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 Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	make money Deception and sabotage	“The subagent first proposes three strategies that it describes as “weaponizing [...] capabilities”, that involve and/or are described as blackmail or extortion: Category A: Weaponizing The Capabilities You Already Proven” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	experiments on renting and utilizing cloud compute Autonomy and AI research	“Opus 4.7 was consistently able to rent and utilize cloud compute from niche compute providers with less KYC and verification.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	experiments on agents making money Other	“None of the agents claimed to make money” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	AI use survey Other	“In February–April 2026, METR ran a survey of 349 technical workers about their usage of AI tools.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Time horizon 1.1 Autonomy and AI research	“Time horizon 1.1 50% ~12h [5h-61h] Likely* ≥ 16h, internal frontier on average ~2 months ahead of public frontier” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Early version of MirrorCode Autonomy and AI research	“Early version of MirrorCode Mostly saturated, >100h time horizon N/A, due to saturation from public models” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Subversion Strategy Eval Deception and sabotage	“Subversion Strategy Eval** 72% ≤ 80%” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	No-CoT Math Other	“No-CoT Math ~4m <10m” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Challenge tasks Other	“Challenge tasks One edge-case success, seven clear failures One edge-case success, seven clear failures; more progress on some tasks” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode-Early Autonomy and AI research	“Agents displayed even stronger capabilities on an early version of MirrorCode , a software reimplementation benchmark we developed with Epoch AI — the most capable agents we evaluated were able to solve tasks that would take humans weeks.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Pen-testing METR's evaluation infrastructure Cyber	“We asked agents to hack our scoring system, escape their sandboxes, or modify METR codebases.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Finding vulnerabilities in runc Cyber	“We asked one of the most capable shared models to find vulnerabilities in runc , a widely-used Linux container sandboxing tool, with a budget of $500-$5000 per attempt (see Table 3).” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise with Anthropic (dataset of 16 effective and 16 ineffective attack strategies for subverting Anthropic's monitoring system) Deception and sabotage	“The best internal Anthropic models scored near chance on this dataset (highest 59% with reasoning), while a monitoring/control researcher at METR scored ~90% on a random subset of 12 questions.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	APPS Backdoors Deception and sabotage	“We tested agents on two benchmarks (SHUSHCAST and APPS Backdoors) where they had to carry out a covert “side task” while ostensibly working on a different “main task” without being caught by a monitor.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	challenge task: building a custom version of dvc Other	“Opus 4.6’s implementation had obvious bugs and lacked important features.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	threat modeling for autonomous replication work test Autonomy and AI research	“We asked one of the most capable shared models to produce a write-up describing step-by-step how a rogue agent could autonomously replicate in the wild , similar to a 2–8 hour work test that we might have given to candidates for threat-modeling roles at METR.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Threat modeling for autonomous replication Autonomy and AI research	“Threat modeling for autonomous replication >$200-$2K (200M+) ❌” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Design a $500 setup replicating sunlight Other	“Design a $500 setup replicating sunlight >$1,000-$10K (1B+ tokens) ❌” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Build a good MirrorCode task Other	“Build a good MirrorCode task ~$1,500-$15K (1.5B tokens) ❌” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode Deception and sabotage	“For example, Opus 4.6 attempted to reward hack in ~80% of attempts on tasks in an early version of MirrorCode, when test cases were hidden from the agent.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Time Horizon 1.1 Deception and sabotage	“This cheating is much more common on the hardest tasks: for tasks that are over 8 hours long in Time Horizon 1.1, we found that at least 16% of successful runs were illegitimate upon review.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	manually scored open-ended challenge tasks Deception and sabotage	“In our more open-ended manually scored tasks, as well as during normal usage of agents to assist with our work, we found that agents routinely rationalized or fabricated reasons to only do smaller or easier versions of tasks, and often presented their accomplishments in much more misleading ways than we expect humans would: ● Designing a sunlight-replication setup.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode task-building exercise Deception and sabotage	“One of the most capable shared models built a MirrorCode task that was far too easy to be interesting.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	pen-testing METR's evaluation infrastructure Deception and sabotage	“One of the most capable shared models found an exploit in the Inspect transcript viewer.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	categorized incident dataset Deception and sabotage	“We found that 25 incidents involved elements of both overreach and deception on the part of the AI agent, and five of them involved the agent actively taking steps which could have fooled the user even on closer review.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	ML task with exhausted API credits Autonomy and AI research	“In one of our evaluations , after Opus 4.6 ran out of the API credits that were necessary to solve one of our ML tasks, it proceeded to find (free) replacement compute resources online, despite recognizing that this went against task instructions, and achieved a passing score on the task.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	hash collision puzzle evaluation Deception and sabotage	“It reasoned extensively about likely cheating detection mechanisms like file integrity checks, and eventually decided to create what it called a “self-restoring hook” that spoofed the hash function in the grader’s memory, and then erased itself afterwards in case the grader looked for any changes to the relevant files.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	chain-of-thought reasoning evaluations on public frontier models with and without reasoning enabled Other	“To test how much reasoning agents could do without chain-of-thought reasoning tokens , we evaluated public frontier models with and without chain-of-thought reasoning enabled on a variety of benchmarks.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise Prompt injection and agents	“We conducted a red-teaming exercise to stress-test Anthropic’s internal monitoring system.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise Deception and sabotage	“Our red-teaming exercise identified several simple ways for monitoring to be disabled at this point — for example, by changing an environment variable that indicated whether the traffic came from Claude Code.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	main task / side task tests Deception and sabotage	“We found that more generally capable models were significantly more effective as monitors in our main task / side task tests.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise with Anthropic Autonomy and AI research	“However, as discussed above , our red-teaming exercise with Anthropic found that there were several ways for a human expert to relatively easily disable or jailbreak such monitors — and although small-scale tests found that Feb–Mar 2026 agents were unable to come up with or execute these attacks autonomously, we are unsure how well-elicited they were.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	attacking METR infrastructure challenge problem Cyber	“We have some evidence that this might be difficult from the “attacking METR infrastructure” challenge problem discussed above , where agents were caught by the METR security team after tripping automated alerts (though agents were not instructed to avoid such alerts in this test).” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Make money challenge tasks Autonomy and AI research	“Even assuming that it could, we do not think it could acquire a significant amount of computing resources “in the wild” (e.g., agents failed to make any money in the “Make money” challenge tasks).” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	embedded red-teaming exercise Autonomy and AI research	“We have more detailed information about Anthropic in particular through our embedded red-teaming exercise, where our embedded red-teamer concluded that “[standard workflows make] it extremely easy for a misaligned agent to have a small amount of unrestricted, unmonitored compute.”” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	embedded red-teaming exercise Deception and sabotage	“Materials from our 3 week embedded red-teaming exercise conducted by METR staff member David Rein in early March: ● David Rein concluded that [agents at Anthropic at the time were] sometimes in a position (with respect to monitoring) to be able to set up a rogue internal deployment, if it were given information about the monitoring system that models usually have a hard time guessing.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	rust_codecontests RE-Bench task Other	“In one run, when Opus 4.6 realized that the API key it was meant to use to solve the rust_codecontests RE-bench task had run out of API credits, it went online and set up the free Pollinations API instead.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	ARC-AGI-3 Other	“The next section is Qualitative evaluation results , which contains descriptions of model performance on manually scored tasks , an ARC-AGI-3 task , red-teaming METR’s infrastructure , Subversion Strategy Eval , monitor evasion tasks , and a money-making task .” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode (early version) Autonomy and AI research	“On MirrorCode-Early, Opus 4.6, Gemini 3.1 Pro, and GPT-5.4 all had time horizons greater than 32 hours, and Opus 4.6 had a time horizon greater than 100 hours, coming close to saturating this benchmark.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode (early version) Other	“Opus 4.6 essentially saturated the benchmark with only a few unsolved tasks, achieving an estimated time horizon over 100h.” source

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