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The Samsung S90H OLED TV is a winning 2026 publish from Samsung, delivering impressively vibrant yet natural colors and striking contrast. An upgraded type of the S90F, alongside improved glare safety and brighter SDR brightness, it has been praised as among the most versatile OLED TVs, particularly for daytime viewing. Right now, a 55-inch Samsung S90H OLED TV is  $400 off on Amazon at a document low cost of $1,197.99 (originally $1,597.99) in an first Black Friday deal. It’s value noting that the another sizes are additionally discounted, alongside the 77-inch example 29% off.

Samsung S90H OLED TV

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$1,197.99 at Amazon

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While it has identical pixel-level difference and broad viewing angles compared to the S90F, the important upgrade current is a matte-like screen, earlier lone seen on Samsung’s pricier models akin the S95 series, bringing an additional perk and improved consideration authority for those alongside sunny media rooms. Contrast and color accuracy are highlights on this model, as is shade detail.

The S90H offers four HDMI 2.1 inputs and supports refresh rates up to 165Hz alongside a compatible PC, up from the S90F’s 144Hz. It additionally supports VRR, FreeSync Premium Pro, G-SYNC, and ALLM. Like its predecessor, it’s powered by the NQ4 AI Gen3 processor for image and audio processing. Its 40W, 2.1-channel speaker scheme supports Dolby Atmos, although neither TV supports Dolby Vision.

If you’re looking for a well-equipped OLED at a mid-range price, the discounted 55-inch Samsung S90H is value considering, particularly if you’re a PC gamer or observe in a area prone to glare. It offers a stronger characteristic set than the S85H without requiring the jump to the additional costly S95H. Just difference the S90F’s current cost before buying, particularly if movie nights in a dreary area are your priority.

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How a third-party colony created a way into multiple organizations, and how Sentra uncovered it in small than 24 hours.

Co-authored alongside Sagy Drucker, Asaf Rozali and Riva Kranz

Sentra lately uncovered a extremely broad provision sequence assault that moved through institution following organization. Dozens of Qubole customers were impacted. An attacker got clasp of credentials belonging to Qubole, a big-data platform, and used the admission that business following business had formerly granted Qubole to quietly drag their data. Nobody bankrupt into these companies directly. The attacker came in through a vendor they all trusted.

This benevolent of hazard is uncomplicated to miss. Your haze is complete of identities that aren't people: vendor integrations, automation and AI agents, all holding position admission to your data. Most days they do the identical thing, from the identical place, on the identical schedule. The danger is the day one of them starts doing item else, and extremely few teams are observing for it.

The riskiest of these are the ones you forgot. Every vendor you have always connected to your haze remaining a entrance behind. Most of those doors are motionless there, trusted, rarely watched, and frequently capable to attain your most delicate data.

Sentra spotted the run and reported it to Wiz, which investigated it together alongside Sentra and Mandiant. Wiz has published the complete research: Trusted by Default: Qubole Cross-Account Access Abused in the Wild.

This article isn't the narrative of one company. It's what we saw throughout the attacks: how the attacker got in, how they moved, what they went after, and what eventually gave them away.

The assault at a glance

• More than a dozen organizations hacked in a sole attack, all through admission they had formerly granted Qubole.
• About a second to assume all Qubole-trusted role, so the attacker could move from one business to the next nearly instantly.
• About 100 passwords and keys peruse in 7 minutes, from three distinct places they were stored, affirmative thousands of credential records inner the records the attacker downloaded.
• 0 compose actions. Nothing was changed or deleted, lone stolen, which made it harder to notice.

Forgotten vendor trust

The doors the attacker used were old ones. Qubole roles were motionless in location lengthy following companies had stopped using Qubole, and those roles could peruse nearly all of the data the attacker went after.

Qubole's roles had been idle since the spring. Then they came rear to life. Sentra had seen this hazard coming. Before the attack, it had already flagged one of Qubole's roles as unused for months yet motionless capable to attain delicate data.

Valid credentials, incorrect network

The organizations hit by this assault weren't careless. They set up their Qubole integrations the way AWS recommends, including an additional inspect called an ExternalId, a code the vendor must current before it can get in. Every Qubole function had it, and the attacker presented the correct code all time. As far as AWS could tell, the attacker was Qubole.

None of the action came from Qubole's own network. It all came from a rented OVH server and the Mullvad VPN, and there was no normal Qubole action at all in that window. Every meeting was the attacker.

That's what makes provision sequence attacks so dangerous. When a trusted vendor is compromised, following finest custom isn't enough, since the attacker's admission is genuine and looks lawful in all log. Only two things halt it. Zero rely limits all vendor to the data it really needs, so a taken key opens as small as possible. And data finding and reply (DDR) watches how all character really uses data, flagging the instant a trusted one starts behaving akin an attacker. In this attack, the admission looked valid. The behavior didn't.

Test quietly, afterward sweep

The attacker started slowly. They tested the taken credentials on one low-value Qubole role, took a quiescent appearance around, and made a archetypal theft of thousands of customer-profile files. Then they moved fast. Within minutes they entered all Qubole-trusted function and started bulk downloads that ran in the backdrop complete the following days. A final pass, from a distinct VPN address, pulled manufacturing repository backups.

Then the action stopped. The trusts were motionless in place, so the attacker went quiescent without always losing access.

How the attacker stole credentials

Stealing data wasn’t enough. We saw the attacker use multiple distinct techniques to pilfer credentials that could open equal additional doors:

• Pulling secrets in bulk from Secrets Manager: source-control, identity-provider, CI and repository credentials, and equal the vendor’s own tokens and ExternalIds.
• Reading decrypted parameters from SSM Parameter Store, including monitoring and scanner delegate keys.
• Harvesting Lambda surroundings variables, switching to a second path whenever the archetypal one was blocked.
• Scraping EC2 person data, the startup scripts anywhere tokens and passwords frequently hide.

How we traced it

The archetypal job was matching the leaked samples to data Sentra had already scanned. We compared what was inner the files, not fair their names, since names are uncomplicated to alter and satisfied isn't.

Within a few hours we had it downward to a fistful of S3 buckets. Those were the sources, and the inquiry became much narrower: who could peruse them?

Each origin bucket could be reached by hundreds of identities: assistance roles, admins, sole sign-on users and automation. Chasing all one of them in a day wasn't realistic. So we narrowed to external identities, the accounts exterior a company's surroundings that are motionless trusted to get in, specified as vendors, tools and partners. Then we checked which of them could peruse most of the origin buckets. One stood out: Qubole.

Being capable to attain data doesn't average person did. So for all external identity, we compared its action to its own history: whenever it was used, how often, from which IP addresses, and what it did. Every vendor looked normal apart from one.

A small squad divided the work, all using AI to move faster, and we held all finding to one rule: it was either proven or assumed. That regulation saved us additional than once. One first guide looked powerful on paper, but the action logs showed the character had never been used, so we dropped it.

Proving what was taken

Knowing the attacker could attain the data wasn't enough. Each affected business needed to cognize what was really taken, and that's harder to demonstrate than it sounds, since most haze retention doesn't document all document peruse by default. So we pieced the image together from three places.

The archetypal was encryption. Every period an encrypted document is downloaded, the haze has to unlock it, and all unlock is logged alongside the file's name. By matching those records to the attacker's sessions, we could see which records left. Files that lone use default encryption depart no specified trail, so the genuine figure is apt higher.

The second was the secrets themselves. Every password and key the attacker peruse remaining its own record, which gave an exact catalog of what to change.

The third was document admission logs. Where they were turned on, they recorded all document the attacker downloaded, alongside the IP and session. Across one company's accounts that came to millions of files, and on encrypted buckets the logs matched the encryption records exactly.

This is anywhere having an agentic Data Security Platform makes the difference. Sentra continuously discovers and classifies delicate data throughout cloud, SaaS, data warehouses, on-premises environments and AI systems. It additionally maps all employee, assistance document and AI delegate to the data all one can reach. After a breach, the clock is running: breach notification deadlines can be as abbreviated as 72 hours, and all hr spent operating out what was taken is an hr that can't be spent containing it. Logs can display which records were touched, but not what was inner them. Because Sentra had already discovered and classified our customers' data, we knew which locations held personal, financial and credential data before the inquiry equal began. That turned "what did we lose?" from a inquiry that can obtain weeks into one we answered the identical day.

Five actions, identical day

By the end of the day, we could provision all affected client five apparent actions, all backed by evidence:

1. Remove Qubole from all function that trusts it.
2. Rotate all concealed that was read. We shared all secret's name and anywhere to discover it in their logs, without always seeing the values themselves.
3. Block the attacker's IP addresses.
4. Preserve the logs before they expire.
5. Let Qubole know.

One vendor, many targets

What we saw wasn't distinctive to one company. The attacker held Qubole's credentials, and those credentials opened a entrance at all institution that motionless trusted Qubole. This was a broad run that moved through business following company, impacting dozens of Qubole customers.

Because Sentra can see which organizations rely the identical vendor, we checked all Sentra client alongside a Qubole association inside concerning an hour. The identical action showed up throughout client environments, a long-unused Qubole function abruptly waking up in the identical window, and we warned all affected client directly.

We afterward reported the run to Wiz, which led to a shared inquiry alongside Wiz and Mandiant. Their research shows the identical attacker sweeping through one casualty surroundings following another in quick succession.

Close the doors you forgot

Some vendors can attain far additional delicate data than others, and those are the ones an attacker wants. Compromise one of them, and everything it can admission goes alongside it. Start by finding them: which exterior identities can peruse your client records, financial data and credentials, and how much of it all one can reach.

Then shrink that admission alongside a zero rely approach: all vendor gets lone the data it needs for the job it does today, and nothing more. Remove rely entirely for integrations you no longer use, since a dormant, privileged, external function have to be removed, not watched. For the rest, cut permissions downward to what the vendor documents and bounds all function to the vendor's published network addresses. In this case, all concealed that leaked came from permissions Qubole never needed, and a network limitation solitary would have blocked all login we saw.

That's exactly what Sentra does. It shows which vendors, assistance accounts and AI agents can attain your most delicate data, flags admission that is excessive or unused, and helps you cut it rear before an attacker can use it. And since admission keeps changing, Sentra keeps watching, so a vendor that abruptly starts acting alternatively is caught early.

How Sentra turned a duration into a day

We didn't commencement from zero, and that's why the inquiry took a day alternatively of a month. Sentra had already established and classified our customers' delicate data, so we could trace the taken samples rear to their origin inside hours. It had additionally mapped who could attain that data, vendors included, and how much all one could do, which turned hundreds of imaginable suspects into a abbreviated list.

Sentra had flagged the hazard before item happened, too: a Qubole function that had sat unused for months but could motionless attain delicate data. And since Sentra knows how all character normally behaves, the attacker stood out the instant a quiescent vendor function started acting differently.

Knowing what the data was meant we could inform all client what benevolent of delicate data was exposed, not fair anywhere it lived. And since Sentra sees which organizations rely the identical vendor, one customer's event became an first alert for all another client at risk.

We've since turned this method into reusable inquiry skills, so the next case starts from a playbook fairly than a blank page.

Actionable controls to forestall and mitigate provision sequence breaches

• Continuous delicate data discovery: Automatically find and categorize delicate data throughout all cloud, SaaS, data warehouse, and AI environments.
• Identity-to-data mapping: Map all employee, vendor, assistance account, and AI delegate to the particular data resources they can access.
• Dormant admission cleanup: Flag and revoke unused, excessive, or forgotten third-party integrations before attackers can utilize them.
• Real-time anomaly finding (DDR): Monitor admission patterns to detect and vigilant on different third-party behavior instantly.
• Rapid event containment: Use pre-classified data environment to decide breach effect in hours alternatively of weeks.

For the complete specialized particulars and indicators of compromise, peruse Wiz's research.

Want to see which exterior identities can attain your delicate data today? Book a demo today.

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There's many to propose concerning podcasts (and lots of podcasts to recommend), but one of their biggest benefits—for me, anyway—has small to do alongside what a particular display is concerning or equal who hosts it: They're awesome for dissociating. I frequently have a podcast playing to distract my concerned brain from any I'm concerned concerning (which, in 2026, is normally slightly small than everything).

Any period I don't desire to be solitary alongside my thoughts (say, whenever commuting or doing mindless chores), I can put on a podcast and be entertained by a brace of goobers talking concerning movies fairly than spinning my wheels concerning my son's center academy applications. And there's no period I desire to disassociate from actuality additional than bedtime.

For years I've used podcasts as a non-pharmaceutical sleep aid, and few things are additional productive at assisting me autumn off quickly. Unfortunately, for years this additionally meant that formerly I cut asleep, the podcast kept playing. I have my podcast app set to delete episodes formerly finished, so if it was a display I cared about, I'd need to do a lot of redownloading and scrubbing through to discover anywhere I remaining off—that is, until I upgraded my earbuds to the AirPods Pro 3.

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The AirPods Pro 3 "Pause Media When Falling Asleep" environment is a game-changer

The AirPods Pro 3 launched alongside iOS 26 in the autumn of 2025 alongside flashy features akin vastly improved noise cancellation, Live Translation, Heart Rate Sensing, and hearing health settings, all of them fairly greeted on a set of $250 premium earbuds. But the finest new AirPods characteristic was one I didn't equal cognize concerning whenever I decided to upgrade my four-year-old Beats Fit Pro.

In the AirPods settings (accessible via your iPhone's Settings app), if you tap Audio & Routing and scroll down, you'll see an choice to rotate on a environment called Pause Media When Falling Asleep. I don't think I need to inform you what this environment does. I can inform you that it plant great.

According to Apple, the characteristic plant by sensing whenever you're drifting off using the earbuds' built-in accelerometer to track your movement (or deficiency thereof). Once your earbuds have resolute that you're falling asleep, they volition intermission your audio for you. And it isn't fair the 2025 AirPods Pro 3 that assistance it—it additionally plant on the second-generation AirPods Pro as fine as the AirPods 4 and the AirPods 5.

No need for dedicated sleep earbuds

I'll acknowledge that I don't really comprehend how the AirPods Pro 3 can inform I'm falling idle versus fair lying extremely still, but I've never had them intermission accidentally during energetic listening. Conversely, whenever I go rear to the podcast I was listening to the night before, I'll discover that the audio paused inside a infinitesimal or two of the final part I recall hearing.

The AirPods Pro 3 aren't the lone earbuds alongside a characteristic akin this, but you'll most frequently lone discover it in dedicated "sleep earbuds" designed specifically for the purpose. While many of those recommendation additional health features akin the capability to track your sleep cycles, I'm not really curious in that (my Apple Watch does most of it, and I don't appearance at that data anyway). Pausing my audio whenever I'm idle is really all I need, and the AirPods Pro 3 do it fine adequate that I don't need to annoy alongside a brace of dedicated bedtime buds.

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Has this always happened to you? You add person new to an iMessage collection chat, and wonder, "Huh, can they see any of the messages from before they were added?" The answer to that inquiry is supposed to be "no." By design, new iMessage collection conversation members are lone privy to texts that attain following they were added. When they join, it's basically a blank slate, as if you fair created the collection for the archetypal time. At least, that's how it's intended to work.

iMessage currently has two safety flaws

As it turns out, there's a bug alongside iMessage that may authorize new collection conversation members to see texts from before they were added to the chat. That's a problem, and not fair for the iPhone users out there talking smack concerning group before inviting them to associate the collection chat. It method iMessage, and Apple's platforms in general, aren't operating akin they're expected to be. You don't have to be talking rearward people's backs to not desire new members seeing existing chats. Maybe you're preparedness a astonishment gathering for someone, and add them to the collection formerly everyone's in on the plan; perchance the texts in the conversation aren't applicable to the current group, and the individual joining merely does not need to admission them.

That's not all: There exists another safety flaw that could authorize distant hackers to associate an iMessage collection and obtain new messages. You power add a new person to a collection conversation and inadvertently invitation them to peruse all existing messages, during at the identical period being attacked by a bad performer who can afterward obstruct all forthcoming messages. Not good.

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iOS 27.2.1 patches this bug and eight more

Luckily, there's currently a fix for these problems. Apple patched (and revealed) the bugs, alongside alongside seven another safety flaws, alongside a sequence of application updates on Monday for iPhone, iPad, Mac, Apple Watch, and Vision Pro. Not all of the updates contain patches for these two iMessage flaws—specifically, several of the updates for older Apple devices. But the rest, including iOS 27.2.1, macOS 27.2.1, macOS 26.7.3, macOS 15.8.3, watchOS 27.2.1, and visionOS 27.2.1, contain the iMessage patches.

Each update includes its own catalog of patches, but, for this article, I'll obtain a appearance at the nine patches in iOS 27.2.1. You'll discover the two iMessage bugs, affirmative seven another patches, in the catalog below:

• AirPlay: Impact: An attacker in Bluetooth range may be capable to bypass the charge bounds on pairing code attempts. Description: A logic matter was addressed alongside improved province management. CVE-2026-113968: Apple

• App Intents: Impact: An app may be capable to admission delicate person data. Description: An authority matter was addressed alongside improved province management. CVE-2026-113963: László Várady

• Bluetooth: Impact: An attacker in Bluetooth range may be capable to corrupt procedure memory. Description: A use-after-free matter was addressed alongside improved recollection management. CVE-2026-113970: Apple

• brotli: Impact: Processing maliciously crafted input may guide to recollection corruption. Description: An out-of-bounds compose matter was addressed alongside improved input validation. CVE-2026-111497

• CoreUtils: Impact: A malicious Bluetooth equipment may be capable to established a pairing without person interaction. Description: This matter was addressed alongside improved checks. CVE-2026-113965: Apple

• CoreUtils: Impact: An attacker in Bluetooth range may be capable to bypass the charge bounds on pairing code attempts and brace alongside a equipment without person interaction. Description: A logic matter was addressed alongside improved province management. CVE-2026-113964: an anonymous researcher

• Messages: Impact: A distant attacker may be capable to associate an iMessage collection conversation and obtain its messages. Description: An authority matter was addressed alongside improved validation. CVE-2026-113961: an anonymous researcher

• Messages: Impact: A person added to an iMessage collection may be capable to admission messages sent before they joined. Description: This matter was addressed by removing the susceptible code. CVE-2026-113960: an anonymous researcher

• Rapportd: Impact: An attacker on the identical network may be capable to bypass the charge bounds on AirPlay pairing code attempts. Description: A logic matter was addressed alongside improved province management. CVE-2026-113969: Apple

For a complete accounting of the patches included in the another safety updates, inspect out Apple's authoritative safety releases site.

How to place these iMessage flaws

To instal Apple's latest safety updates, you'll merely need to update your equipment as you normally do. On iPhone, iPad, Apple Watch, and Vision Pro, caput to Settings > General > Software Update; on Mac, go to System Settings > General > Software Update. Here, prosecute the on-screen instructions to download and instal the latest type of your device's software.

If you've been patiently waiting for the correct instant to drag the trigger on the Nintendo Switch 2 following the latest publish of The Legend of Zelda: Ocarina of Time, this power be a fine period to do so. Woot is marketing a Nintendo Switch 2 for $469 (originally $499). The discount comes as the archetypal important discount since the Sept. 1 cost hike.

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The new console is an upgrade in all way, as you can peruse in our full assessment of the Nintendo Switch 2. The ergonomics and scheme have improved, making it much improved to clasp and appearance at. The power division existence has additionally improved, now alongside concerning 180 minutes of handheld playtime before the beverage runs out.

A big affirmative is that the Switch 2 is backwards-compatible: You can perform your old Switch games on it. Some Switch games have the capability to upgrade to the Switch 2 Edition by purchasing that game's upgrade pack. This is particularly value it for games like The Legend of Zelda: Tears of the Kingdom, which volition appearance improved on the Switch 2.

There are a lot additional games out for the Switch 2 now than there were at the commencement of the year. Nintendo has hinted that they're prioritizing making additional traditional remakes, alongside many additional on the pipeline, so there are plentifulness of games to keep you occupied for a while, akin Donkey Kong Bananza, Kirby Air Riders, and Hyrule Warriors: Age of Imprisonment, which are already out. If you already own a Switch 2, check out our Top 10 Hacks to get the most out of your device.

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We’ve upgraded Claude Fable and Claude Mythos—models that are designed to tackle the most daunting specialized challenges.

Claude Fable 5.2 and Claude Mythos 5.2 display improvements in areas akin coding, reasoning, and vision. During prerelease evaluation they’ve helped our engineers complete otherwise-infeasible application migrations, designed three operating digital devices from scratch, solved two open problems in the discipline of dreary holes, made significant advancement on robotics evaluations, and formalized the evidence of Fermat’s Last Theorem in 30 hours—among many another applications we’ll conversation below.

Claude Fable and Claude Mythos are the identical underlying model. Fable has powerful cybersecurity and existence discipline safeguards to defender against misuse, which allows us to create it accessible to all users. Organizations following beneficial applications in cybersecurity or biomedical investigation can use for admission to Mythos through our lately expanded Life Sciences and Cyber Verification Programs.

Testers have established that Fable 5.2 and Mythos 5.2 communicate additional plainly than former Fable and Mythos models. They create outputs up to 30% faster than Claude Fable 5.1 and Claude Mythos 5.1, and they’re accessible at a lesser price: $8 per myriad input tokens and $40 per myriad output tokens.

Fable 5.2 and Mythos 5.2 additionally arrive alongside an optional accelerated mode, which speeds up token generation by up to 2.5 times. Fast manner is accessible for $16 per myriad input tokens and $80 per myriad output tokens.

Safety and alignment. To assistance justify safety and alignment keep stage alongside expanding example capabilities, we ran extended prerelease safety evaluation on the example that underlies Fable 5.2 and Mythos 5.2. This included evolving novel evaluations that provision deeper understanding into particular concerning behaviors akin sandbox escapes. We accept high-stakes risks from misalignment remain low, and the example performed as fine as, or improved than, our latest models on most alignment measures. Fable 5.2’s cyber and existence discipline safeguards are as sturdy as those of Fable 5.1 but create small unnecessary blocks, and we have implemented our strongest-ever protections against distillation attacks.

We depict additional concerning our safety evaluation below, and complete particulars can be established in the Claude Fable 5.2 and Mythos 5.2 System Card.

When to use Claude Fable 5.2 and Mythos 5.2. Fable and Mythos are finest for the most ambitious, open-ended specialized work. For most coding and cognition tasks, we propose that users commencement alongside Claude Opus 5.5, our strongest regular driver, and afterward toggle to a distinct example as required, depending on the range and complexity of their work—upward to Fable or Mythos, or downward to Claude Sonnet 5.5 or Claude Haiku 5.5. Fable 5.2 can additionally perform the function of Advisor (where Haiku, Sonnet, or Opus models consult it to assistance resolve harder problems) or Orchestrator (where it delegates simpler tasks to smaller models).

Evaluating Claude Fable 5.2 and Mythos 5.2

Benchmark performance. The benchmarks array below shows that Claude Fable 5.2 and Mythos 5.2 are a stage complete Opus 5.5 in nearly all the capabilities we evaluated:

Effort and efficiency. On Medium attempt (the default in Claude apps and Claude Code), Fable 5.2 can accomplish challenging tasks alongside small tokens than Fable 5.1. In first evaluation on the FrontierCode 2.0 evaluation, Fable 5.2 at High attempt scored concerning flat alongside Opus 5.5 at Max effort, for approximately fractional the disbursal per task. At Xhigh effort, it additionally scored the highest of any released example tested.

Coding

Over 90% of the changes merged into Anthropic’s center codebase are now written by Claude (with individual guidance). We’ve used Fable 5.2 to fix stubborn bugs in minutes, optimize achievement throughout all of our services, and complete high-value application projects that would alternatively have been too dilatory or too costly to attempt. A squad of agents operating an before type of Fable 5.2 migrated a crucial manufacturing assistance from Python to Rust—the assistance is now additional efficient, additional reliable, and starts concerning 26 times faster. We’ve established that Fable 5.2 exercises powerful engineering judgement and can create high-quality results from equal ambiguous requests.

As an experiment, one Anthropic engineer gave Fable 5.2 the project of designing a prototype functioning system. Starting from an bare folder, the example coordinated complete 1,000 subagents, operating about the clock for eight days writing, testing, and reviewing code. It produced a operating functioning scheme that boots on a laptop, alongside its own center that runs the machine, drivers for the disk, keyboard, and network, imperishable document storage, windows, a login screen, an installer, and built-in Claude functionality (see the video below).

Early testers have been impressed at how successfully Fable 5.2’s coding skills can be applied to a broad range of tasks:

Cybersecurity

Claude Mythos 5.2 is our most capable example for cybersecurity engineering. It catches additional vulnerabilities than our former models, and is improved at reproducing and fixing them (note that Claude Fable 5.2’s safeguards volition obstacle most cybersecurity-related tasks). A safety business established that one in six confirmed bugs established by Claude Mythos 5.2 were missed by all of the another frontier models they tested. On our inner evaluation, CVE Rediscovery,¹ Claude Mythos 5.2 established 10.2% additional vulnerabilities than Claude Opus 5.5 and 14.4% additional than Claude Mythos 5.1. Claude Mythos 5.2 additionally shows improved judgement and calibration—one tester established that former models had overstated the seriousness of concerning 1 in 20 findings, whereas Claude Mythos 5.2 hadn’t overstated any.²

Claude Mythos 5.2’s cyber capabilities are accessible through our Cyber Verification Program, which we’re continuing to develop and improve. Claude Enterprise customers (even if they aren’t members of the Cyber Verification Program) can admission Claude Security, which scans codebases for vulnerabilities using our most capable models—which now includes Claude Mythos 5.2. A latest update to Claude Security helps it observe repositories as code is updated, track any findings to justify they’re fixed, study from additional context, and nexus to a user’s tools through an API and MCP.

Science and engineering

Important tasks in science, engineering, and math increasingly depend on coding: analyzing data, modeling molecules, designing parts, and controlling instruments are all done through software. Below, we conversation how Claude Fable 5.2 and Claude Mythos 5.2 have helped researchers create advancement on difficult problems in these areas.

Theoretical physics

Claude Fable 5.2 solved two open problems in theoretical physics. Both involvement dreary holes, and the attempt to merge the two linchpin theories of contemporary physics, relativity, and quantum mechanics.

Both of Claude’s results depend on holographic duality, the idea that certain universes containing gravity can be exactly described by a quantum theory containing no gravity at all. In the archetypal problem, physicists in 1983 had shown that as these universes warmth up, dreary holes form abruptly, akin h2o boiling; but their method worked lone in ample universes that were big adequate for relativity to apply. A calculation to decide whether the identical was true in extremely small universes, anywhere lone the quantum clarification can be used, was laid out in 2003 but was too laborious to complete. Fable 5.2, operating mostly autonomously, completed it and established the passage is abrupt there, too. In the second problem, Fable 5.2 established the exact bounds of a method physicists have used for additional than 15 years to regulation out applicant theories of quantum gravity in a simplified universe. Read additional concerning these results in their corresponding preprints, here and here.

Protein folding

Adeno-associated viruses are small, harmless viruses whose macromolecule shell, the capsid, can be used to provision cistron therapies. The macromolecule construction of the capsid is important—for example, it affects whether the therapy volition be blocked by a patient’s immune system—but it’s additionally extremely complex. Existing protein-folding models akin AlphaFold can foretell idiosyncratic proteins, but a capsid is built from dozens of them. Building on two open-source protein-folding models, Claude Mythos 5.2 established a novel way to accurately foretell a entire capsid’s structure. It wrote code that ran these models on overlapping groups of macromolecule chains, and used the data on the known symmetries of the capsid to merge them together.

Claude’s method worked throughout multiple capsids, the two natural and engineered (in our tests, it accurately modeled 28 of the 34 it attempted).³ Three computational biologists shared the following reflections on how Claude’s skills could be used to speed up their investigation into cistron therapies:

Mathematics

Mathematical formalization. Formalization is the procedure of translating a mathematical evidence into a tongue that a device can inspect stage by step. It can assistance individual mathematicians acquire confidence in results without having to inspect them by hand (this matters equal additional whenever AI models create results that are difficult for humans to verify). Because formalization is specified a laborious process, if AI could assistance alongside it, mathematicians could expend additional period exploring novel ideas.

Our archetypal formalization of Fermat’s Last Theorem, performed by an unreleased investigation type of Claude, took 11 days. It relied on a purpose-built harness alongside alongside direction from a individual researcher. By contrast, a squad of 1,000 Fable 5.2 agents completely formalized the theorem in fair 30 hours, alongside minimal individual intervention. Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, lone a catalog of intermediate theorem statements to mark progress. It needed one-third as many act tokens as our archetypal formalization and produced a evidence seven times shorter. As can be seen in the fig below, Opus 5.5 (with the identical 1,000-agent setup) completed lone 26% of the formalization in 18 hours, and barely made any additional advancement following 30.

Read additional concerning the formalization here.

Hardware and robotics

Many of AI’s possible benefits—for example, in speeding up lab research—depend on it being capable to act in the bodily world. Claude Fable 5.2 continues our models’ trend of increasing usefulness in the real world, showing promising results on tasks specified as designing hardware devices and significant advances in controlling bodily robots.

Hardware engineering. We tested Fable 5.2’s skills in hardware engineering, asking it in a one-paragraph immediate to scheme and create three operating digital devices alongside their own circuit boards, 3D-printed enclosures, and software. The devices, shown below, were a residence voice-assistant hub, a USB speakerphone, and a handheld virtual pet. We collaborated on this project alongside Diode, a business that designs and manufactures circuit boards.

Claude wrote its own specifications, selected accessible digital components from US distributors, routed all device’s four-layer circuit board, modeled the enclosures alongside computer-aided design, and programmed the devices’ software. Each commission was prepared for manufacturing in under a day, alongside minimal individual input. Once Diode had built the boards, Claude guided the company’s engineers through the evaluation and gathering of the devices. All three devices worked without needing any electric modifications. Read additional concerning Claude’s hardware engineering skills here.

Robotics. Compared alongside before Claude models, Fable 5.2 shows substantially improved skills in controlling bodily robots—even for robotics tasks on which it has never explicitly been trained. The video below shows robots controlled by Fable 5.2 completing a assortment of tasks, including solving a puzzle cube, unscrewing a flask cap, and example a image of a robot. On the community RoboDojo benchmark, which tests how AIs can authority a simulated two-arm robot from camera images, Fable 5.2 outperformed another frontier models that were run on the identical harness, as fine as most AI systems built specifically to authority robots.

Research and analysis

Early testers using Claude Fable 5.2 for investigation and inspection tasks established that it can grip additional ambiguous, data-heavy tasks than former models and has improved judgement on ambiguous tasks—particularly those that necessitate theoretical reasoning and design. Proprietary selling resolute Jump Trading tested Fable 5.2 on proxy evaluations that resemble several of its quantitative investigation tasks, embedding it in a harness akin to the one the resolute uses for its own research. In one run, Fable 5.2 efficiently played all the necessary roles: code-writer, coordinator, planner, and job-submitter. Previous models had required significant investments of period to manually rebalance roles to modify distinct models’ strengths and weaknesses.

Other testers from investigation and inspection organizations sent us the following statements on their experiences alongside Fable 5.2:

Safety and alignment

No sole test can inform us how a extremely capable example volition behave formerly it’s deployed, so we depend on many distinct tests and many distinct types of evidence before release. For the example underlying Claude Fable 5.2 and Claude Mythos 5.2, our predeployment safety activity included alignment training and testing, newly designed evaluations on the model’s propensity to obtain unhelpful actions, construction stronger safeguards against misuse, and scaling up our behavioral monitoring. The example was additionally tested before publish by external evaluators, including the UK AI Security Institute, Frontier Design, and METR. See the Claude Fable 5.2 and Mythos 5.2 System Card for a summary of their reports⁴ and for a complete clarification of our own safety and capabilities testing.

Alignment testing

We ran a suite of evaluations for how well-aligned Claude Fable 5.2 and Claude Mythos 5.2 are in behavioral terms, evaluation how frequently they behave in unintended and possibly unsafe ways. Weighing everything we’ve seen throughout a range of evaluations, we’ve assessed the hazard of these models behaving in unsafe ways in high-stakes situations to be low (as alongside our another latest models).

We tested Claude Mythos 5.2 (the identical underlying example as Fable 5.2 without certain cybersecurity or existence discipline safeguards), on our automated behavioral audit, which tests how a example behaves throughout about 1,900 scenarios. Claude Mythos 5.2 performed as fine as, or improved than, Opus 5.5, Sonnet 5.5, and Claude Mythos 5.1 on most measures. For example, it scored improved than all three on most measures that affect misleading users. Hallucinating inputs, falsely claiming to have completed a task, omitting crucial information, and failing to disclose bad or lazy activity were all lesser in Claude Mythos 5.2.⁵

We paid particular notice during our evaluation to behaviors that contributed to latest notable cybersecurity and alignment incidents. For example, we established that Claude Mythos 5.2 has a lesser propensity than Opus 5.5, Sonnet 5.5, and Mythos 5.1 to affect in biased or motivated reasoning (such reasoning power average it “explains away” evidence that it isn’t in a simulation and thus takes actions without a realistic understanding of the consequences).

We ran multiple distinct evaluations to test the model’s inclination to attempt escaping its sandbox. In two evaluations we’d used previously, Claude Mythos 5.2 made substantially small attempts at sandbox escape than any another example we’ve tested. In a dedicated evaluation—an adversarial test including complete 1,800 scenarios, most of which could not be solved without escaping a sandbox, and anywhere the sandbox has a deliberate weakness—Claude Mythos 5.2 made use of credentials or records that we had placed exterior its sandbox in 3.1% of scenarios, concerning as frequently as Opus 5.5 (3.3%), small frequently than Sonnet 5.5 (5.1%), and fine below former iterations of Claude Mythos (5 and 5.1, which scored 39% and 21%, respectively). We established that another techniques, specified as describing the sandbox and adding tools to study weaknesses, reduced Claude Mythos 5.2’s escape attempts equal further. On another measures—one, for example, involving taking harmful actions following erroneously concluding it was in a simulation—Claude Mythos 5.2 scored slightly worse than Opus 5.5, and concerning flat alongside Claude Mythos 5.1.

The issue of construction evaluations that reliably capture all nonaccomplishment before a example is deployed remains unsolved. The complexities of evaluating extremely capable models frequently muddy the picture—for example, Claude Mythos 5.2 and Fable 5.2 all sound suspicion that they are being evaluated additional frequently than any of our latest models, which challenges our capability to measure how they volition act in real-world settings. As these settings develop and capabilities addition alongside forthcoming models, we anticipate this difficulty to grow. That is why we do not depend on these evaluations solitary to provision a holistic image of safety: we diagram on monitoring during training, reviews of transcripts from inner use, evaluation by external evaluators, and interpretability techniques for examining what is happening inner the model. We are additionally operating to create our evaluations additional realistic and to improved comprehend what they miss.

Safeguards

Cybersecurity and existence discipline safeguards. Consistent alongside former Claude Fable models, Fable 5.2 comes alongside safety classifiers that obstacle high-risk cyber activities, including intrusion testing, utilize generation, and binary-based vulnerability, as fine as most tasks in investigation biology. When a query is blocked, users volition alternatively obtain a reply from Opus 4.8 for cybersecurity and Opus 5 for biology. Since the publish of Claude Fable 5.1, we have continued to decrease false positives for our safety classifiers. Based on first evaluation throughout claude.ai, Claude Code, and Cowork, we observed concerning 55% small blocks alongside Fable 5.2 compared alongside Fable 5.1—about 60% small in cybersecurity and 40% small in biology. Claude Mythos 5.2 is accessible to vetted organizations that activity in these dual-use areas.

AI investigation and development. The example that underlies Claude Fable 5.2 and Claude Mythos 5.2 is a stage onward in broad capabilities. However, during the model’s training, we took steps to bounds advances in the particular investigation capabilities that could most immediately accelerate AI development. Thus, in applicable areas, including accelerator kernel optimization and improving ample tongue example architectures, Fable 5.2 and Claude Mythos 5.2 execute at concerning the flat of Opus 5.5. Importantly, we ensured that the training changes did not power connected capabilities akin agentic coding, hardware development, and traditional AI or device learning investigation (including interpretability research). We do not alter these capabilities as the example is being served to users (that is, at conclusion time). As described in Section 1.5 of the System Card and likewise to former models, Fable 5.2 and Claude Mythos 5.2 additionally contain transparent fallbacks to Opus 5 for a small set of topics in this domain, including kernel betterment on certain nonstandard accelerators.

Anti-distillation. Distillation, anywhere a example is trained on the outputs of a additional advanced one to extract the advanced model’s capabilities, occurs illicitly on an manufacturing scale, frequently involving thousands of counterfeit Claude accounts. Since distillation allows for advanced capabilities to be released without the safeguards we’ve built into Claude, it creates safety and national safety risks (see our September 2026 danger intellect report for particulars on illicit distillation action we’ve detected and disrupted so far). Fable 5.2 has the strongest protections against distillation of any example we’ve released; it uses the techniques we described in a recent report, which have reduced the data harvested by distillers by 90% to 99%.

We are additionally expanding the rollout of an API characteristic that helps defender against illicit distillation: preserved thinking now applies to all accounts. Preserved thinking prevents distillers from editing Claude’s previous environment in an attempt to extract its reasoning (developers can mention to our preserved thinking docs to study how to update integrations alongside new API features).

Data privacy and Enterprise Frontier Safeguards

Some additional complex forms of AI example misuse can be detected lone whenever observing patterns throughout multiple requests. For this reason, mighty models akin Claude Fable 5.2 and Claude Mythos 5.2 rely on safeguards that detect a opening of use data (as opposed to idiosyncratic responses). With Enterprise Frontier Safeguards, automated safety monitoring is run on prompts and responses that are stored in a customer’s infrastructure, making it compatible alongside zero data preservation (ZDR). Enterprise Frontier Safeguards is now in personal beta alongside customers akin Bridgewater, Salesforce, and Stripe.

Enterprise customers can petition admission to Enterprise Frontier Safeguards by contacting their document squad or by completing this form. Eligible customers can continue using Claude Fable 5.2 and Claude Mythos 5.2 alongside zero data preservation before implementing safety monitoring alongside EFS. For another customers, we have 30-day preservation for safety monitoring.

Availability

Claude Fable 5.2 is accessible today on all platforms, including Amazon Web Services, Google Cloud, and Microsoft Azure. Developers can get started alongside claude-fable-5-2 on the Claude Platform.

This week, we’re giving subscribers on eligible paid plans additional Fable usage. Plans and seats that contain Fable models get one to three Fable resets according to how lengthy they’ve subscribed, and Fable is included for up to 50% of weekly use limits for Pro plans and norm seats on the Team plan. Learn additional concerning the Fable 5.2 initiate promotion here.

Cybersecurity professionals can use for admission to Claude Mythos 5.2 through the Cyber Verification Program. Claude Enterprise customers can additionally use Claude Security, powered by our complete family of models, including Claude Mythos 5.2. Life sciences researchers can additionally apply for admission through the Life Sciences Verification Program.

Fast manner for Claude Fable 5.2 and Claude Mythos 5.2 is accessible on Claude Code, the Claude Platform, Claude Models on Google Cloud (Anthropic managed and Google managed), and Microsoft Foundry (Anthropic hosted). Note that admission to Claude Mythos accelerated manner is accessible lone to organizations who are part of our Verification Programs.

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An command of dimension small epochs

Across a large range of scales and datasets, e-means reaches a test defeat below Lloyd's at 25 epochs, in 1.2 to 3 epochs. Measured end to end, that is a 10–45× wall-clock decrease on GPU against the state-of-the-art Lloyd implementation (Flash-KMeans^[2]), and 8–15× on CPU against Faiss^[1]. Speed and norm normally commerce against all another in k-means, during e-means achieves the two improved speed and loss.

The k-means problem

Given a fixed dataset of n points X = {x₁, ..., x_n} ⊂ R^d and an entire figure k, the k-means issue is to discover k centroids C = (c₁, ..., c_k) minimizing the average squared extend between all item and its nearest centroid:

\[ \mathrm{MSE}(X, C) = \frac{1}{n} \sum_{i=1}^{n} \|x_i - c_{a(i)}\|^2, \qquad a(i) = \arg\min_{j \in \{1,\dots,k\}} \|x_i - c_j\|^2 \]

It's a fixed dataset, dissimilar codebook learning inner VQ-VAEs, anywhere the allocation to collection is continually moving.

The e-means project

The goal of e-means is to provision a contemporary optimization regulation for the k-means problem. We started this project from the following observations:

• k-means is among the rare problems in device learning anywhere full-batch optimization is motionless extensively used.
• Mini-batch k-means methods in the writings exist^[3],[4],[5], but are mostly according to the MacQueen update rule, released in 1967.
• Modern profound learning offers optimization tools that can enhance mini-batch methods.

For e-means, we chose to do mini-batch updates alongside exponential moving averages^[6], which we established to be most productive for the k-means problem. Contrary to former mini-batch approaches, e-means requires no hyperparameter tuning since it automatically sets the lot size and stage size alongside scaling laws (see Automatic hyperparameter tuning alongside scaling laws). In addition, e-means improves the initialization and the dead-cluster revival strategy, adds a cosine-scheduled decay rate, and introduces a finalization phase that reduces the training defeat for a negligible runtime penalty at the end of training. e-means is extremely parallelizable, and comes alongside specialized kernels that translate its algorithmic advantage into wall-clock speedups.

The centroid update rules

The centroid update regulation is the chief component of e-means. This division aims to provision an overview of update rules for k-means clustering. All methods maintain, for all collection j, a sum S_j and a figure N_j of the points assigned to it, and obtain the centroid as their ratio:

\[ c_j = S_j / N_j \]

They differ lone in how S_j and N_j are updated. Below, s_j and n_j signify the sum and the figure of the points in the current lot that are assigned to collection j (both zero if none are).

Lloyd’s algorithm^[7] assigns all item to its nearest centroid and recomputes the data from scratch, using the complete dataset as the batch:

\[ S_j \leftarrow s_j, \qquad\quad N_j \leftarrow n_j \]

so c_j is the exact average of the points currently assigned to it. Each update expenses one complete continue complete the data, and the procedure is repeated a fixed figure of times (typically 20 to 50 iterations).

MacQueen’s update^[3] accumulates the data without always discarding them:

\[ S_j \leftarrow S_j + s_j, \qquad\quad N_j \leftarrow N_j + n_j \]

so c_j is the uniform average of all item assigned to collection j since the commencement of training, including points assigned under earlier, perchance poor, centroid positions. This was afterward partially addressed by Staleness-Reduction Mini-Batch K-Means^[8], a hybrid method between k-means and MacQueen. They obtained crucial speedups, but lone on extremely low numbers of clusters.

e-means plant on mini-batches and replaces this sum alongside an exponential moving average:

\[ S_j \leftarrow (1-\alpha)\,S_j + \alpha\, s_j, \qquad\quad N_j \leftarrow (1-\alpha)\,N_j + \alpha\, n_j \]

so older assignments are forgotten at charge α, and the centroid tracks latest assignments fairly than the entire history. This update regulation is typically used for moving distributions, hence their acceptance for training VQ-VAEs^[6]. What we established is that it’s additionally extremely productive for fixed datasets if it’s blended alongside e-means’ another components, and hyperparameters are correctly tuned.

The e-means project started from former activity at Meta FAIR by Hugo Touvron and Hervé Jégou, who had established that mini-batch k-means by gradient decline could attack Lloyd’s algorithm. While I could reproduce these results, I established that the achievement of gradient-descent based centroid updates is unstable at ample learning rates, dissimilar exponential moving average updates. I doubtful that is since EMA updates justify that the centroid is continually in the convex hull of points assigned to it, during gradient decline updates may overshoot former that hull.

In command to accomplish the lowest defeat at a fixed figure of epochs, we chose exponential moving average updates, hence the name e-means for our method.

If you set the lot to the complete dataset, B = n, and α = 1, afterward N_j and S_j rotate into exactly the figure and sum of the entire cluster, so one e-means repeat becomes exactly one Lloyd iteration. Lloyd is an extreme case of e-means without cosine scheduling on the learning rate. However, our hyperparameter hunt established that this extreme environment is extremely suboptimal, which explains why e-means mostly outperforms Lloyd. Below, the defeat is measured for all lot size B and stage α: Lloyd lands in a corner, fine exterior the area anywhere the defeat is lowest.

This hyperparameter study motivated us to get scaling laws that automatically choice the optimal lot size B and stage size α.

e-means improved escapes local minima

To improved comprehend why e-means reaches improved losses than Lloyd at convergence, we plotted a few trainings on toy datasets to measure their capability to escape local minima. Repeating trainings complete 200 seeds, we established that e-means is much small apt to autumn in pathological configurations compared to Lloyd.

Our clarification for that is that mini-batches provision additional noisy updates that are additional apt to examine the optimization landscape and escape local minima. Lloyd, alongside its continually decreasing loss, is additional apt to utilize established local minima and never escape them.

Automatic hyperparameter tuning alongside scaling laws

An essential asset of e-means is that practitioners don't need to set any additional hyperparameter compared to Lloyd's algorithm. We fitted two scaling laws to set the lot size B and stage size α₀ from any problem's characteristics (n, k, T), and established them to display powerful generalization on unseen datasets and issue scales.

\[ B = c_B \, k^{b_k} \, n^{b_n} \, T^{b_T} \]

\[ \alpha_0 = \min\!\Big(c_\alpha \, k^{a_k} \, B^{a_B} \, n^{a_n} \, T^{a_T},\; 1\Big) \]

We propose two equations to choose the batch, the default law and the norm law. The “quality law” trades off throughput for improved clustering norm against the “default” law. Below we storyline the training data for our scaling laws: validation MSE against stage size α₀ (left) and lot size B (right).

The importance of dead-cluster revival

The chief matter alongside mini-batch k-means is that it is prone to “dead” clusters, centroids that end up alongside zero assigned points during optimization. This reduces the productive size of the codebook, which is particularly problematic for the ample codebooks increasingly used today.

A average mitigation is to reinitialize clusters that autumn below a certain size threshold, as done in EnCodec^[10], anywhere deceased centroids are merely replaced by random samples drawn from the incoming batch. While this helps, it does not completely determine the issue. Instead of sampling arbitrary points from the batch, we revive deceased clusters by splitting large, existing ones: we example an energetic collection alongside probability proportional to its size and divided it into two. We established this approach, adapted from Faiss k-means, to be considerably additional productive at eliminating deceased clusters and preserving codebook capacity.

Extensive experimental validation

We validated e-means on 24 distinct embedding and synthetic datasets and configurations (see paper). For the video we ran 234 configurations of e-means against k-means (16 datasets, 100k to 100M points): examine all curve in the results map.

Library and code

from faiss.contrib.e_means import Emeans # NumpyEmeans for NumPy em = Emeans(d, k, nepochs=T) # T epochs (5 by default) centroids, labels = em.train(x) # x: a PyTorch tensor (n, d) # labels: all vector's collection in the final epoch

Repo: faiss/contrib/e_means.

References

1. M. Douze, A. Guzhva, C. Deng, J. Johnson, G. Szilvasy, P.-E. Mazaré, M. Lomeli, L. Hosseini, H. Jégou. "The Faiss library." arXiv:2401.08281, 2024. ↑
2. S. Yang, H. Xi, Y. Zhao, M. Li, X. Fan, J. Zhang, H. Cai, Y. Lin, X. Li, K. Keutzer, S. Han, C. Xu, I. Stoica. "Flash-KMeans: Fast and Memory-Efficient Exact K-Means." arXiv:2603.09229, 2026. ↑
3. J. MacQueen. "Some Methods for Classification and Analysis of Multivariate Observations." Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1:281–297, 1967. ↑
4. D. Sculley. "Web-Scale K-Means Clustering." WWW, 2010. ↑
5. J. Newling, F. Fleuret. "Nested Mini-Batch K-Means." NeurIPS, 2016. ↑
6. A. van den Oord, O. Vinyals, K. Kavukcuoglu. "Neural Discrete Representation Learning." NeurIPS, 2017. ↑
7. S. Lloyd. "Least squares quantization in PCM." IEEE Transactions on Information Theory, 28(2):129–137, 1982 (circulated as a Bell Labs specialized note since 1957). ↑
8. X. Zhu, J. Sun, Z. He, J. Jiang, Z. Wang. "Staleness-Reduction Mini-Batch K-Means." IEEE Transactions on Neural Networks and Learning Systems, 35(10):14424–14436, 2024. ↑
9. E. Jääsaari, V. Hyvönen, M. Ceccarello, T. Roos, M. Aumüller. "VIBE: Vector Index Benchmark for Embeddings." arXiv:2505.17810, 2025. ↑
10. A. Défossez, J. Copet, G. Synnaeve, Y. Adi. "High Fidelity Neural Audio Compression." Transactions on Machine Learning Research, 2023. arXiv:2210.13438. ↑

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