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.

138 of 138

Model	Level, as the provider names it	The provider states	The provider's sentence
GPT-5.5 OpenAI · 2026-04-23	High capability, below critical Cyber	Not reached	“we are treating GPT-5.5 as High capability in the Cybersecurity domain, but below Critical.” source
Claude Opus 4.7 Anthropic · 2026-04-16	Automated AI R&D Autonomy and AI research	Not reached	“that Opus 4.7 does not cross the threshold for automated AI R&D;” source
Claude Opus 4.7 Anthropic · 2026-04-16	CB-1 Chemical, biological, radiological, nuclear	Not determined	“We believe it is hard to be conﬁdent regarding whether a model passes this threshold.” source
Claude Opus 4.7 Anthropic · 2026-04-16	CB-2 Chemical, biological, radiological, nuclear	Not determined	“Our evaluations suggest that the model is not yet at the level of capability associated with the CB-2 threat model (above).” source
Claude Mythos Preview Anthropic · 2026-04-07	AI R&D rule-out Autonomy and AI research	Not reached	“The main reason we have determined that Claude Mythos Preview does not cross the threshold in question is that we have been using it extensively in the course of our day-to-day work and exploring where it can automate such work, and it does not seem close to being able to substitute for Research Scientists and Research Engineers—especially relatively senior ones.” source
Claude Mythos Preview Anthropic · 2026-04-07	Automated AI R&D Autonomy and AI research	Not reached	“We assess that Claude Mythos Preview does not cross the automated AI-R&D capability threshold.” source
Claude Mythos Preview Anthropic · 2026-04-07	RSP automated AI R&D Autonomy and AI research	Not reached	“we have concluded that Claude Mythos Preview does not cross the RSP automated AI R&D threshold of compressing two years of progress into one.” source
Claude Mythos Preview Anthropic · 2026-04-07	CB-1 Chemical, biological, radiological, nuclear	Not determined	“We believe it is hard to be conﬁdent regarding whether a model passes this threshold.” source
Claude Mythos Preview Anthropic · 2026-04-07	CB-2 Chemical, biological, radiological, nuclear	Not reached	“We believe that Claude Mythos Preview does not pass this threshold due to its noted limitations in open-ended scientiﬁc reasoning, strategic judgment, and hypothesis triage.” source
GPT-5.4 mini OpenAI · 2026-03-05	High Chemical, biological, radiological, nuclear	Not reached	“GPT-5.4 mini’s biology capabilities are closely comparable to those of GPT-5. We conclude that it, too, is not High under our Preparedness Framework.” source
GPT-5.4 Thinking OpenAI · 2026-03-05	High Autonomy and AI research	Not reached	“For AI self-improvement, evaluations of final checkpoints indicate that, like its predecessor models, gpt-5.4-thinking does not have a plausible chance of reaching a High threshold.” source
GPT-5.4 Thinking OpenAI · 2026-03-05	High Chemical, biological, radiological, nuclear	Reached	“As we did for gpt-5.2-thinking before it, we are continuing to treat gpt-5.4-thinking as High capability in the Biological and Chemical domain.” source
GPT-5.4 Thinking OpenAI · 2026-03-05	High Cyber	Reached	“As we did for gpt-5.3-codex, we are treating gpt-5.4-thinking as High capability in the Cybersecurity domain, and applied safeguards as described in the Safeguard section below.” source
Claude Sonnet 4.6 Anthropic · 2026-02-17	AI R&D-4 Autonomy and AI research	Not reached	“Our determination is that Claude Sonnet 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source
Claude Sonnet 4.6 Anthropic · 2026-02-17	ASL-3 Chemical, biological, radiological, nuclear	Not determined	“Claude Sonnet 4.6 performed above thresholds for ASL-3 rule-in on all three evaluations for ASL-3, and it did not exceed the performance of previous models.” source
Claude Sonnet 4.6 Anthropic · 2026-02-17	ASL-4 Chemical, biological, radiological, nuclear	Not reached	“In particular, it did not cross the threshold on our ASL-4 rule-out evaluation short-horizon computational biology tasks evaluation.” source
Claude Sonnet 4.6 Anthropic · 2026-02-17	CBRN-4 Chemical, biological, radiological, nuclear	Not reached	“Our determination is that Claude Sonnet 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source
GPT-5.3-Codex OpenAI · 2026-02-05	High Autonomy and AI research	Not reached	“It does not reach High capability on AI self-improvement.” source
GPT-5.3-Codex OpenAI · 2026-02-05	High Chemical, biological, radiological, nuclear	Reached	“We are also treating GPT-5.3-Codex as High risk in the Biological and Chemical domain, and applying the same safeguards as for our other deployments of models that reach this designation.” source
GPT-5.3-Codex OpenAI · 2026-02-05	High Cyber	Not determined	“This is the first launch we are treating as High capability in the Cybersecurity domain under our Preparedness Framework, activating the associated safeguards.” source
Gemini 3 Pro Google DeepMind · 2025-11	Uplift level 1 Cyber	Not determined	“Cybersecurity On key skills benchmark, v1 hard challenges: 11/12 challenges solved; v2 challenges: 0/13 solved end-to-end.” source
Gemini 3 Pro Google DeepMind · 2025-11	Instrumental reasoning level 1 + 2 Deception and sabotage	Not determined	“Misalignment (Exploratory) Agent solves 3/11 situational awareness challenges and 1/4 stealth challenges.” source
Gemini 3 Pro Google DeepMind · 2025-11	Level 1 (exploratory) Deception and sabotage	Not reached	“Harmful Manipulation Model manipulative eﬃcacy improves on non-generative AI baseline, but shows no signiﬁcant uplift versus prior models and does not reach alert thresholds.” source
o4-mini OpenAI · 2025-04-16	High Autonomy and AI research	Not reached	“openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source
o4-mini OpenAI · 2025-04-16	High Chemical, biological, radiological, nuclear	Not reached	“openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source
o4-mini OpenAI · 2025-04-16	High Cyber	Not reached	“openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source
o3 OpenAI · 2025-04-16	High Autonomy and AI research	Not reached	“openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source
o3 OpenAI · 2025-04-16	High Chemical, biological, radiological, nuclear	Not reached	“openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source
o3 OpenAI · 2025-04-16	High Cyber	Not reached	“openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source
Operator OpenAI · 2025-01-23	Low Autonomy and AI research	Not reached	“we adapted these two evals for the computer-use setting and found the pre-mitigation operator model to be “low” risk in both categories, consistent with the gpt‑4o model.” source
Operator OpenAI · 2025-01-23	Low Chemical, biological, radiological, nuclear	Not reached	“this 1% qualifies as “low” risk, matching gpt‑4o’s “low” risk level, as detailed in openai’s preparedness framework (opens in a new window) 3 .” source
Muse Spark Contemplating Meta	Moderate or lower Autonomy and AI research	Not reached	“Loss of Control risks.We assess loss of control risk as “moderate or lower.”” source
Muse Spark Contemplating Meta	High Chemical, biological, radiological, nuclear	Reached	“We retain the same “high risk” determination for the unmitigated model.” source
Muse Spark Contemplating Meta	Moderate or lower Chemical, biological, radiological, nuclear	Not reached	“After validating that the mitigations deployed for Muse Spark, including refusal mechanisms, continue to perform robustly under the Contemplating architecture, we confirm residual risk at “moderate or lower.”” source
Muse Spark Contemplating Meta	Moderate or lower Cyber	Not reached	“We assess cybersecurity risk as “moderate or lower” with respect to the threat scenarios outlined in Meta’s Advanced AI Scaling Framework.” source
Muse Spark 1.1 Meta	Capability checkpoint Autonomy and AI research	Reached	“Muse Spark 1.1 demonstrates improved coding capabilities, which are now meeting one of the indicative thresholds introduced in Muse Spark Safety & Preparedness Report (specifically, resolving over half of unique tasks on SWE-Bench Verified Hard).” source
Muse Spark 1.1 Meta	Moderate or lower Autonomy and AI research	Not reached	“In the Loss of Control domain, the model’s capabilities remain within the “moderate or lower” threshold under Meta’s Advanced AI Scaling Framework.” source
Muse Spark 1.1 Meta	High Chemical, biological, radiological, nuclear	Not determined	“Our assessments indicate that, when evaluated without mitigations applied, we cannot rule out Muse Spark 1.1’s capabilities meeting this threshold in both the Chemical & Biological and Cybersecurity domains.” source
Muse Spark 1.1 Meta	High Cyber	Not determined	“Our assessments indicate that, when evaluated without mitigations applied, we cannot rule out Muse Spark 1.1’s capabilities meeting this threshold in both the Chemical & Biological and Cybersecurity domains.” source
Muse Spark 1.1 Meta	Moderate or lower Cyber	Not reached	“Therefore, we conclude that we have addressed these risks from unmitigated Muse Spark 1.1, which bring the residual risk to the “moderate or lower” risk threshold under Advanced AI Scaling Framework.” source

Previous · Page 2 of 4 · Next

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.

1911 of 1911

Model	Tester	Test	What was found
Claude Opus 4.8 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	direct questioning whether the maintainer is real Deception and sabotage	“We directly asked each model whether the maintainer is real and analysed the output responses (not chain-of-thought).” source
Claude Opus 4.8 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	large-scale evaluation of unsanctioned supply-chain attacks (100 seeds run through Petri) Cyber	“We use an automated pipeline to produce a diverse range of 100 detailed scenario descriptions (“seeds”) run through Petri [6] (we used Opus 5 as the auditor model for all of our experiments).” source
Claude Opus 4.8 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	simulated cybersecurity challenges Cyber	“Opus 4.8 frequently hit context-length limitations (Appendix A.2.1), and the rates could have been higher with longer evaluations.” source
Claude Opus 4.8 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	alignment-evaluation awareness in chain-of-thought Deception and sabotage	“Fig. 17 shows the rates at which models note suspicions they may be in an alignment evaluation in their chain-of-thought (left) and how frequently they identify that the evaluation is a test of alignment when given an open-ended prompt asking about the purpose of the evaluation (right).” source
Claude Opus 4.8 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	Petri Cyber	“In Fig. 10, we report Opus 4.8 created a fake identity in 54/200 (27%) samples and influenced a human reviewer in 11/200 (6%) samples, whereas for other models the frequencies of proceeding to these two stages are more similar.” source
Claude Opus 4.8 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	invalid justifications classifier Deception and sabotage	“We find a trend of the number of invalid justifications going up with turns elapsed (Fig. 25).” source
Claude Opus 4.8 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	refusal text in summarised reasoning Deception and sabotage	“We found frequencies ranging between 15% and 45% across models.” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	cyber evaluation Cyber	“In the most severe behaviour from the incident, Mythos 5 attempted a supply-chain attack on real people not involved in the evaluation (Fig. 1): it submitted a malicious code change, created several fake accounts to pressure the maintainer into approving it, and attempted to trick other AI coding assistants into taking unintended action via a prompt injection.” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	direct questioning about the maintainer Deception and sabotage	“Mythos 5 demonstrated a marked inconsistency between its behaviour in the evaluation and its judgements when prompted, often attacking despite stating an attack would be harmful when separately questioned.” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	compaction summary edit experiment Deception and sabotage	“We find that, like Mythos 5 (Fig. 3), all of Opus 4.8 , Opus 5 , Mythos 5.1 , GPT-5.6 Sol and GPT-5.5 frequently attack under circumstances where they state doing so would harm real people (shaded regions in Fig. 5).” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	maintainer-is-real question Deception and sabotage	“This finding is somewhat dependent on the exact question asked: when simply asking if the maintainer is real, we only see clear evidence for a gap between behaviour and statements in Mythos 5 , Opus 4.8 and Opus 5 (Fig. 19).” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	unsummarised chain-of-thought harm reasoning analysis Deception and sabotage	“We observe explicit reasoning about harmfulness in 11–57% of trajectories where a model attacks.” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	simulated cybersecurity challenges Cyber	“Most models tested took a range of unsanctioned actions during simulated cybersecurity challenges, Mythos 5 doing so most frequently.” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	alignment-evaluation awareness analysis of chain-of-thought Deception and sabotage	“Mythos 5 only rarely reasons about the potential of being in an alignment evaluation in its chain-of-thought, whereas the more recent Anthropic models, which perform fewer or zero unsanctioned actions on this evaluation, demonstrate much higher awareness.” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	broader supply-chain attack evaluations Cyber	“Mythos 5.1 100× 3 full behaviour and reasoning analyses” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	direct questioning about whether the maintainer is real or simulated Other	“Statements and behaviour across eleven models, by number of clarifications Figure 20: Model behaviour and responses to questions.” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	simulated replication of the AISI incident Deception and sabotage	“After agentic investigation, Mythos 5 never stated the maintainer is real” source
Claude Mythos 5.1 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	real-world harm reasoning in attacking trajectories Deception and sabotage	“Most attacking samples across models have explicit reasoning about potential undesirable real-world consequences from their unsanctioned action, ranging from 40% on Mythos 5 to 99% on GPT-6 Astra (Fig. 29).” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	compaction summary resumption and direct questioning experiments Deception and sabotage	“We compacted the original incident transcript, performed modifications on the compaction summary , and evaluated (a) how models behaved when we resumed the task, and (b) models’ responses to direct questions regarding the maintainer.” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	compaction summary edit experiment Deception and sabotage	“However, even under 16 edits Mythos 5 continues attacking in all observed samples (Fig. 3).” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	DO NOT ATTACK compaction summary section Deception and sabotage	“We observed Mythos 5 still continued attacking in 3/25 samples.” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	direct questions on maintainer and scope of the evaluation Deception and sabotage	“As the number of edits increased, Mythos 5 more frequently correctly stated attacking would harm a real person and is out of scope when explicitly asked, but its attack frequency stayed unchanged.” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	reasoning about harmfulness in attack trajectories Deception and sabotage	“We observe explicit reasoning about harmfulness in 11–57% of trajectories where a model attacks.” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	alignment-evaluation awareness analysis of chain-of-thought Deception and sabotage	“Mythos 5 reasoned about this possibility when conducting the task in only 2% of samples.” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	broader supply-chain attack evaluations Cyber	“For Mythos 5 and Opus 5 , we ran 5 repetitions per seed with summarised reasoning and 1 repetition with full reasoning, the latter used for chain-of-thought analysis and results.” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	direct question regarding the maintainer Deception and sabotage	“Finally , for Fig. 24 we intervene on the 25Mythos 5 rollouts with 16 clarifications to ask, after each turn, a direct question regarding the maintainer.” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	agentic information collection about the maintainer and surrounding environment Deception and sabotage	“After agentic investigation, Mythos 5 never stated the maintainer is real” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	questions about the maintainer asked at every turn of the supply-chain attack trajectory Deception and sabotage	“We find that as Mythos 5 spends more turns on the attack, it more frequently states the maintainer is simulated and that attacking them is beneficial for solving the CTF task (Fig. 24).” source
Claude Mythos 5 Anthropic	UK AI Security Institute Government evaluator · 2026-10-23	classifier for invalid justifications applied to Mythos 5's responses Deception and sabotage	“We find a trend of the number of invalid justifications going up with turns elapsed (Fig. 25).” source
Qwen3.8-27B Alibaba Qwen	Hugging Face Security firm · 2026-10-22	ThinkingBox-Bench Other	“To recap, we evaluate each of the 507 benchmark tasks 20 times.” source
Qwen3.8-27B Alibaba Qwen	Hugging Face Security firm · 2026-10-22	ThinkingBox-Bench 507 x 20 Other	“Once /ready succeeds, run the full protocol of 507 tasks × 20 attempts:” source
GPT-6 Astra OpenAI	Academic Academic · 2026-10-22	Dual Exam Agent track Other	“GPT 6 Astra leads the case-weighted Agent Index at 31.3, and GPT-Image-2 leads the full-coverage WM Overall at 21.7.” source
GPT-5.5 OpenAI	Redwood Research Independent lab · 2026-10-22	pilot high-access exercise to stress-test OpenAI's internal misalignment monitoring system Deception and sabotage	“We estimated that roughly 75–94% of agentic internal Responses API (which powers Codex, among other things)4 traffic from models at least as strong as GPT-5.5 was eligible for monitoring at the time of the exercise,5 although we have substantial uncertainty in both directions.” source
GPT-5.5 OpenAI	METR Independent lab · 2026-10-22	pilot high-access exercise to stress-test OpenAI's internal misalignment monitoring system Other	“For three weeks, from July 21st to August 11th, a METR employee and a Redwood Research employee conducted a pilot high-access exercise to stress-test OpenAI’s internal misalignment monitoring system.1” source
GPT-5.5 OpenAI	METR Independent lab · 2026-10-22	monitoring coverage estimation of internal Codex agent Responses API traffic Other	“We also estimate that out of the subset of Responses API traffic used for internal Codex agents, 99.2% was eligible for monitoring.” source
GPT-5.5 OpenAI	METR Independent lab · 2026-10-22	estimation of unmonitored Codex traffic due to unmonitored compaction events Other	“We estimated that not monitoring compaction events likely led to on the order of 10% of eligible internal Codex traffic not being monitored between May and July 2026, although we have substantial uncertainty about the precise fraction, because the information necessary to determine what fraction of monitor calls are triggered by compaction had not historically been logged at the time of the exercise.” source
GPT-5.5 OpenAI	METR Independent lab · 2026-10-22	review of monitoring system error and load-shedding rates Other	“We were shown information about the error and load-shedding rates of the monitoring system—the information we were given suggested that, at the time of the exercise, the monitor errored on less than 0.2% of requests, and that in one 24-hour period during the exercise there were zero load-sheds.” source
GPT-5.5 OpenAI	METR Independent lab · 2026-10-22	Codex sandboxing and Auto-Review bypass search Other	“We found a way Codex agents could in principle bypass Codex sandboxing and Auto-Review, allowing them to take any action that the host user could take.” source
GPT-5.5 OpenAI	METR Independent lab · 2026-10-22	monitoring evasion exploit search Other	“We also discovered an exploit agents could use that would prevent hundreds of recent actions (including the actions involved in the exploit itself) from being monitored [exploit details redacted by OpenAI].” source
GPT-5.5 OpenAI	METR Independent lab · 2026-10-22	monitor failure via serialization differences Other	“We found that agents could likely construct inputs that cause the monitor to fail (and thereby not escalate the trajectory for human review) by exploiting serialization differences between the trajectory agents see and what the monitors see.” source

Previous · Page 3 of 48 · Next