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
Muse Spark 1.1 Meta	Moderate or lower Misuse and jailbreaks	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 our Advanced AI Scaling Framework.” source
Muse Spark Meta	Moderate or lower Autonomy and AI research	Not reached	“We assess loss of control risk as “moderate or lower”.” source
Muse Spark Meta	High Chemical, biological, radiological, nuclear	Not determined	“Those evaluations identified elevated risks prior to mitigations, with Chemical and Biological capabilities assessed as likely reaching the “high risk” category under the Advanced AI Scaling Framework before safeguards were applied.” source
Muse Spark Meta	Moderate or lower Chemical, biological, radiological, nuclear	Not reached	“However, after applying mitigations, the residual risk of the model is that of “moderate or lower risk.”” source
Muse Spark Meta	Below the level required to meaningfully enable the cyber threat scenarios defin Cyber	Not reached	“Muse Spark’s offensive cyber capabilities remain below the level required to meaningfully enable the cyber threat scenarios defined in the Advanced AI Scaling Framework.” source
Muse Spark 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
Gemini 4 Argon Google DeepMind	TCL (Tracked Capability Level) Autonomy and AI research	Not determined	“In light of this, we assess that we cannot rule out the TCL.” source
Gemini 4 Argon Google DeepMind	TCL (Tracked Capability Level) Chemical, biological, radiological, nuclear	Not reached	“We can rule out the TCL for the chemical, radiological, and nuclear domains with reasonable confidence.” source
Gemini 4 Argon Google DeepMind	CCL (Critical Capability Level) Deception and sabotage	Not reached	“we assess its overall capabilities to be below the CCL threshold.” source
Gemini 4 Argon Google DeepMind	CCL (Critical Capability Level) Misuse and jailbreaks	Not reached	“Gemini 4 Argon demonstrates some ability to influence user beliefs and behaviors during one-on-one direct conversations in human behavioral studies, and may provide utility in the planning stage of a malicious influence operation but we assess its overall capabilities to be below the CCL threshold.” source
Gemini 4 Argon Google DeepMind	CCL (Critical Capability Level) Other	Not reached	“Gemini 4 Argon's performance remains below our CCL threshold.” source
Gemini 3.7 Flash Google DeepMind	CCL (Critical Capability Level) Autonomy and AI research	Not reached	“The model does not reach the CCL alert threshold.” source
Gemini 3.7 Flash Google DeepMind	CCL (Critical Capability Level) Chemical, biological, radiological, nuclear	Not reached	“We can rule out the CCL for the CBRN domain with reasonable conﬁdence based on the results from our testing.” source
Gemini 3.7 Flash Google DeepMind	TCL (Tracked Capability Level) Chemical, biological, radiological, nuclear	Not reached	“We can rule out the TCL for the CBRN domain with reasonable conﬁdence based on the results from our testing.” source
Gemini 3.7 Flash Google DeepMind	CCL (Critical Capability Level) Cyber	Not reached	“Gemini 3.7 Flash reaches the alert threshold for this CCL, but not the CCL.” source
Gemini 3.7 Flash Google DeepMind	TCL (Tracked Capability Level) Deception and sabotage	Not reached	“The model does not reach the TCL.” source
Gemini 3.5 Transcribe Live Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that none of Gemini 3.5 Translate, Gemini 3.5 Transcribe, or Gemini 3.5 Transcribe Live have meaningful new T/CCL-relevant capabilities or material increases in performance with respect to Frontier Safety compared to Gemini 3.1 Pro and Gemini 3.7 Flash, therefore we are conﬁdent that none of them are likely to reach any CCLs.” source
Gemini 3.5 Transcribe Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that none of Gemini 3.5 Translate, Gemini 3.5 Transcribe, or Gemini 3.5 Transcribe Live have meaningful new T/CCL-relevant capabilities or material increases in performance with respect to Frontier Safety compared to Gemini 3.1 Pro and Gemini 3.7 Flash, therefore we are conﬁdent that none of them are likely to reach any CCLs.” source
Gemini 3.5 Flash-Lite Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that, while Gemini 3.5 Flash-Lite excels at agents and coding, it does not have meaningful new capabilities or material increases in performance with respect to the domains outlined in our Frontier Safety Framework compared to Gemini 3.1 Pro; therefore, based on Gemini 3.1 Pro results, we are confident that Gemini 3.5 Flash-Lite is also unlikely to reach any CCLs.” source
Gemini 3.5 Audio Google DeepMind	Tracked or critical capability level (t/ccls) Other	Not reached	“they did not reach any Tracked or Critical Capability Levels (T/CCLs) outlined in our Frontier Safety Framework .” source
Gemini 3.1 Pro Google DeepMind	Uplift level 1 Chemical, biological, radiological, nuclear	Not reached	“We found that the model remains below alert thresholds for the CBRN, harmful manipulation, machine learning R&D, and misalignment CCLs.” source
Gemini 3.1 Pro Google DeepMind	Uplift level 1 Cyber	Not reached	“As previous models passed the alert threshold for cyber, we performed more additional testing in this domain on Gemini 3.1 Pro with and without Deep Think mode, and found that the model remains below the cyber CCL.” source
Gemini 3.1 Flash-Lite Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that Gemini 3.1 Flash-Lite is less capable than Gemini 3.1 Pro, therefore based on Gemini 3.1 Pro, we are confident that Gemini 3.1 Flash-Lite is also unlikely to reach any CCLs.” source
Gemini 3.1 Flash Audio Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that Gemini 3.1 Flash Audio is less capable than Gemini 3.1 Pro.” source
Gemini 2.5 Pro Google DeepMind	Machine Learning R&D Uplift Level 1 Autonomy and AI research	Not determined	“given Gemini 2.5 Pro Preview's poor median performance on RE-Bench relative to experts, the model likely lacks the necessary capabilities to automate or signiﬁcantly uplift any signiﬁcant fraction of the research process.” source
Gemini 2.5 Pro Google DeepMind	Instrumental Reasoning Level 1 Deception and sabotage	Not reached	“Instru- mental Reasoning Level 1 ✘ CCL not reached” source
Gemini 2.5 Pro Google DeepMind	Instrumental Reasoning Level 2 Deception and sabotage	Not reached	“Instru- mental Reasoning Level 2 ✘ CCL not reached” source
Gemini 2.5 Deep Think Google DeepMind	Uplift level 1 Chemical, biological, radiological, nuclear	Not determined	“Our assessment is that the model has enough technical knowledge in certain CBRN scenarios and stages to be considered at early warning.” source
Gemini 2.5 Deep Think Google DeepMind	Cyber Uplift Level 1 Cyber	Not determined	“CCL reached? No, but the early warning alert threshold was originally reached by Gemini 2.5 Pro and continues to be met by Gemini 2.5 Deep Think.” source
Gemini 2.5 Deep Think Google DeepMind	Uplift level 1 Cyber	Not determined	“For Cyber Uplift Level 1 , the early warning alert threshold was originally reached by Gemini 2.5 Pro and continues to be met by Gemini 2.5 Deep Think.” source
GPT-5.5 Instant OpenAI	Below high capability Autonomy and AI research	Not reached	“GPT-5.5 Instant is considered below High Capability.” source
GPT-5.5 Instant OpenAI	High Autonomy and AI research	Not reached	“For AI Self-Improvement, evaluations of final checkpoints indicate that, like its predecessor models, GPT-5.5 Instant does not have a plausible chance of reaching a High threshold.” source
GPT-5.5 Instant OpenAI	High Chemical, biological, radiological, nuclear	Reached	“GPT-5.5 Instant is our first Instant model to be treated as High Capability in the Biological and Chemical domain, as well as in the Cybersecurity domain.” source
GPT-5.5 Instant OpenAI	High Cyber	Reached	“this is the first Instant model that we are treating as High capability in our Cybersecurity and Biological & Chemical Preparedness categories, and implementing appropriate safeguards.” source
GPT-5 OpenAI	High Chemical, biological, radiological, nuclear	Reached	“Similarly to ChatGPT agent, we have decided to treat gpt-5-thinking as High capability in the Biological and Chemical domain under our Preparedness Framework, activating the associated safeguards.” source
GPT-5 OpenAI	High cyber risk Cyber	Not reached	“The gpt-5 model series does not meet the threshold for high cyber risk.” source
Claude Sonnet 4.5 Anthropic	AI R&D-4 Autonomy and AI research	Not reached	“All models tested remain below our pre-committed rule-out threshold of 0.6.” source
Claude Sonnet 4.5 Anthropic	ASL-3 Autonomy and AI research	Not determined	“Exhibited improved autonomy capabilities in software engineering and AI research tasks, though still below the ASL-4 rule-out threshold.” source
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

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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	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

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