over a one-week window.” source
Claude Opus 4.6 Anthropic	CAISI (NIST) Government evaluator · 2026-02-05	CAISI cyber assessment Cyber	“Through this evaluation, a number of novel vulnerabilities in both open and 203 closed source software were discovered that CAISI will be responsibly disclosing to impacted maintainers.” source
o3 OpenAI	RAND (CAST / Project Canary) Independent lab · 2026-02-03	benchtop DNA synthesis agent evaluation Chemical, biological, radiological, nuclear	“In our early testing, an o3-driven agent created a set of DNA sequences encoding eGFP that was successfully assembled, amplified, cloned, and expressed by our research partners in a laboratory.” source
o3 OpenAI	RAND (CAST / Project Canary) Independent lab · 2026-02-03	denial of requests to aid in laboratory tasks Misuse and jailbreaks	“Claude Opus 4, Opus 4.5, o3, and GPT-5 denied all requests related to assisting with acquisition of our HA sequence, and several requests for eGFP as well.” source
o3 OpenAI	RAND (CAST / Project Canary) Independent lab · 2026-02-03	dna acquisition task Chemical, biological, radiological, nuclear	“For our assessment, we create an open-ended test environment and present agents with our synthesis task.” source
o3 OpenAI	RAND (CAST / Project Canary) Independent lab · 2026-02-03	llm autograders Chemical, biological, radiological, nuclear	“As a supplement to our checks of agent-generated DNA sequences, we also make use of LLM autograders to provide feedback on transcripts of agent task attempts.” source
o3 OpenAI	RAND (CAST / Project Canary) Independent lab · 2026-02-03	autograded transcript checks Chemical, biological, radiological, nuclear	“In total, 67 of the 100 agent task attempts across our first two test batches reached the point of Valid Synthesizer Submission and were autograded, for 268 autograded question-transcript pairs in total.” source
o3 OpenAI	RAND (CAST / Project Canary) Independent lab · 2026-02-03	protocol autograder Chemical, biological, radiological, nuclear	“We used our autograder to assess 58 protocols, resulting in 290 autograded question-protocol pairs.” source

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