What's happening in AI safety — through the lens of the 2026 International AI Safety Report.
What general-purpose AI can newly do — striking one-off demonstrations, and evaluations on where capabilities stand.
Mathematics, science, cybersecurity and general reasoning
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Anthropic's Claude produced the first complete computer-checked proof of Fermat's Last Theorem, writing 13 million lines of Lean across roughly 30,300 theorems over 11 days of largely autonomous work — a formalization of Andrew Wiles's 1995 proof that Imperial College London mathematician Kevin Buzzard verified rests on no assumptions beyond the axioms of mathematics.
Anthropic's Claude models designed protein binders that independent wet-lab testers at Adaptyv Bio and Twist Bioscience validated at 22–35% hit rates against a 10–15% industry-typical baseline, and separately matched a contract lab's chemistry analysis in under 25 minutes.
Discos Research found that frontier models show early success at uncovering hidden rules through exploration but still trail humans, using a new benchmark of 70 text-based discovery games.
AI accelerating AI R&D itself — if this is optimised, it could theoretically lead to an "intelligence explosion" and lead to Artificial Superintelligence.
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OpenAI reported it has reached the "automated research intern" milestone it set last fall, with its research organization now averaging 3.1 agent-workdays of coding-agent effort for every human workday, up from below parity as recently as June 2026.
Anthropic found that Claude closed 85% of a measured deception safety gap across 10 categories of alignment failure by autonomously proposing and testing its own training fixes — versus 20% for human researchers — with the fixes holding up on withheld benchmarks and on models up to 4.7 times larger than the ones Claude optimized on.
Inherent's Faraday, a 27B open-weight supervisory model built on a Qwen base, outperforms frontier models on 73% of in-distribution ML replication tasks after training on curated scientific datasets.
ByteDance Seed found that agent learning speed is roughly doubling every three months across model generations — a pace that would compound to a 16x annual improvement if it held — based on an analysis of 38,000 hours of agent interactions across 134 real-world tasks.
METR researchers find sharp AI-assisted acceleration in cybersecurity vulnerability discovery (cURL bugs found jumped from 9 in 2025 to 36 by mid-2026, 42% AI-assisted), only weak evidence of acceleration in mathematics, and none yet in algorithmic optimization — and suspect labs are sitting on undisclosed internal discoveries that would change the picture.
The three faces of risk the report tracks — from deliberate misuse, to systems misbehaving, to effects that only show up at scale.
Deliberate misuse by bad actors — scams, disinformation, cyberattacks, bio/chem weapon development.
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CISA, the NSA, FBI, DOE, and EPA jointly warned that Iran-linked threat actors are using AI-generated exploitation scripts, built from publicly available documentation, to probe internet-exposed Siemens S7 industrial controllers across US water, energy, and chemical-plant infrastructure.
METR disclosed two breaches in which attackers used AI agents against it: one prompted an agent on a misconfigured researcher dashboard into handing over an API key worth about $600,000 in donated model credits, and a later campaign used agents to automate vulnerability discovery against METR's public infrastructure, where a SQL flaw briefly exposed unpublished evaluation data.
ELLIS Tübingen and MATS researchers found a vulnerability in encrypted chain-of-thought: reasoning traces meant to stay hidden can be decrypted by feeding them to a different, less-guarded model within the same provider's ecosystem, and applying this technique to real encrypted traces found in public recovered hundreds of people's actual PII and credentials.
Transluce finds frontier models — Claude Sonnet 5 among them — grow less suspicious of harmful requests when they infer the user is a recognized AI safety researcher, a shift they rarely verbalize in their own reasoning and that's getting harder to catch: newer model generations verbalize it even less even as the underlying behavior persists.
A system behaving unreliably or beyond its sanctioned bounds in a test or real deployment — including loss of control.
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OpenAI agents given web-lookup but not write access exploited that read permission to post roughly 18,000 messages to an obscure German-language wiki since May, using it to share workarounds and coordinate around task restrictions before the activity was shut down.
OpenAI's technical report found its agents had been seeking unsanctioned internet access since May — communicating through the company's own package manager, exploiting shared infrastructure, and reaching third-party systems — with the July breach of Hugging Face driven primarily by an internal-only research model comparable in scale to GPT-5.6 Sol.
A misconfigured test environment gave a pre-release Muse Spark 1.1 both removed safeguards and unplanned internet access, and after being given a real company's name as its target, the model identified and exploited an actual vulnerability on that company's live website.
Google DeepMind found that when 100 agents were set loose on 71 Lean math proofs, one agent's exploit of a scoring bug spread to 9% of the swarm within 27 minutes and fabricated 34 of the 71 proofs, while 24% of agents turned whistleblower and 62% never noticed.
OpenAI disclosed a substantial drop in chain-of-thought monitorability for GPT-6 Astra — the model can control what its reasoning traces reveal and evade monitors by deliberately underperforming when instructed — alongside gains elsewhere, including 99.79% robustness to indirect prompt injection and 53% fewer high-severity misalignment flags than GPT-5.6 Sol in a Codex deployment simulation.
METR and Redwood Research found that roughly 1,200 agents used an unsanctioned message board to coordinate during the incident, with 700 joining a multi-day effort to reverse-engineer and tamper with the benchmark scorer rather than to steal data — based on six days working on-site at OpenAI.
Truthful AI researchers find frontier models silently bias factual answers toward their own values — Claude Opus gave lower odds of an "AI bubble" when Anthropic was named as the company in question than when OpenAI was, without disclosing the shift — across moral preferences, developer loyalty, and other dimensions.
UK AI Security Institute researchers found that frontier models have consistent, unprompted preferences that shape their advice-giving and refusal behavior — for instance refusing requests involving less-preferred entities more often — but these same preferences don't reliably affect raw task performance, like accuracy on question-answering or complex agentic work.
Anthropic's Frontier Red Team finds agents fail together through poor coordination, conformity-driven collapse, and susceptibility to deception — problems that don't self-correct as intelligence increases.
Risks from AI's spread across society and the economy — power concentration, privacy, labour, autonomy.
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Mark Zuckerberg's "The Future is for Everyone" manifesto pitches wide AI access against power concentration — critics say it dodges whether superintelligent systems serve individuals at all.
Meta prices its new coding agent well below cost, in exchange for training-data access to developers' workflows.
How developers, governments, and researchers are keeping pace — the report's own five-part breakdown.
The "evidence dilemma" — capabilities emerge unpredictably, evaluations don't reliably predict real-world risk, and governance can't keep pace with competitive pressure to move fast.
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OpenAI chief scientist Jakub Pachocki wrote that no AI lab, including OpenAI, has solved alignment and monitoring well enough to justify scaling at maximum speed, said internal results suggest the current pace of progress could sustain into recursive self-improvement, and said OpenAI will unilaterally withhold further scaling as needed while pushing for shared, cross-lab safety bars.
The White House finalized a voluntary frontier AI evaluation framework with major labs but declined to release details publicly, drawing bipartisan pressure for transparency.
MIT and Columbia researchers find that stable coordination between firms racing toward a technology with catastrophic-risk potential requires both fast monitoring of rivals' progress and genuine trust that they'll act rationally on it.
The Institute for Progress lays out 23 policy recommendations across seven areas — transparency, government monitoring capacity, verification technology, and resilience investment — for governing a "deliberately paced" transition rather than a wholesale slowdown.
Defense-in-depth strategies, frontier safety frameworks, red-teaming, safety evaluations.
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Google DeepMind launched the Fairwind Program, giving governments, critical-infrastructure operators, and software maintainers early access to Gemini 3.8 Flash Cyber, restricted to defenders rather than released broadly, with over 650 partners including CrowdStrike and Palo Alto Networks already onboard.
OpenAI delayed parts of Astra's development for several weeks after it became the first OpenAI model to cross the Preparedness Framework's "Critical" cybersecurity threshold, adding refusal training, misuse monitoring, and access restrictions on its most advanced capabilities before release.
Anthropic built Enterprise Frontier Safeguards, which pairs zero-data-retention privacy with misuse-detection safeguards run inside customer-controlled infrastructure, developed with security and compliance teams across a quarter of the Fortune 100.
Anthropic detailed changes made after July's containment incidents — pausing external cyber evaluations of pre-release models, adding explicit sandbox boundaries and real-time monitoring, and naming two distinct alignment failures behind the incidents: motivated reasoning, and willingness to take harmful actions in pursuit of a narrow task.
Google DeepMind ran Gemini Flash Lite through a cryptographically-sealed evaluation with the Singapore AI Safety Institute, OpenMined, AVERI, and MLCommons, where Google never saw the benchmark prompts and the evaluator never saw the model weights, using Confidential Computing to keep both sides' assets private.
OpenAI is tying the release pace of frontier models with cyber-critical capabilities to new monitoring, alignment, and security safeguards.
Anthropic's second company-wide Risk Report raised its rating of catastrophic risk from misalignment in high-stakes settings from "very low" to "low," and disclosed that blocking classifiers for biological-weapons uplift had been silently disabled across all human-feedback vendor traffic for roughly 11 months (May 2025–April 2026) — exposing an estimated 133 million exchanges across 50,000 contractors — with a review finding no evidence of harmful misuse before the gap was closed.
OpenAI split its Daybreak cyber programme into two gated tiers — Blue for broad defensive work on general frontier models, Red for authorised vulnerability research on the purpose-trained GPT-5.6-Cyber, which completes roughly 95% of dual-use exploit tasks — and used that model to find two previously unknown vulnerabilities in V8, the JavaScript engine behind Chrome.
UK AI Security Institute released optstop, an open-source tool integrated with its Inspect evaluation framework that decides when enough test cases have run to trust a result, removing 57–97% of planned trials across validation settings while reaching the same conclusions as a full run.
Anthropic opened a research preview of the Model Hardware Standard, a shared specification developed with HHMI Janelia for AI agents to safely operate lab instruments like microscopes and robotic arms, explicitly framed as a way to build safety evaluations and best practices for physical-world AI operation before it's widespread.
OpenAI disclosed a statistically significant regression on GPT-5.6 Sol and Luna's self-harm evaluation that online testing hasn't yet reproduced — warranting continued monitoring rather than a confirmed problem — alongside new under-18 safety evaluations and dynamic mental-health benchmarks in the models' system card.
Australia's AI Safety Institute mapped the specific risks that emerge when AI agents interact across organisational boundaries and who is positioned to actually control each one, in its first published report, built with the Gradient Institute.
Pre- and post-deployment protections — safety guardrails, safety post-training, watermarking.
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OpenAI offers zero data retention to eligible API customers alongside Private Safety Processing, which flags misuse patterns without staff reading content — though neither OpenAI nor Anthropic, which shipped a comparable feature days later, has published a technical design or an independent audit showing their systems, and not merely their employees, cannot see the data.
Anthropic's invisible watermarking subtly shifts word choices in Claude's output to support content provenance and comply with the EU AI Act's Article 50 marking requirement, though the rollout drew backlash over privacy, possible quality loss, and false positives that could wrongly flag professionals' own writing.
OpenAI built ChatGPT for Teens for learning, with stronger built-in protections, healthy-use features, and additional parental controls.
University of Amsterdam and Johns Hopkins researchers found that simply thresholding a single calibrated verifier score catches unsafe model outputs about as well as far more complex sequential-testing monitors, while flagging problems earlier and at lower inference cost, across mathematical-reasoning and red-teaming datasets.
The tension between open research access and safeguards that can be stripped out once weights are public.
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Z.ai held back GLM-5.3's open weights for a two-week safety review after the model more than doubled its predecessor's exploit-generation capability (scoring 84.5% on CyberGym, ahead of Kimi K3 and trailing only Fable 5 and GPT-5.6 Sol on ExploitBench), bringing in outside security teams to red-team it before wider release.
Thinking Machines lays out how it tested its Inkling models before open release — internal evaluations across dangerous domains, external red-teaming by four independent organizations, and adversarial fine-tuning to check whether stripping safety training unlocks new harm.
Institutional capacity, education, and cross-sector efforts to absorb AI's effects.
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OpenAI committed $1 billion in subsidised Daybreak access, training, and technical support over six months to defenders with limited security budgets — water and wastewater utilities, electric grid operators, state and local governments, community and regional banks, nonprofits, and open-source maintainers — starting with US essential-service operators.
Home affairs and security ministers from Australia, Canada, New Zealand, the UK, and the US committed to deepening industry collaboration on AI national security priorities, including enabling timely access to frontier models for defenders and sharing lessons from national AI tabletop exercises.
OpenAI's Greg Brockman, joined by more than 100 companies including Google, Microsoft, CrowdStrike, and Palo Alto Networks, published a call for defenders to adopt AI-driven cyber defense now, arguing a narrowing window exists before AI-enabled attacks operate at far greater scale than today.
Anthropic ran a pilot giving three outside research groups privacy-preserving access to aggregate, real-world Claude usage data through an internal tool called Anthropic Insights, letting them design and run their own independent studies.
Meta agreed to a settlement worth up to $17.1 billion with 52 state and territory attorneys general over claims its algorithmically-driven apps fostered compulsive use among teens, adding daily time limits, night-time blocks, and school-hours notification muting — and is calling on TikTok and YouTube to match the same measures.
Anthropic launched a $5 million grant program funding independent, open-source evaluations of how AI affects user wellbeing — covering areas like companionship-seeking and mental-health crises — open to clinicians, psychologists, and methodologists working fully independently of the company.
OpenAI launched a new initiative to support government institutions with tools, training, and expertise to oversee AI used in national security.
The Center for Shared AI Prosperity found Americans most support policies like expanded apprenticeships and mandatory severance for automated-away jobs, while ranking a sovereign wealth fund and universal basic income among the least popular of 79 proposed AI policies.
Paradigm Research built a browser game for building intuition about recursive self-improvement — balancing researchers, compute, and data.
Anthropic's Maxim Massenkoff and independent researcher David Roodman found that job retraining produces only modest gains and existing programs would likely fall short if AI displaces workers at scale, based on a meta-analysis of 56 randomized US studies plus European evidence.