TECH

Fairphone 6 turns into a Linux phone running Fedora 44 with working cameras

A developer has managed to boot Fedora 44 on the Fairphone 6, bringing a desktop-oriented Linux distribution to a smartphone already available with Android-based /e/OS. The latest demonstration confirms all three cameras are fully operational.

The main upgrades to the Fairphone 6 Plus are internal: the phone now features 12 GB of RAM—a 50% increase over the base model's 8 GB—and is equipped with the Snapdragon 7s Gen 4 processor, which is slightly more powerful than the 7s Gen 3 found in its standard sibling.

Additionally, the lineup has gained a new color option, Cobalt Blue (dark blue), joining the existing Forest Green (dark green) and Horizon Black (black); the Cloud White option appears to be exclusive to the standard Fairphone 6.

The rest of the specifications remain unchanged, maintaining a robust configuration for a premium mid-range smartphone. The device features a 6.31-inch LTPO OLED display with a resolution of 2484 x 1116 pixels, a refresh rate of up to 120 Hz, and Gorilla Glass 7i protection, along with a 4,415 mAh battery supporting 30W fast charging.

The camera setup includes a 50 MP Sony LYTIA 700C main sensor with optical image stabilization, accompanied by a 13 MP ultrawide lens and a 32 MP front-facing camera. On the software front, the phone ships with Android 16, an upgrade from the Android 15 found on the older model.

The manufacturer's commitment to longevity is a key strength: six major operating system updates are guaranteed, along with ongoing security patches extending until 2032 or 2033—the website mentions both dates, making the exact cutoff unclear, though both timeframes are impressive.

Getting desktop-class Linux to run on mobile hardware is notoriously difficult, but developer Nick on X has managed to pull it off on the modular Fairphone 6, giving us our first look at Fedora 44 running on an Android device. 

In a hands-on video, the phone is shown running a touch-friendly GNOME interface running smoothly, without any noticeable lag. Scrolling through the Settings pane displays options for wi-fi, network, multitasking, appearance and even sections for mouse & touchpad, keyboard, color management, printers etc. just like you would see on a desktop. Swiping down from the top bar smoothly pulls up the calendar widget alongside the quick-settings shade, where toggles for Wi-Fi, Night Light, Dark Style, Tiling, and display brightness are all fully interactive. Heading into the system's "About" page verifies the build as Fedora Linux 44 (Forty Four) under the host name fedora, with the system accurately detecting 7.1 GB of RAM and 251.5GB of internal storage.

Because it is an early port, there are still a few issues. As shown at the end of the video, tapping the Trash icon on the bottom dock brings up an error message saying "Failed to launch 'Trash': Failed to find default application for content type 'inode/directory', which simply shows that default file manager shortcuts aren't properly linked yet.

A follow-up demo shows the camera app opening smoothly and switching seamlessly between the main, ultra-wide, and front lenses without a single crash.

The Fairphone 6+ recently made its global debut alongside an official launch in the US. It retains almost the same hardware and design as the standard Fairphone 6, differing only under the hood with an upgraded Snapdragon 7s Gen 4 processor and 12GB of RAM compared to the regular Fairphone 6 which features Snapdragon 7s Gen 3 and 8GB RAM configuration.

mundophone

TECH

When zombie credit cards attack: UMass researchers discover loophole that can ranimate expired cards

A new security loophole can bring some expired credit cards back to life, researchers from the University of Massachusetts Amherst have discovered. If stolen, these “zombie credit cards” could leave cardholders vulnerable to fraudulent charges, according to the new study, presented at the conference USENIX Security 2026.

The researchers discovered that thieves “can still use the victim’s expired credit card, despite the victim receiving another card,” says Taqi Raza, assistant professor in the Riccio College of Engineering at UMass Amherst.

This loophole exists because credit card accounts do not expire with the physical card. For instance, if you make a return, your account will be refunded, even if the purchasing credit card has expired. But this made Raza wonder: “If the card can get a refund, can the card make a payment?”

For some credit cards, the answer is yes. Raza and his research team devised a system using two, off-the-shelf smartphones and basic emulator software to fool an in-store card reader, also known as a point-of-sale (POS) terminal, into thinking a card was active.

Using the same technology that enables tap-to-pay transactions (near-field communications, or NFC), the first phone is used to activate the credit card. It tells the card: a purchase is trying to be made, so send over the cardholder data and payment application. This includes the past-due expiration date.

However, using a “man-in-the-middle” Wi-Fi-based relay system, the second phone takes the credit card information but rewrites the expiration date. Raja Hasnain Anwar, the lead author of the study and doctoral candidate with the Khwarizmi Lab at UMass Amherst, notes that this attack is particularly dangerous because thieves do not need to determine the actual expiration date of a replacement card; any arbitrary expiration date in the future is sufficient to successfully make a charge.

“The expiration date printed and stored on the card is the only way for the POS to know whether the card is active or expired,” he says. “Yet it is not cryptographically protected. So we can easily modify it to fool the POS.”

This second phone is then tapped to the card reader. To an outside observer, the behavior would look the same as using any kind of digital wallet.

“Now, you’re expecting the bank should notice it,” says Raza. But not all banks verify the terminal-read expiration date against authenticated data. If other security measures are not in place to recheck card lifecycle status, the fraudulent transaction will be successful.

''The expiration date printed and stored on the card is the only way for the POS to know whether the card is active or expired, yet it is not cryptographically protected. So we can easily modify it to fool the POS''... Raja Hasnain Anwar, doctoral candidate with the Khwarizmi Lab at UMass Amherst and lead author of the new study.

Image

The loophole that enables an expired credit card to be revived is based on a relay system using software that can be run on two standard smartphones and the physical card. The researchers found it worked across a variety of point-of-sale terminals(image above)

In fact, credit cards have another expiration date in addition to the one printed on your card: a date embedded in the security key that encrypts the transaction between the card and the bank, referred to as a digital certificate. This digital certificate is checked first, enabling the card to “talk” to the POS.

“What we found is that the expiry date for the digital certificate for the security key is longer than the expiry date of return on the card,” says Raza, making this date an ineffective check of the card’s actual expiration status.

The researchers demonstrated that this loophole works both in the lab and in the wild—at local dining facilities and grocery stores. However, not all credit cards were equally impacted by this loophole and digital wallets included additional security measures making them more resilient against this particular kind of attack. However, separate research from Raza’s lab has found digital wallets are susceptible to other types of exploitation.

The root cause of such issues lies in how payment cards and systems have evolved over time. As we move to smarter but distributed systems, decisions are divided between the card chip, POS terminal, payment networks (e.g., VISA, Mastercard), and the bank. Discrepancies often arise, such as the ability to fool a terminal with a limited view of card expiration when the bank relies on that terminal’s verification. “With the rise of AI, it is becoming increasingly easy for attackers to spot these discrepancies and devise exploits, effectively putting millions of credit cards at risk,” says Anwar.

While the major card companies have been notified of their discovery, Raza says consumers should still abide by safe credit card practices.

“The attack exploits a documented misconception—expired cards are widely assumed inert, so cardholders discard them carelessly,” says Raza. “Always discard your expired card, no matter what. Even if you permanently close your credit card, still monitor the transaction on the closed account.”

Start by demagnetizing the card by slowly running a magnet along the magnetic strip. Next, destroy the embedded chip either with a hammer or scissors. Cut apart your card or put it through a paper shredder, ensuring you cut through any raised letters or numbers. Finally, separate the credit card pieces into different trash cans. For metal cards, contact your company’s customer service department. Fraudulent charges should be immediately reported to your bank.

University of Massachusetts Amherst

TECH

Data centers grow with AI, but energy and water consumption raise alarms

Data centers are proliferating rapidly across the United States, Europe, and other regions of the globe. These massive facilities are essential for digital services and, crucially, for the new artificial intelligence race. However, their growth comes at a high cost: increased electricity usage, vast water consumption, and direct impacts on the cities hosting these structures.

Emmanuel Ferrario, a columnist for *Infobae en Vivo*, explained that data centers do more than just determine how we store and process information; they will also play a pivotal role in the development of AI and, consequently, in the daily lives of millions of people.

According to Ferrario, these facilities function like true digital factories. They house the computers, servers, storage systems, and massive networks responsible for storing and processing virtually everything that occurs in our connected world.

Furthermore, they provide the necessary infrastructure to train and run the artificial intelligence models that are gaining ground across various economic sectors.

Resistance to data centers is mounting...Despite their technological importance, building new data centers is becoming increasingly difficult.

Ferrario notes that this resistance is growing primarily in the United States and Europe. Key concerns include electricity consumption, water usage, noise from the facilities, and the generation of electronic waste.

The United States already hosts around 5,500 data centers and had planned to add nearly 4,000 more over the next three years. However, according to the columnist, the actual pace of construction is falling well short of expectations.

Currently, only about 40% of the capacity needed to meet expansion plans is reportedly under construction.

This slowdown stems from various causes, including a shortage of skilled labor, difficulties in sourcing chips, limitations in electrical infrastructure, and—increasingly—opposition from local communities.

A study cited by Ferrario highlighted the scale of this resistance. Only 23% of respondents in the United States viewed the expansion of these facilities positively, while 48% held a negative view. More than 60% also opposed having a data center located in their own city.

Energy and water have become critical issues...Resource consumption lies at the heart of the debate.

According to Ferrario, a mid-sized data center can consume a daily amount of water and electricity comparable to that used by a city of 30,000 to 50,000 inhabitants.

The issue becomes even more sensitive when governments or utility companies offer incentives to attract these facilities.

The columnist notes that part of this cost may end up being passed on to other consumers in the region. This fuels resistance among residents who fear paying higher prices for the energy required to power large-scale tech operations.

Water presents another major challenge. Many data centers require massive cooling systems to prevent servers from overheating.

Some countries are already attempting to curb this waste. In Germany, for instance, initiatives are underway to capture some of the heat generated by data centers and repurpose it for urban heating systems.

Public pressure is also mounting on authorities.

Ferrario highlighted that several U.S. states have adopted restrictions or begun discussing limits on new facilities. In some regions, local governments are even considering moratoriums to temporarily halt new construction or expansions.

The situation varies significantly across Europe.

Countries with abundant energy supplies may adopt a more flexible approach; France, for example, boasts extensive nuclear infrastructure. Other European governments, however, impose stricter environmental requirements before greenlighting new projects.

This disparity demonstrates that the race for data centers hinges on more than just technology. Energy availability, electrical infrastructure, water resources, and public acceptance have also become strategic factors.

AI is also turning data into a matter of sovereignty...There is yet another concern: who controls all this infrastructure. Concentrating vast amounts of information in a few data centers raises questions regarding digital sovereignty, security, and control over sensitive data.

At the same time, the phenomenon known as NIMBY—short for "not in my backyard"—is on the rise. The term describes people who acknowledge the need for certain infrastructure but do not want its impacts near where they live.

According to Ferrario, something similar is already happening with data centers.

Growth seems inevitable, but it will come at a price...The expansion of artificial intelligence will likely require even more computing power in the coming years. This means new servers, larger facilities, and a growing demand for electricity and cooling systems.

As a result, the conversation is shifting. The question is no longer simply whether or not to build data centers, but how to expand this infrastructure without offloading its environmental and economic costs onto communities.

Governments are exploring various approaches, ranging from strict restrictions to economic incentives and environmental mandates.

Artificial intelligence may be at the heart of the next major technological transformation. Yet, behind the seemingly invisible algorithms lies a deeply physical infrastructure—one that requires vast amounts of energy, water, and space to keep running.

mundophone

TECH

Big Tech and potential stock market vulnerability

Tech giants like Amazon and Alphabet have driven the stock market to record highs in recent years, fueled by the growth of their artificial intelligence and cloud computing businesses. However, in recent months, a significant portion of these two companies' profits has come from an unusual source: the appreciation of their equity stakes in artificial intelligence companies.

More than 70% of Alphabet's quarterly net income came from investments in other companies—specifically Elon Musk's SpaceX—according to a recent regulatory filing and an analysis by Satori Insights, a financial market research firm. SpaceX went public in June in the largest initial public offering (IPO) in history.

Investment gains also accounted for about 65% of Amazon's net income, largely stemming from its stake in Anthropic, a leading artificial intelligence startup that also plans to go public.

These gains highlight a growing vulnerability in the stock market as a whole: the companies continuing to drive the market upward are increasingly dependent on each other's success.

"It’s circular," said Matt King, founder of Satori Insights. "What is funding artificial intelligence is, increasingly, artificial intelligence itself."

Concerns regarding the circular nature of the AI ​​boom have persisted for some time as investors watch dominant tech giants, chipmakers, and AI labs invest in or lend money to one another.

This money is often used to purchase cloud computing products or services from the very companies providing the funding.

Executives in the AI ​​sector have defended these circular funding models. Sam Altman, CEO of OpenAI, described the deals as a creative way to unlock the capital needed to accelerate innovation during a time of rapid transformation. But the investment gains recorded by Alphabet and Amazon show how the financial performance of these companies is increasingly intertwined. They also point to the growing interconnection between the stock market and the broader economy: artificial intelligence is driving growth in both, making the threat of a sharp stock market drop an even greater concern for economic policymakers.

"The stock market has never been merely a reflection of the economy, but it has now become one of the economy's main engines," said King.

Unrealized investment gains are factored into a company's profit based on the value of its holdings at the end of each quarter. Unless the company sells any of these holdings, the gains exist only on paper.

The mechanism works in reverse as well: a drop in the value of investments during a quarter can reduce the profits reported by the company.

Alphabet and Amazon did not respond to requests for comment.

They are part of a small group of technology companies closely watched by the market—dubbed the "Magnificent Seven"—which also includes Microsoft, Meta, Apple, Tesla, and Nvidia. The group has become a benchmark for the tech-driven stock market rally, and many investors prefer to analyze the seven companies collectively.

In total, the Magnificent Seven recorded $315.6 billion in net income for the second quarter. This period corresponds to the three months ending in June for all the companies, except Nvidia, which follows a slightly different fiscal year.

Of that total, $134.6 billion—or about 42%—came from investment gains, according to calculations by King, who adjusted the figures reported by the companies to account for taxes. The other sources of net income are typically revenues from the sale of products and services.

Without these investment gains, the group's profits would have remained roughly at the same level as the previous quarter—meaning they would not have grown—according to King. Microsoft, Meta, Apple, and Tesla did not record comparable revenue from investments.

Nvidia recorded approximately $13 billion in investment gains in the most recent quarter. The chipmaker holds stakes in OpenAI and Anthropic, as well as investments in other artificial intelligence-related companies—including CoreWeave and Applied Digital—that lease access to data centers equipped with high-capacity processors for AI model development.

Investment gains for the seven companies in the second quarter accounted for a much larger share of total profits than the 5% recorded in the previous quarter, according to King's calculations.

Going forward, investment gains from these tech companies' holdings may continue to fluctuate as some of the private companies they invest in go public.

The gains announced by Alphabet, Google's parent company, appear to be largely attributable to its investment in SpaceX, Elon Musk's space exploration and artificial intelligence company. The company recorded nearly $80 billion in pre-tax profit from investments in restricted equity securities and reported holding $94.1 billion in SpaceX shares.

mundophone

 

TECH

Life after AI: Why cory doctorow’s brilliant guide is an outdated map, lacking answers

What does life after AI look like? Cory Doctorow’s new book claims to know, and a pointed review in The Conversation claims he has got it wrong. Doctorow’s The Reverse Centaur’s Guide to Life After AI arrived in June 2026 promising a route out of the hype cycle, and by 10 August reviewer Michael Noetel had branded it an outdated map that fails to answer its own question.

This opinion piece weighs both sides of that argument, because we think each camp is holding half of a genuinely useful book. We cover AI releases weekly on our AI models and tools hub, from OpenAI’s mid-August ChatGPT update to open-weight model launches, and we build systems that use natural language processing every working day — so the question of life after AI is not academic for us or for our clients. Here is where the book helps, where the review lands, and what neither offers a business trying to plan.

The phrase life after AI sounds apocalyptic, but Doctorow means something narrower and more interesting. His argument is that today’s generative AI industry is a classic investment bubble, and that the important question is not whether the bubble pops but what remains afterwards. Life after AI, in his telling, is the period after the money runs out — when the hype evaporates, some companies collapse, and society decides what to salvage from the wreckage.

A question the whole industry is avoiding...That framing deserves more credit than it usually gets. Almost every AI vendor pitch assumes a straight line from today’s capabilities to permanent transformation. Almost no vendor deck contains a slide titled “what happens to your workflow if our funding dries up”. Doctorow has been asking that second question since his 2023 Locus essay on what kind of bubble AI is, and the book extends it to a full theory of life after AI: which tools survive, who ends up owning them, and whose labour gets reorganised around them.

Why the answer matters in 2026...The stakes have only grown since he drafted it. AI infrastructure spending now props up a meaningful share of stock-market value and capital expenditure, which means the shape of life after AI is a macroeconomic question, not a tech-blog debate. If the spending stops abruptly, the consequences reach pension funds and payrolls far outside Silicon Valley. A serious guide to life after AI would therefore be genuinely useful. The dispute is whether Doctorow has written one.

The book, published by Verso in June 2026 at a brisk 240 pages, is organised around one memorable idea. A centaur, in the old chess-computing sense, is a human assisted by a machine. A reverse centaur is a human conscripted into assisting a machine — the delivery driver whose routes, breaks and bathroom stops are dictated by an algorithm, or the moderator cleaning up after an automated feed.

The reverse centaur, explained...Doctorow’s fear is that the AI economy is built to mass-produce reverse centaurs. The industry’s most valuable product, he argues, is not any model but a story told to investors: that workers are about to be obsolete, so firms should buy the machine and demote the human to its minder. Whether the model can actually do the job matters less than whether the boss believes it can.

What the book actually recommends...The consolation Doctorow offers is that the bubble will burst before the worst version arrives, and that life after AI can be shaped by policy: antitrust enforcement, interoperability rights, worker protections, and picking through the productive residue — cheap GPUs, unemployed statisticians, open-weight models — once prices collapse. The tools should work for us, he writes, not the other way round. As a diagnosis it is vivid and frequently persuasive. As a plan, the reviewer argues, it is where the book runs out of road.

Readers of Doctorow’s earlier work will recognise the machinery. The book is effectively the enshittification thesis — platforms decay once they stop competing for users and start squeezing them — applied to the biggest capital buildout in tech history. That lineage is a strength: it grounds the AI argument in a pattern he has documented across search, social media and marketplaces for a decade, and it explains why Brian Eno and others blurbed the book as the clearest guide to the moment.

Noetel’s review makes two central charges. First, the book was drafted in mid-2025 and the ground has moved: dismissing coding assistants and agents as pure hype reads badly in a year when those systems shipped real, measurable work. Second, the guidance is thin — his summary of the book’s advice is “pick a side and boo”, which is a cruel line precisely because it is not entirely unfair.

Fourteen months is a long time in AI...The timeline problem is structural, not a matter of sloppiness. A book drafted in mid-2025 reached shops roughly twelve months later and met its most-read review about fourteen months after drafting. The chart below shows why that gap hurts a book making claims about fast-moving capabilities.

Months from mid-2025 drafting to each milestone

Manuscript drafted (mid-2025) 0 months

Publication (June 2026) ~12 months

Critical review (August 2026) ~14 months

The claims that aged worst...Noetel’s sharpest evidence is concrete. Capabilities Doctorow waves away as marketing had, by review time, produced results that are hard to dismiss — and a reader relying on the book alone would not know any of it happened.

A guide that declines to guide...The deeper complaint is the missing second half. A reader finishing the book knows what Doctorow is against, but not what to do on Monday. Noetel contrasts it with scenario-planning work that names concrete levers — compute disclosure, capability evaluations, chip tracking — and concludes that on the question of where AI is actually heading, the book “will leave you misinformed”. For a volume whose subtitle promises to teach you how to think about AI before it is too late, that is the most damaging sentence a reviewer could write.

Here is where our opinion parts company with the review’s harshest reading. Strip out the capability predictions and the book’s economic core survives contact with 2026 remarkably well — because it never depended on models being weak. In his widely shared essay on the coming AI economic shock, Doctorow assembles figures that no capability breakthrough has answered: the gap between what the industry earns and what its infrastructure requires keeps widening.

The revenue gap nobody has closed...The sums are stark. Doctorow cites Morgan Stanley’s estimate that the industry’s real annualised revenue sits near 45 billion dollars, against Sequoia partner David Cahn’s calculation that current data-centre spending needs about 800 billion dollars in revenue to pay back, and Bain’s projection that profitability requires some 2 trillion dollars a year by 2030. One takeaway sentence before the numbers: revenue is running at roughly a fortieth of what the buildout assumes.

Better models do not fix broken unit economics...This is the part of the life after AI argument the review never really engages. An agent solving a maths problem is a scientific milestone; it is not 755 billion dollars of new annual revenue. If anything, stronger capabilities deepen the hole, because frontier training and inference costs climb with every generation. You can believe the models are genuinely impressive and still believe the financial structure carrying them is unsustainable — that is precisely Doctorow’s position, and calling the map outdated does not redraw the terrain. Life after AI remains a live scenario for any planner who can read a balance sheet.

by Michael Noetel

CANON

Canon toner printers honored with Keypoint Intelligence energy efficiency awards

Canon’s toner printers achieved a dual energy efficiency distinction for the 2026–2028 period, awarded by the independent laboratory Keypoint Intelligence. The analytics firm published the results on August 11, 2026, focusing on the power consumption of professional office equipment. The assessment ranked the brand first in both A3 and A4 format categories simultaneously—an unprecedented feat in the testing body's history.

The technical analysis compared the brand's equipment performance against over two hundred competing models throughout a five-year testing cycle. Laboratory data revealed power consumption levels below the market average across all operational tiers.

The brand's A4 models demonstrated 47% greater energy efficiency than the average of direct competitors evaluated during the same period. Keypoint Intelligence noted that this competitive advantage extends to the entire range of the brand's A4 offerings.

For systems designed for the A3 format, the electricity savings margin stood at 18% compared to other tested brands. Measurements covered the equipment's daily lifecycle in an office environment, quantifying electricity consumption during active printing, idle states, and extended sleep modes.

This calculation allowed for the projection of each device's annual power consumption based on real-world workload conditions. The evaluating body confirmed Canon toner printers' leadership in balancing operational performance with energy savings.

Tests took into account various usage scenarios...The analysis spanned five years and included A3 and A4 devices from different manufacturers. Factors considered included color and black-and-white printing, simplex and duplex modes, recovery times, and power consumption during periods of inactivity.

Keypoint Intelligence awarded the two prizes to Canon based on the overall energy performance of the tested devices, while also factoring in productivity levels and startup and recovery times.

Marc Bory, Director of Marketing and Innovation for the EMEA region at Canon Europe’s Printing and Integrated Services Group, states that energy efficiency has been a key consideration in device development—specifically through low-temperature toner technologies and reduced startup times: “Sustainability lies at the heart of our business and product design strategy, shaping how we develop, deliver, and extend the lifecycle of our technologies.”

The award covers the 2026–2028 period and applies to both A3 and A4 toner-based device categories.

What methodology did Keypoint Intelligence apply? The laboratory testing protocol monitored approximately 200 devices from various manufacturers, focusing on continuous document workflows. Technicians measured energy usage during cold startups, recovery from sleep mode, and the processing of complex files. The sample set dedicated to the Japanese manufacturer comprised 20 specific models, representing the imageFORCE, imageRUNNER ADVANCE DX, and i-SENSYS lines. The final report highlighted the speed of recovery from sleep mode without incurring a penalty on overall energy consumption.

Mechanical design and the development of thermal components were key factors in achieving these results. Stable performance under heavy workloads ensured the technical validation of Canon’s toner-based printers.

Evolving European regulations impose strict electricity consumption targets on office equipment intended for corporate and institutional tenders. The integration of low-temperature fusing technology has reduced the power required to fix toner to the paper.

Marc Bory, Director of Marketing and Innovation for the EMEA region within Canon Europe’s Printing and Integrated Services Group, stated:

"Sustainability lies at the heart of our business strategy and product design, shaping how we develop, deliver, and extend the lifecycle of our technologies. As regulatory expectations evolve and organizations place greater emphasis on their sustainability goals, we continue to innovate with features such as low-temperature toner technology and fast device startup times, helping customers reduce energy consumption. We are delighted to receive this significant recognition from Keypoint Intelligence, which reflects our ongoing commitment to delivering high-performance, sustainable innovations to the industry."

This technical strategy addresses the need to reduce direct operating costs within IT infrastructures. Companies are looking to upgrade their fleets with certified devices to meet carbon footprint targets without sacrificing productivity.

Lowering power consumption during idle periods translates into ongoing cost savings for organizations. The independent validation of Canon’s toner-based printers reinforces energy efficiency as a central criterion when selecting professional printing solutions. For more information on Canon Europe's approach to sustainability, visit: https://www.canon-europe.com/sustainability/

mundophone

DIGITAL LIFE

Brazil ranks second in stolen cookies by cybercriminals, study finds

Brazil holds second place in the global ranking of stolen cookies, according to new research by cybersecurity firm NordVPN. Globally, the study identified over 52.4 billion stolen cookies—sourced from historical infostealer data and the NordStellar platform—across 250 countries and territories over a one-year period (June 9, 2025, to June 8, 2026). Brazil accounted for 2.83 billion of these.

India leads the list with 4.68 billion. The United States ranks third with 2.43 billion. Rounding out the top five are Indonesia (2.10 billion) and the Philippines (1.93 billion). When adjusted for population size, Uruguay, Peru, and Chile showed the highest concentrations of stolen cookies per capita.

According to the study, browser cookies have become the primary currency for cybercriminals, appearing 4.6 times more frequently than all other types of stolen data combined—including passwords, files, and payment card details.

The data further reveals that the most frequently stolen records were not linked to banking access but to popular platforms used on a daily basis. Google topped the dataset with 11.78 million stolen records, followed by Facebook (8.10 million) and Microsoft (7.85 million). Services such as Twitch, Netflix, YouTube, Reddit, and Bing also frequently appeared among the detected exposures.

Cookies are small data files that websites store in a user's browser to recognize them and retain information across different visits. They help maintain login sessions, language preferences, shopping cart items, and browsing settings, and can also support analytics and advertising tools. NordVPN explains that, while these files do not necessarily store the password itself, some contain identifiers that prove an authenticated session. If a criminal steals an active session cookie, they can take over the account without re-entering credentials. This attack, known as session hijacking, remains possible until the user terminates or invalidates the access.

“We have observed a fundamental shift in how hackers operate. It is no longer just about discovering a password. Now, they seek to steal the digital key that is already turned in the lock,” said Marijus Briedis, the company’s Chief Technology Officer.

Since more than 96% of the analyzed infection records originated from devices with active security software, the study highlights the need for users to combine traditional protections with rapid responses.

Briedis recommended ending sessions, clearing browser caches, and using session monitoring tools to invalidate stolen digital keys before criminals can exploit them.

“Cookie theft demonstrates that cybersecurity is not just about prevention. It also depends on how quickly you react when something goes wrong. If someone steals a session cookie, logging out of the affected accounts and renewing the session can significantly limit the damage,” the executive added.

Infostealers are harvesting cookies on a massive scale...Research by NordVPN shows that browser cookies have become a primary target for infostealer malware. Over the course of a year—from June 9, 2025, to June 8, 2026—researchers identified 52,389,324,619 stolen cookies within infostealer datasets.

Among the types of stolen data included in this dataset, cookies appeared most frequently in terms of raw numbers. The same dataset includes 6.75 billion login autofill entries, 2.17 billion files, 1.55 billion credential records, and 607.9 million passwords, as well as 341.4 million unique victim email addresses and 1.09 million payment card records. In total, infostealers collected 4.6 times more cookie records than all other listed data types combined.

This scale illustrates why browser cookies have become valuable targets. Some cookies help websites remember your preferences or keep you logged into your account, while others are used for advertising, tracking, or analytics. When infostealers harvest cookies from an infected device, they can reveal your online activities, helping criminals build a profile of your interests. Alternatively, in the case of session cookies, they enable criminals to hijack active login sessions.

People typically contract infostealer malware through unsafe downloads, fake software updates, malicious ads, phishing links, cracked apps, game cheats, or infected email attachments. The services mentioned in this research (such as Netflix, YouTube, Twitch, or Google) are not sources of malware themselves; rather, they are examples of platforms where accounts can be compromised after browser data is stolen from an infected device.

Tracking and advertising cookies account for the largest share of cookies stolen in this study. While these cookies may not grant cybercriminals direct access, they can still reveal information about your online behavior, interests, and browsing patterns.

Tracking can also involve various types of cookies you might not recognize:

Third-party cookies. These are placed by sites or services other than the specific site you are visiting and are used for advertising, retargeting, and cross-site tracking.

Supercookies. These are highly persistent tracking files that can store identifiers outside the browser's standard cookie storage, making them harder to delete than standard browser cookies.

Zombie cookies. These tracking cookies can recreate themselves even after being deleted by the user, making them highly intrusive regarding user privacy.

That said, these findings show that stolen cookies do not need to unlock an account to be valuable. At this scale, even cookies used for advertising, analytics, and tracking can reveal to cybercriminals which sites the victim visits, what their interests are, and how they navigate the web.

mundophone