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Digital Surveillance Turns Everyday Devices Into Evidence

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Every time you unlock your smartphone or start your connected car, you are generating a trail of digital evidence that can be used to track your every move.

In Your Data Will Be Used Against You: Policing in the Age of Self-Surveillance, just published by NYU Press, law professor Andrew Guthrie Ferguson exposes how the Internet of Things has quietly transformed into a vast surveillance network, turning our most personal devices into digital informants. The following excerpt explores the concept of “sensorveillance,” detailing the specific mechanisms—such as Google’s Sensorvault, geofence warrants, and vehicle telemetry—that allow law enforcement to repurpose consumer technology into powerful tools for investigation and control.

A man walked into a bank in Midlothian, Va., his black bucket hat pulled low over dark sunglasses. He handed a note to the teller, brandished a gun, and walked away with US $195,000. Police had no leads—but they knew that the robber had been holding a smartphone when he entered the bank. Guessing that the smartphone, like most smartphones, had some Google-enabled service running, police ordered Google to turn over information about all the phones near the bank during the holdup. In response to a series of warrants, Google produced information about 19 phones that had been active near the bank at the time of the robbery. Further investigation directed the police to Okelle Chatrie, who was ultimately charged with the crime.

Cathy Bernstein had a tough time explaining why her own car reported an accident to police. Bernstein had been driving a Ford equipped with 911 Assist, which was automatically enabled when she struck another vehicle. Rather than stick around to trade insurance information, she sped away. But her smart car had registered the bump—and called the police dispatcher, leading to a fairly awkward conversation:

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Apparently, Bernstein did do something “like that.” She was soon caught and cited for leaving the scene of the accident. Her own car provided evidence of her guilt.

The Rise of “Sensorveillance”

Once upon a time, our things were just things. A bike was a tool for biking. It got you from one location to another, but it didn’t “know” more about your travels than any other inanimate object did. It was dumb in a comforting way, and we used it as intended. Today, a top-of-the-line bike can track your route and calculate your average speed along the way. Hop on an e-bike from a commercial bike share, and it will collect data for your trip, plus the trips of everyone else who used it that month.

These “smart” objects belong to what technologist Kevin Ashton named the Internet of Things. Ashton proposed adding radio-frequency identification (RFID) tags and sensors to everyday objects, allowing them to collect data that could be fed into networked systems without human intervention. A sensor in a river could monitor the cleanliness of the water. A tag on a bottle of shampoo could trace its journey throughout the supply chain. Add enough sensors to enough objects and you can model the health of an entire ecosystem—or learn whether you’re sending too much of your inventory to Massachusetts and too little to Texas.

Ashton first theorized the Internet of Things (IoT) in the late 1990s. Today, the IoT goes well beyond his initial vision, including not only RFID tags but also sensors with Wi-Fi, Bluetooth, cellular, and GPS connections. These small, low-cost sensors record data about movement, heat, pressure, or location and can engage in two-way communication.

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Of course, such a system is also, by necessity, a system of surveillance. “Sensorveillance”—a term I created to highlight the intersection of sensors and surveillance—is slowly becoming the default across the developed world.

Cellphone Surveillance Networks

Let’s start with phones. You’re probably not surprised that your cellphone company tracks your location; that’s how cellphones work. Both smartphones and “dumb” mobile phones use local cell towers, owned by cellphone companies, to connect you to your friends and family, which means those companies know which towers you are near at all times.

If you always carry your phone with you, your phone’s whereabouts—recorded as cell-site location information (CSLI)—reveal yours. One man, Timothy Carpenter, found this out the hard way after he and a group of associates set out to rob a series of electronics stores. Carpenter was the alleged ringleader, but he didn’t enter the stores himself. He served as the lookout, waiting in the car while his associates stuffed merchandise into bags.

It might have been hard for investigators to tie him to the crimes—if not for the fact that every minute he kept watch, his cellphone was pinging a local tower, logging his location. Using that information, the FBI was able to determine that he had been near each store during the exact moment of each robbery.

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Cell signals are the tip of the proverbial data iceberg. If you have a smartphone, you’re almost certainly using something created by Google. Google makes money off advertising. The more Google knows about users, the better it can target ads to them. Google’s location services are on all Android phones, which use the company’s operating system, but they’re also on Google apps, including Google Maps and Gmail.

For years, all that location information ended up in what the company called the Sensorvault. The Sensorvault, as the name suggests, combined data from GPS, Bluetooth, cell towers, IP addresses, and Wi-Fi signals to create a powerful tracking system that could identify a phone’s location with great precision. As you might imagine, police saw it as a digital evidence miracle. In 2020, Google received more than 11,500 warrants from law enforcement seeking information from the Sensorvault.

“Sensorveillance”—a term I created to highlight the intersection of sensors and surveillance—is slowly becoming the default across the developed world.

In 2024, Google announced that it would no longer retain all of this data in the cloud. Instead, the geolocation information would be stored on individual devices, requiring police to get a warrant for a specific device. The demise of the Sensorvault came about through a change in corporate policy, which could be reversed. But at least for now, Google has made it significantly harder for police to access its data.

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And while the Sensorvault was the biggest source of geolocational evidence, it is far from the only one. Even apps that have nothing to do with maps or navigation might nonetheless be collecting your location data. In one Pennsylvania case, prosecutors learned that a burglar used an iPhone flashlight app to search through a home, and they used the data from the app to prove he was in the home at the time of the break-in. These apps might be advertised as “free,” but they come with a hidden cost.

Cars, increasingly, collect almost as much information as phones. Mobile extraction devices can collect digital forensics about a car’s speed, when its airbags deployed, when its brakes were engaged, and where it was when all that happened. If you connect your phone to play Spotify or to read out your texts, then your call logs, contact lists, social media accounts, and entertainment selections can be downloaded directly from your vehicle. Because cars are involved in so many crimes (either as the instrument of the crime or as transportation), searches of this data are becoming more commonplace.

Even without physically extracting information from the car, police have other ways to get the data. After all, the car’s built-in telemetry system is sharing information with third parties. In addition to the usual personal information you give up when buying a car (name, address, phone number, email, Social Security number, driver’s license number), when you own a Stellantis-brand car, the company collects how often you use the car, your speed, and instances of acceleration or braking. Nissan asserts the right to collect information about “sexual activity, health diagnosis data, and genetic [data]” in addition to “preferences, characteristics, psychological trends, predispositions, behavior, attitudes, intelligence, abilities, and aptitudes.” Nissan’s privacy policy specifically reserves the right to provide this information to both data brokers and law enforcement.

The Law of Smart Things

The fact that government agents can glean so much information from our things does not mean that they should be able to do so at any time or for any reason. The U.S. Fourth Amendment—drafted in an era without electricity—protects “persons, houses, papers, and effects” against unreasonable search and seizure, but is naturally silent on the question of location data.

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The first question is whether the data from our smart things should be constitutionally protected from police. In the language of the constitutional text, the smart device itself is an “effect”—a movable piece of personal property. But what about the data collected by the effect? Is the location data collected by your smartwatch considered part of the watch, or part of the person wearing the watch? Neither? Both?

To its credit, the U.S. Supreme Court has addressed some of the hard questions around digital tracking. In two cases, the first involving GPS tracking of a car and the second involving the CSLI tracking of Timothy Carpenter’s cellphone, the court has placed limits on the government’s ability to collect location data over the long term.

United States v. Jones involved GPS tracking of a car. Antoine Jones owned a nightclub in Washington, D.C. He also sold cocaine and found himself under criminal investigation for a large-scale drug distribution scheme. To prove Jones’s connection to “the stash house,” police placed a GPS device on his wife’s Jeep Cherokee. This was before GPS came standard in cars, so the device was physically attached to the undercarriage of the vehicle.

Data about Jones’s travels was recorded for 28 days, during which he visited the stash house multiple times. The prosecutors introduced the GPS data at trial, and Jones was found guilty. Jones appealed his conviction, arguing that the warrantless use of a GPS device to track his car violated his Fourth Amendment rights.

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“When the Government tracks the location of a cell phone it achieves near perfect surveillance.” — the Supreme Court

In 2012, the Supreme Court held that a warrant was required, based on the reasoning that the physical placement of the GPS device on the Jeep was itself a Fourth Amendment search requiring a warrant. Justice Sonia Sotomayor agreed regarding the physical search but went further, discussing the harms of long-term GPS tracking: “GPS monitoring generates a precise, comprehensive record of a person’s public movements that reflects a wealth of detail about her familial, political, professional, religious, and sexual associations.”

Timothy Carpenter’s ill-fated robbery spree gave the Supreme Court another chance to address the constitutional harms of long-term tracking. In their attempts to connect Carpenter to the six electronics stores that had been robbed, federal investigators requested 127 days of location data from two mobile phone carriers. The problem for the police, however, was that they had obtained the information on Carpenter without a judicial warrant.

Carpenter challenged the FBI’s acquisition of his CSLI, claiming that it violated his reasonable expectation of privacy. In a 5–4 opinion, the Supreme Court determined that the acquisition of long-term CSLI was a Fourth Amendment search, which required a warrant. As the Court stated in its 2018 ruling: “A cell phone faithfully follows its owner beyond public thoroughfares and into private residences, doctor’s offices, political headquarters, and other potentially revealing locales…. [W]hen the Government tracks the location of a cell phone it achieves near perfect surveillance.”

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Jones and Carpenter are helpful for setting the boundaries of location-based searches. But, in truth, the cases generate a lot more questions than answers. What about surveillance that is not long-term? At what point does the aggregation of details about a person’s location violate their reasonable expectation of privacy?

The Warrant According to Google

Okelle Chatrie’s case, in which police used Google’s location data to identify him as the mystery bank robber, offers a stark warning about the limits of Fourth Amendment protections under these circumstances. It’s also a terrific example of why “geofence” warrants, which request information within a certain geographic boundary, are appealing to police. From surveillance footage, detectives could see that the suspect had a phone to his ear when he walked into the bank. A geofence could identify who the suspect was, and likely where he came from and where he went. Google held the answer in its virtual vault. A warrant gave investigators the key.

The police cast a broad net. The geofence warrant asked for data on all the cellphones within a 150-meter radius, an area, as the court described it, “about three and a half times the footprint of a New York city block.” After receiving the police’s initial request for information on all the phones in the area, Google returned 19 anonymized numbers. Over the course of a three-step warrant process, the company narrowed those 19 phones down to three and then to one, which it revealed as belonging to Okelle Chatrie.

If the police wish to buy the data, just like an insurer or marketing firm might, how can you object? It’s not your data.

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The three-step warrant process is a unique innovation in the digital evidence space. Google’s lawyers developed a procedure whereby detectives seeking targeted geolocation data had to file three separate requests, first requesting identifying numbers in an area, then narrowing the request based on other information, and finally obtaining an order to unmask the anonymous number (or numbers) by providing a name.

To be clear, Google—a private company—required the government to jump through these hoops because Google considered it important to protect its customers’ data. It was the company’s lawyers—not the courts or the government—who demanded these warrants.

Buying Data

Warrants provide at least some procedural barrier to data collection by police. If government agencies want to avoid that minor hassle, they can simply buy the data instead. By contracting with data-location services, several federal agencies have already done so.

The logic for this Fourth Amendment loophole is straightforward: You gave your data to a third-party company, and the company can use it as it wishes. If you own a car that is smart enough to collect driving analytics, you clicked some agreement saying the car company could use the data—study it, analyze it, and, if it wants, sell it. If you don’t want to give them data in the first place, that is okay (although it will likely result in less optimal functionality), but you cannot rightly complain when they use the data you gave them in ways that benefit them. If the police wish to buy the data, just like an insurer or marketing firm might, how can you object? It’s not your data.

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Who Is to Blame?

Fears about the amount of personal information that could be revealed with long-term GPS surveillance have become reality. Today, police don’t need to plant a device to track your movements—they can rely on your car or phone to do it for them.

This happened because companies sold convenience and consumers bought it. So it might be tempting to blame ourselves. We’re the ones buying this technology. If we don’t want to be tracked, we can always go back to using paper maps and writing down directions by hand. If few of us are willing to make that trade, that’s on us.

But it’s not that easy. You may still be able to choose a dumb bike over a smart one, but a car that tracks you will soon be the only type of car you can buy. And while cars and data can, in theory, be separated, that’s not true for all our smart things. Without cell-signal tracking capabilities, a cellphone is just a paperweight. And in today’s world, living without a phone or a car is simply not practical for many people.

There are technological steps we can take toward protecting privacy. Companies can localize the data the sensors generate within the devices themselves, rather than in a central location like the Sensorvault. Similarly, the information that allows you to unlock your Apple iPhone via facial recognition stays localized on the phone. These are technological fixes, and positive ones. But even localized data is available to police with a warrant.

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This is the puzzle of the digital age. We can’t—or don’t want to—avoid creating data, but that data, once created, becomes available for legal ends. The power to track every person is the perfect tool for authoritarianism. For every wondrous story about catching a criminal, there will be a terrifying story of tracking a political enemy or suppressing dissent. Such immense power can and will be abused.

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AI is now taking over game servers, and Stormgate is the first casualty

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Stormgate, a free-to-play, StarCraft-style RTS developed by Frost Giant Studios, relies on a third-party “game server orchestration partner” to run its online modes. Frost Giant told players on Discord that the provider had been acquired by an AI company, forcing a planned outage that will take Stormgate’s multiplayer modes offline…
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Apps on the App Store are being updated by Apple, though there's no clear reason why

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A range of seemingly random apps in the App Store have been updated by Apple itself, though nothing has been shared about why, nor have there been changes in the codebases themselves.

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Apple has been known to push updates to apps in its App Store, though they’re usually to ensure legacy apps still work. On Monday, some users have noted both new and old apps have received an update direct from Apple.
According to a report from MacRumors based on a Reddit post, the updates don’t appear to change anything about the app itself. The changes could be related to something on Apple’s backend, or a specific API, but it is unclear.
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OpenAI Calls For Robot Taxes, Public Wealth Fund, and 4-Day Workweek To Tackle AI Disruption

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OpenAI is proposing (PDF) sweeping policy changes to help manage the societal disruption caused by advanced AI, including taxes on automated labor, a public wealth fund, and experiments with a four-day workweek. The company said the policy document offered a series of “initial ideas” to address the risk of “jobs and entire industries being disrupted” by the adoption of AI tools. Business Insider reports: Among the core policy suggestions is a public wealth fund, which would see lawmakers and AI companies work together to invest in long-term assets linked to the AI boom, with returns distributed directly to citizens. Another is that the government should encourage and incentivize employers to experiment with four-day workweeks with no loss in pay and offer “benefits bonuses” tied to productivity gains from new AI tools.

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Seattle entrepreneur Robbie Cape’s lengthy job search takes unexpected turn with launch of new startup

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Robbie Cape is a tech veteran and serial entrepreneur. (File Photo via 98point6)

Robbie Cape, the Seattle tech entrepreneur who has dabbled in healthcare and fried chicken in recent years, has another new venture.

In a post on LinkedIn on Monday, Cape said his nine-month search for a new job led somewhere he didn’t expect — and he’s starting a company.

“We’re in stealth for now — the idea and the story behind it will come,” Cape wrote. “But right now, we’re imagining. We’re shaping the vision, building the team, defining the culture. The slate is clean. The sky is open. And we are having an absolute blast.”

Cape said the new venture incorporated in March, and a few weeks ago he welcomed CTO T Van Doren and chief product officer Matt Witcher as co-founders. Cape said Van Doren was employee No. 1 and Witcher was employee No. 8 at 98point6, the telehealth startup that Cape co-founded and ran as CEO for six years.

Cape previously spent 11 years at Microsoft and was the co-founder and CEO of Cozi, an app for managing family events, activities and schedules. After being forced out of 98point6, Cape helped launch the sustainable chicken restaurant Mt. Joy in 2022. The small chain has locations in Seattle’s South Lake Union and Capitol Hill neighborhoods.

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Cape left Mt. Joy in May 2025, according to his LinkedIn. And in his post on Monday, he said he’d been searching for a job until last month. The process — in which he was looking for any size company, stage or title — took longer than he imagined it would as he connected with 200 people across nearly 2,000 interactions.

“It was hard in ways I didn’t expect,” Cape wrote. “But it gave me something I didn’t expect either — real empathy for a process most people dread but everyone eventually has to go through.”

GeekWire reached out to Cape for details on his new company, and we’ll update when we hear back.

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Trump Administration Bans Chinese Routers. Phones and Cameras Could Follow

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The Federal Communications Commission continued its crackdown on Chinese tech on Friday, issuing a new proposal that would extend a ban on companies to products previously authorized.

In 2021, companies such as Huawei, Hikvision, Dahua, Hytera and ZTE were added to the FCC’s Covered List, a record of companies and products that the FCC believes pose a national security risk to the US, under the Secure Networks Act. The Chinese companies produce mobile phones, security cameras and other tech products.

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“Older models of covered equipment pose an unacceptable risk today when imported or marketed in the United States, not only when such equipment is new to the market,” an FCC report from October said.

The proposal will be open for comment until May 6, after which the commission will vote on whether to adopt the rules. The ban won’t affect devices already owned by Americans.

Read more: My Expert Advice: Don’t Buy a Router Until We Know More About the FCC’s Ban

Millions of consumers and businesses rely on Wi-Fi routers, telecommunications equipment and security cameras every day, making these devices critical links in both home and office networks. The Federal Communications Commission shocked the broadband industry on March 23 by effectively banning the sale of future foreign-made Wi-Fi routers (including some of the biggest router brands). 

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In recent years, Chinese telecommunications companies have faced restrictions on operating in the US. In 2020, The Wall Street Journal cited US officials who reportedly said that Chinese companies, including Huawei, used backdoor access intended for law enforcement to track sensitive information.

But this ban could be implemented quickly. The FCC proposes that “all parties [will have to] cease all importation and marketing activities within 30 days of the effective date of the prohibition.”

This proposition doesn’t reflect a final legal ruling on telecommunications imports, but it does reflect how the Trump administration has been increasingly pressuring Chinese tech companies in recent months.

The foreign-made router ban was only the latest in a string of decisions that have placed restrictions on Chinese tech companies operating in the US.

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In December, the FCC banned the importation of Chinese-made drones into the US. Just months before that, the agency voted to block new approvals for any device containing parts manufactured by companies on the Covered List.

Representatives from the FCC and Huawei didn’t immediately respond to requests for comment.

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New Jersey has no right to ban Kalshi’s prediction market, US appeals court rules

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Kalshi can’t be stopped in New Jersey. A 3rd US Circuit Court of Appeals panel ruled on Monday that New Jersey has no authority to regulate Kalshi’s prediction market allowing people to bet on the outcome of sports events. That power rests with the Commodity Futures Trading Commission, the panel ruled 2-1.

The CFTC is headed by President Donald Trump appointee Michael Selig, who vocally and actively supports prediction markets like Kalshi and Polymarket, calling them “exciting products.” The Trump family agrees: Donald Trump Jr. is a paid adviser to Kalshi and an unpaid adviser to Polymarket, and Truth Social, which is run by the Trump Media and Technology Group, is set to start a prediction market of its own.

Online prediction markets are an emerging phenomenon that allow users to bet on the outcome of basically anything, from local athletic competitions to lethal military invasions. Though they’re new, these marketplaces have already shown evidence of insider trading on an extreme scale, with suspicious bets and big payouts tied to the US and Israel’s military strikes in Iran, and also the US’ brief invasion in Venezuela. According to blockchain analyst DeFi Oasis, fewer than 0.04 percent of Polymarket accounts captured more than 70 percent of profits, totaling $3.7 billion.

Multiple state gaming regulators have filed legal challenges against Kalshi and Polymarket in recent months, and just last week the CFTC sued Arizona, Connecticut and Illinois over their attempts to regulate prediction markets. While each state has its own angle of attack, from election issues to underage betting, they’re all broadly claiming that prediction markets are just illegal gambling businesses. Today’s ruling marks the first federal-level decision in one of these cases and it’s in favor of the prediction markets.

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New Jersey sent Kalshi a cease and desist letter in 2025, claiming the service violated the state’s ban on collegiate sports betting. Kalshi escalated the situation and sued New Jersey, arguing that its sports contracts are actually swaps, a type of financial investment that’s (conveniently) regulated by the CFTC. A lower-court judge previously sided with Kalshi, prompting New Jersey to appeal. Two of the three judges in that appeal ruled that Kalshi’s sports-related event contracts were indeed swaps. Kalshi CEO Tarek Mansour called Monday’s ruling “a big win for the industry.”

US Circuit Judge Jane Richards Roth dissented, writing that Kalshi’s “offerings were virtually indistinguishable from the ​betting products available on online sportsbooks, such as DraftKings and FanDuel.”

New Jersey Attorney General Jennifer Davenport has the option to ask the full 3rd Circuit to rehear the case, and the issue is also pending in several other courts.

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New GPUBreach attack enables system takeover via GPU rowhammer

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New GPUBreach attack enables system takeover via GPU rowhammer

A new attack, dubbed GPUBreach, can induce Rowhammer bit-flips on GPU GDDR6 memories to escalate privileges and lead to a full system compromise.

GPUBreach was developed by a team of researchers at the University of Toronto, and full details will be presented at the upcoming IEEE Symposium on Security & Privacy on April 13 in Oakland.

The researchers demonstrated that Rowhammer-induced bit flips in GDDR6 can corrupt GPU page tables (PTEs) and grant arbitrary GPU memory read/write access to an unprivileged CUDA kernel.

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An attacker may then chain this into a CPU-side escalation by exploiting memory-safety bugs in the NVIDIA driver, potentially leading to complete system compromise without the need to disable Input-Output Memory Management Unit (IOMMU) protection.

GPUBreach attack steps
GPUBreach attack steps
Source: University of Toronto

IOMMU is a hardware unit that protects against direct memory attacks. It controls and restricts how devices access memory by managing which memory regions are accessible to each device.

Despite being an effective measure against most direct memory access (DMA) attacks, IOMMU does not stop GPUBreach.

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“GPUBreach shows that GPU Rowhammer attacks can move beyond data corruption to real privilege escalation,” the researchers explain.

“By corrupting GPU page tables, an unprivileged CUDA kernel can gain arbitrary GPU memory read/write, and then chain that capability into CPU-side escalation by exploiting newly discovered memory-safety bugs in the NVIDIA driver.”

“The result is system-wide compromise up to a root shell, without disabling IOMMU, unlike contemporary works, making GPUBreach a more potent threat.”

Overview of how GPUBreach works
Overview of how GPUBreach works
Source: University of Toronto

The same researchers previously demonstrated GPUHammer, the first attack showing that Rowhammer attacks on GPUs are practical, prompting NVIDIA to issue a warning to users and suggesting the activation of the System Level Error-Correcting Code mitigation to block such attempts on GDDR6 memory.

However, GPUBreach is taking the threat to the next level, showing that it is possible not only to corrupt data but also to gain root privileges with IOMMU enabled.

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The researchers exemplified the results with an NVIDIA RTX A6000 GPU with GDDR6. This model is widely used in AI development and training workloads.

Comparison to other attacks
Comparison to other GPU attacks
Source: University of Toronto

Disclosure and mitigations

The University of Toronto researchers reported their findings to NVIDIA, Google, AWS, and Microsoft on November 11, 2025.

Google acknowledged the report and awarded the researchers a $600 bug bounty.

NVIDIA stated that it may update its existing security notice from July 2025 to include the newly discovered attack possibilities.

As demonstrated by the researchers, IOMMU alone is insufficient if GPU-controlled memory can corrupt trusted driver state, so users at risk should rely solely on that security measure.

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Error Correcting Code (ECC) memory helps correct single-bit flips and detect double-bit flips, but it is not reliable against multi-bit flips.

Ultimately, the researchers underlined that GPUBreach is completely unmitigated for consumer GPUs without ECC.

The researchers will publish the full details of their work, including a technical paper and a GitHub repository with the reproduction package and scripts, on April 13.

Automated pentesting proves the path exists. BAS proves whether your controls stop it. Most teams run one without the other.

This whitepaper maps six validation surfaces, shows where coverage ends, and provides practitioners with three diagnostic questions for any tool evaluation.

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Spain’s Xoople raises $130m to build the data infrastructure AI needs to understand Earth

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In short: Xoople, a Madrid-based geospatial data company founded in 2019, has raised a $130 million Series B led by Nazca Capital, bringing its total funding to $225 million and pushing its valuation into unicorn territory. The round was co-invested by MCH Private Equity, CDTI (the Spanish government’s technology development fund), Buenavista Equity Partners, and Endeavor Catalyst. Alongside the raise, Xoople announced a partnership with US space and defence contractor L3Harris Technologies to build sensors for its own satellite constellation, designed to produce Earth surface data it says will be “two orders of magnitude better than existing monitoring systems.” The company’s EarthAI platform, built on Microsoft Azure and distributed through Microsoft and Esri, delivers continuous surface intelligence for insurers, farmers, governments, and infrastructure operators.

Xoople has spent seven years building something that did not previously exist in a commercially deployable form: a continuous, AI-native data layer for the Earth’s surface. The Madrid startup, founded in 2019, emerged from that development period with a €115 million in prior funding, a platform embedded in the two most widely used enterprise geospatial ecosystems in the world, and a thesis that the AI era will require a fundamentally different approach to Earth observation — one designed from the ground up for machine learning rather than adapted from satellite imagery workflows built for human analysts. The $130 million Series B, led by Nazca Capital, confirms that investors believe that thesis is credible enough to back at scale.

CEO and co-founder Fabrizio Pirondini told TechCrunch the raise brings Xoople’s total funding to $225 million and puts the company in unicorn territory on valuation. The round was joined by MCH Private Equity, CDTI, the Spanish government-backed technology development fund that has also backed Nazca Capital’s aerospace and defence fund, Buenavista Equity Partners, and Endeavor Catalyst.

What EarthAI actually does

Xoople’s core product, EarthAI, is an end-to-end Earth intelligence system. It ingests continuous surface data, currently sourced from government spacecraft and third-party satellite networks, and processes it into AI-ready datasets that can be queried for change detection, risk prediction, and environmental monitoring. The key design choice is continuity: rather than producing point-in-time images for human review, EarthAI is built to stream a persistent, structured view of the planet’s surface into AI models that need regular, reliable ground truth.

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The use cases span industries that share a dependence on understanding what is happening on the physical surface of the Earth. For agriculture, EarthAI provides early detection of crop stress, monitors soil health and water conditions, and generates data that enables farmers to participate in carbon credit markets. For insurance, it enables more precise climate risk pricing and real-time verification of natural disaster claims, removing the delay and subjectivity of ground-based assessments. For infrastructure operators, it monitors physical assets for signs of stress or degradation before failures occur. For governments, it supports emergency planning, environmental enforcement, and humanitarian response. Capital flowing into specialised AI applications at the intersection of science, data, and infrastructure has accelerated considerably over the past year, and Xoople sits precisely at that intersection.

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The satellite play

The $130 million will fund Xoople’s transition from a platform built on others’ data to one powered by its own. Alongside the Series B, the company announced a partnership with L3Harris Technologies, a US space and defence contractor, to design and manufacture sensors for Xoople’s own satellite constellation. The sensors will collect optical data. Pirondini told TechCrunch that the constellation is designed to produce “a stream of data that is going to be two orders of magnitude better than existing monitoring systems“, a claim that, if borne out, would represent a substantial leap over the imagery quality currently available from commercial earth observation operators.

That claim is where Xoople meets its competitive reality. The company is entering a market that includes Vantor (formerly Maxar Intelligence, rebranded in October 2025), Planet Labs, BlackSky, Airbus Defence and Space, ICEYE, and Capella Space — all of which have satellites already in orbit and established AI-focused data processing pipelines. Companies building the hardware and data layers that AI depends on face a lengthy gap between the announcement of a new approach and its delivery in deployable form, and Xoople’s constellation is not yet in orbit. For now, EarthAI runs on data it did not produce. The L3Harris partnership signals that the proprietary data supply is the next phase.

Distribution before data

Xoople’s strategic sequencing is unusual for an Earth observation company. Most competitors in the space led with hardware — launching satellites, then figuring out distribution. Xoople did the reverse: it spent its first seven years embedding its platform into Microsoft and Esri, the two dominant environments where enterprise buyers, governments, and GIS professionals already live. Neither Microsoft nor Esri has its own proprietary satellite data. Xoople positioned itself to supply that gap from inside the platforms where the purchasing decisions are made.

The Microsoft relationship is structural: Xoople’s platform runs on Azure, and the company is integrated with Microsoft’s Planetary Computer Pro, which delivers AI-powered geospatial insights for enterprise use. Esri, the world’s largest geospatial software company, is a partner distributor. The implication is that when Xoople’s own constellation is operational and its data quality delivers on the “two orders of magnitude” promise, it will have distribution in place that its newer competitors would need years to replicate. The investment flowing into cloud-based AI data infrastructure has made the ability to process and deliver petabytes of Earth surface data at low latency a tractable problem; the scarcity is in the quality and continuity of the underlying data itself.

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A Spanish unicorn in a European context

Xoople’s raise is one of the larger deep tech rounds to come out of Spain in recent years, and it lands in a moment that the European space and defence investment community has been accelerating. Nazca Capital, which led the Series B, runs Spain’s largest private equity fund specialised in aerospace and defence, a fund that also received a €294 million commitment from CDTI and a €40 million investment from the European Investment Fund. The investor composition of the Xoople round,government-backed funds, European private equity, and Endeavor Catalyst, which focuses on high-impact technology entrepreneurs, reflects the persistent tension in European technology between deep technical ambition and the capital required to realise it: the funding is patient, multi-source, and has a public interest dimension that pure venture rounds often lack.

The earth observation market was valued at $7.04 billion in 2025 and is projected to reach $14.55 billion by 2034, growing at just over 8% annually. Xoople is betting that as AI models grow more capable and more dependent on real-world data, the market for continuous, structured Earth surface intelligence, rather than periodic imagery, will grow faster than that aggregate. A year in which the appetite for AI applications in climate, infrastructure, and environmental risk grew considerably provided the validation Xoople needed; the $130 million is the bet that the second half of the decade will prove it right at scale.

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