FEATURE When Instructure “reached an agreement” with data theft and extortion crew ShinyHunters this week, the education tech giant assured Canvas users after attackers claimed to have stolen data tied to 275 million students, teachers, and staff that their private chats and email addresses would not turn up on a dark-web marketplace, and that they would not be extorted over the incident.

“We received digital confirmation of data destruction (shred logs),” Instructure assured the nearly 9,000 affected universities and K-12 schools. “We have been informed that no Instructure customers will be extorted as a result of this incident, publicly or otherwise.”

Not a single responder that The Register spoke with believes this is true.

“Do I believe they deleted the data? No. They’re criminals and scumbags,” Recorded Future threat intelligence analyst Allan Liska, aka the Ransomware Sommelier, told us. 

“But, this is part of what Max Smeets calls ‘The Ransomware Trust Paradox,’” he added. “Ransomware groups have to, minimally, not post data they claimed to have deleted or no one will pay them in the future, but this is done knowing that the data is likely not deleted.”

Halcyon Ransomware Research Center SVP Cynthia Kaiser, who previously spent two decades at the FBI, said she doesn’t think that anyone who studies ransomware groups’ operations believes the gang actually destroyed the stolen files.

“‘We destroyed the data’ is a standard line from extortion groups once a payment is made or negotiations conclude, but time after time it has proven untrue,” Kaiser told The Register. “ShinyHunters in particular has a documented history of recycling, reselling, and re-leveraging stolen data across campaigns –  data they claimed was contained from earlier intrusions has resurfaced on criminal forums months and years later.”

Kaiser also doesn’t think this is the last threat that the schools will face from the Canvas breach.

“Halcyon expects targeted phishing waves against staff, students, and parents over the next six to 12 months using leaked names, email addresses, and Canvas chat context to make the lures convincing,” she said.

To be clear: Instructure execs never directly said the company paid the ransom, and we don’t know the exact amount of money the criminals demanded from the digital learning biz. 

We do know, however, that “reached an agreement” is corporate-speak for the victim paid up. Doug Thompson, chief education architect at cybersecurity firm Tanium, estimates the figure sits somewhere between $5 million and $30 million. 

Meanwhile, this latest extortion attack illustrates the impossible choice facing organizations entrusted with protecting people’s data when digital thieves breach their networks and steal sensitive information.

“The FBI says don’t pay,” Thompson told The Register. “But the operational reality at 3 a.m. during finals week or enrollment season can push institutions toward a very different calculation. Until that incentive structure changes, education is likely to remain unusually vulnerable to extortion pressure.”

To pay, or not to pay?

The US federal government, law enforcement agencies, and private-sector threat intelligence analysts all advise victims not to pay a ransom.

“Paying ransoms rewards and incentivizes the criminals, funding their search for new victims, and I’ve long advocated before for a ban on ransomware payments,” Emsisoft threat analyst Luke Connolly told us. “But in the absence of regulation applying to all organizations, the stark reality is that Instructure faced a crisis, and they negotiated to try to minimize risk and harm.”

No company wants to pay a ransom to its attackers, and most say they won’t – at least in principle  –  because they don’t want to fund criminal operations and incentivize the crooks. 

There’s also no guarantee that paying will guarantee the return of their data or prevent additional extortion attempts. CrowdStrike surveyed 1,100 global security leaders last summer, and of the 78 percent who said they experienced a ransomware attack in the past year, 83 percent of those that paid ransoms were attacked again. Plus 93 percent lost data regardless of payment.

While data suggests that fewer organizations are paying criminals’ ransom demands – Chainalysis found the percentage of paying victims in 2025 dropped to an all-time low of 28 percent, despite attacks hitting record highs – when faced with extortion or a ransomware infection, the “to pay or not to pay” debate becomes much more complicated.

“Most organizations still say publicly that they won’t pay, and many genuinely don’t, but when the alternative is mass downstream harm to students, parents, and thousands of customer institutions, the calculus shifts,” Kaiser said. “Pay-or-leak groups like ShinyHunters specifically engineer that calculus by creating intense financial and reputational pressure, and when demands go unmet, they escalate to direct harassment of victim companies, employees, and clients.”

ShinyHunters did just that. The crew initially compromised Instructure in late April, and after the initial pay-or-leak deadline passed on May 6, ShinyHunters switched tactics to school-by-school extortion. They injected a ransom message into about 330 Canvas school login portals, causing Instructure to take the platform offline for a day – during final exams and Advanced Placement testing for many.

Other ransomware scum have gone to horrifying extremes, posting pictures and addresses of preschool children in an effort to get a payday, leaking cancer patients’ nude photos and threatening them with swatting attacks.

Mandiant Consulting CTO Charles Carmakal previously told The Register that ransomware infections have morphed into “psychological attacks” with crooks SIM swapping executives’ kids to pressure their parents into paying.

Calculating risk

In addition to responding to criminals directly harassing their students, patients, customers and employees, victim organizations also have to take into account potential lawsuits if the crooks dump individuals’ personal or health data, and the reputational hit from seeing all of this protected information published online.

The decision about what to do in a ransomware attack revolves around risk reduction, Liska said. 

“Not paying a ransom means an increased risk of data exposure, which in this case could cause serious harm,” he told us. “While there is no good decision in most ransomware negotiations, the idea is to protect as many people as possible and that may mean that paying is the least bad option.” 

While he didn’t respond to or investigate the Instructure case, “protecting children’s data is absolutely a critical factor in these types of decisions, especially when the attacks originate from one of the groups associated with The Com,” Liska added. 

The Com, a loosely knit group of primarily English speakers who are also involved in several interconnected networks of hackers, SIM swappers, and extortionists such as ShinyHunters and Scattered Lapsus$ Hunters, has been known to blackmail kids and teens into carrying out shootings, stabbings, and other real-life criminal acts.

“These groups are known to coerce victims using threats of physical harm, including bricking and swatting,” he said. “Not paying may have increased the risk of serious harm to the children whose data was exposed.”

Ed sector ‘more likely to pay’

Instructure’s intrusion follows several other high-profile attacks against education-sector software providers.

In December 2024, PowerSchool suffered a breach, affecting tens of millions of students. The company reportedly paid about $2.85 million in bitcoin in exchange for a video supposedly showing the attackers destroying the data. But about five months later, in May 2025, the ed-tech provider’s school district customers received individual extortion threats from either the same ransomware crew that hit PowerSchool or someone connected to the crooks.

Earlier this year, ShinyHunters claimed it stole data from K-12 software provider Infinite Campus as part of a broader wave of Salesforce-related intrusions.

“Education keeps emerging as one of the sectors where organizations are still more likely to pay under pressure,” Thompson said. 

In addition to students’ – especially minors’ – data containing highly sensitive personal details, and therefore presenting an attractive target for attackers, this is also driven in part by market pressure and economics.

It’s costly and inconvenient for schools to switch learning management systems, and they are typically locked into multi-year contracts with these software vendors, according to Thompson.

“The other issue is concentration,” he said. “A relatively small number of vendors hold data for enormous portions of the education system. PowerSchool, Infinite Campus, Canvas, Blackboard; those four hold records on something close to every American student, and hackers know it. Three of the four have been breached at a multi-million-record scale in the last 18 months.”

Thompson said he expects to see additional attacks against major education platforms to follow. 

“The economics are good. Instructure paid. PowerSchool paid last year. Every other ed-tech vendor’s board just had a conversation about what their number would be,” he told us. “The pattern is established.”

According to Connolly, the universities and K-12 schools affected by the Canvas hack shouldn’t consider their data safe, regardless of Instructure’s assurances or the crooks’ promises to delete it. “There will be future attacks, without a doubt.” ®

Cerebras Systems has done what many chip startups aspire to but few ever achieve. On Thursday, the company and long-time Nvidia rival raised $5.55 billion in an initial public offering (IPO), making the company worth more than $66 billion on its first day of trading.

The milestone didn’t happen overnight. It took more than a decade, a radically different approach to chipmaking, and two separate attempts at an IPO to pull off.

Founded in 2015 by former SeaMicro head Andrew Feldman, Cerebras Systems’ first chips looked nothing like GPUs or AI accelerators of the time.

The bet that put Cerebras on the map

At the time, most high-end GPUs used dies measuring roughly 800 square mm that’d been cut from a larger wafer. Eight or more of these GPUs would typically be stitched together by high-speed interconnects, like NVLink, which allowed them to pool their resources and behave like one big accelerator.

Rather than cutting up a wafer into smaller chips just to reconnect them again, Cerebras figured why not etch all that compute into a wafer-sized chip? And so the Wafer-Scale Engine (WSE), a giant chip measuring 46,225 square mm — about the size of a dinner plate — was born.

Cerebras’ first chips weren’t just bigger; they were purpose-built for AI training and sported a novel compute engine designed to speed up the highly sparse matrix multiply-accumulate operations common in deep learning.

This hardware sparsity took advantage of the fact that large portions of a neural network’s parameters ultimately end up being zeros, allowing Cerebras to boost the effective computational output of its first-gen WSE accelerators from 2.65 16-bit petaFLOPS to 26.5.

Nvidia added support for sparsity in its Ampere generation a year later, but it only worked for a specific ratio (2:4), limiting its effectiveness to select use cases.

To train a model, up to 16 of these chips could be ganged together over a high-speed interconnect. This was kind of important too, because unlike GPUs, which stored model weights in HBM or GDDR memory, Cerebras’ chips were almost entirely reliant on on-chip SRAM. Although SRAM is insanely fast, which is why it’s used for caches in basically every modern processor, it’s not particularly space efficient.

While Cerebras’ first wafer-scale accelerator could theoretically reach 9 petabytes per second of memory bandwidth, it was limited to just 18 GB of capacity at a time when Nvidia was already at 32 GB per GPU and about to make the leap to 40 GB or even 80 GB per chip.

Still, the approach was performant enough that for its second-generation wafer-scale accelerator, launched in 2021, Cerebras doubled down on the architecture.

While the WSE-2 wasn’t physically larger, the move to TSMC’s 7nm process tech allowed the company to more than double the transistor count, compute density, SRAM capacity, and bandwidth.

The chips also supported larger clusters, scaling up to 192, though in practice these clusters were usually smaller at between 16 and 32 systems per site.

It was also around this time that Cerebras caught the attention of United Arab Emirates-based cloud provider G42, which quickly became its largest financier. By mid-2023, the chip startup had secured orders worth $900 million for nine supercomputing sites with a 36 exaFLOPS of super sparse AI compute between them.

A year later, Cerebras made the jump to TSMC’s 5nm process with the WSE-3 and while memory and bandwidth only saw modest gains, compute once again doubled now topping a 125 petaFLOPS of Sparse (12.5 petaFLOPS dense) compute at 16-bit precision.

Cerebras’ CS-3 systems have now seen the largest deployment, and now power the majority of the Condor Galaxy cluster it built for G42, as well as several new sites across North America and Europe.

Cerebras’ inference inflection

Up to mid-2024, Cerebras’ primary focus had been on training, but then the company announced a boutique inference-as-a-service offering to rival those from competing chip startups like Groq and SambaNova.

It turns out, Cerebras’ latest AI accelerators’ massive SRAM capacity not only made them potent training accelerators but particularly well suited to high-speed LLM inference. 

In its third iteration, Cerebras’ wafer scale accelerators boasted more memory bandwidth than they could realistically use. At 21 PB/s, the chip’s memory is nearly 1000x faster than Nvidia’s new Rubin GPUs.

This, along with a dash of speculative decoding, allowed Cerebras to generate tokens far faster than any GPU-based system of the time. Even today, Cerebras routinely ranks among the fastest inference providers in the world.

According to Artificial Analysis, Cerebras’ kit can churn out more than 2,200 tokens a second when running GPT-OSS 120B High, 2.8x faster than the next closed GPU cloud Fireworks.

Cerebras didn’t know it at the time, but its inference platform would be a much bigger business than anyone had expected, and in September 2024, the company submitted its S-1 filing to the SEC to take the company public. Almost exactly a year later, Feldman quietly pulled its S-1, delaying its IPO.

His reasons? The company’s initial S-1 filing was rather concerning, as it showed G42 was responsible for 87 percent of its revenues. But in the year since launching its inference platform, Cerebras had racked up several high-profile customer wins from big names like Alphasense, AWS, Cognition, Meta, Mistral AI, Notion, and Perplexity. Feldman explained that the initial S-1 didn’t yet show the financial results of this growth. The company believed it would have a better story to tell investors later down the road.

Cerebras’ inference platform has only grown since then. The company has steadily expanded its footprint while announcing deeper relationships with AWS and adding OpenAI as a customer.

On Thursday, the startup officially joined the NASDAQ under the ticker CBRS, having raised $5.5 billion in the process. Shares skyrocketed nearly 70 percent on the first day of trading, as investors poured their money into a new way to play the AI boom.

An IPO is something many startups aspire to but few, especially in the cut throat world of semiconductors, ever accomplish. 

What happens now

From a technical perspective, Cerebras is overdue for a refresh.

The WSE-3 accelerators that pushed it over the IPO finish line are getting rather long in the tooth and the architecture lead afforded by its SRAM-heavy design is shrinking.

Nvidia’s acquihire of Groq gave Feldman’s long-time rival an SRAM-packed inference platform of its own, while others are racing to catch up.

From here, we can only speculate, but we’ll hazard a guess that Cerebras’ new shareholders are going to want to see new silicon sooner than later.

Based on its existing roadmap, we expect WSE-4 will offer a sizable leap in floating point performance, though not necessarily at 16-bit precision. Much of the industry has aligned around lower precision data types like FP8 and FP4. An exaFLOP of ultra-sparse FP4 compute wouldn’t shock us in the least. 

How useful sparsity would actually be for LLM inference is another matter. LLM inference hasn’t historically benefited much from sparsity, but that’s never stopped chipmakers from advertising sparse FLOPS anyway.

We also expect to see Cerebras pack more SRAM into its next wafer scale compute platform, possibly using TSMC’s 3D chip stacking tech to do it. The WSE-3’s 44GB of SRAM capacity remains a limiting factor for what models it can and can’t serve efficiently.

A trillion parameter model like Kimi K2 would require somewhere between 12 and 48 of Cerebras’ WSE-3 accelerators, depending on how the model weights are stored and how many parameters have been pruned, and so any increase in SRAM capacity would go a long way toward improving the efficiency of its accelerators.

More collaborations

Alongside new silicon, we can also expect to see more collaborations akin to Cerebras’ tie-up with AWS.

Earlier this year, AWS announced it would combine its Trainium3 AI accelerators with Cerebras’ WSE-3-based systems to speed up its inference platform in much the same way Nvidia is doing with Groq’s accelerators.

Cerebras could certainly do something similar with AMD or any other chipmaker. In this sense, Cerebras is in the position to offer its chips as a decode accelerator, which offloads the bandwidth intensive parts of the inference pipeline onto its chips, while other parts handle the compute heavy prompt processing side of the equation.

However, Cerebras frames its next collab; its shareholders are going to expect growth. And as the saying goes, the enemy of my enemy is my friend. ®