The word ‘hacker’ comes loaded with a cliched image: A hoodie-clad loner hunched over a keyboard in a room lined with monitors. The stereotype stuck for a reason. And for decades hacking really did come down to how well a hacker could operate a computer.

That trend might change. The next generation of attacker may have more in common with a cat burglar than a code monkey. They slip physically close to a target instead of typing their way in. Some of the sharpest new attacks skip the login screen entirely. They reach straight into the hardware, sometimes from the other side of a wall.

The researchers behind the discovery are led by Prof. Han Jun of KAIST, working with researchers from the National University of Singapore and Zhejiang University in China. At NDSS (Network and Distributed System Security) 2026, they demonstrated that an antenna trained on a running computer can capture the faint electromagnetic leakage from its GPU. This new technique was enough to reconstruct the layer structure of the AI model inside, with up to 97.6 percent accuracy. They call the technique ModelSpy, and it works even through a wall.

If this technique fell into the wrong hands, stealing a company’s AI would hardly look like an attack. Someone could walk down the hallway with a 20-liter backpack of antenna and receiver tucked inside and walk back out with the blueprint of the AI model running on that floor. No malware, no breached server, no exposed source code, not a single line of planted code. Just the AI’s design, leaking out as electromagnetic noise. The research won the Distinguished Paper Award at NDSS 2026.

Stealing AI without touching the computer

AI has gotten valuable enough that plenty of people are now trying to figure out how to steal it. None of the usual paths are easy. You can break into the company’s network and plant malicious code. But planting anything on a hardened corporate server is hard, and getting caught is easy. What about going after the hardware directly, skipping the software entirely?

The most promising example is the side-channel attack. Instead of breaking in, an attacker just listens. Any running computer leaks signals like small flickers in the current it draws, the heat coming off the chips, the hum of its fans, the faint vibrations of its components. Read those signals carefully enough, and they can tell you what the machine is doing inside. Researchers have been chasing that idea for decades.

Some of this work has been done. Researchers have clipped sensors onto the power lines feeding a GPU, and they’ve stripped chips bare to probe their internals directly. The catch is always the same: you have to be standing next to the machine, hands on the hardware.

The KAIST researchers wanted to know if they could pull off a side-channel attack from a distance by listening to it. The idea was to reassemble the signals that leak from a computer as it runs, and work backward through them to uncover the architecture of the AI inside. But how do you reconstruct a model from a few stray waves of static? The answer comes down to what GPUs unwittingly emit while they compute.

A running GPU is electricity in constant motion, current racing through millions of circuits as they pass signals back and forth. Nothing in a GPU ever rests. The memory clocks keep the rhythm of data access, voltage regulators hold the power steady, refresh circuits rewrite the memory before it forgets itself. Each of these subsystems gives off its own electromagnetic signature as it works. Engineers call them carrier waves.

Those carrier waves are not steady. The moment a GPU starts running an AI model, its electromagnetic emissions begin to shimmer. They rise and fall as the current through the chip shifts to match whatever the model is computing and however often it needs to reach into memory. The GPU’s memory-access patterns are imprinted like traces onto the waves it gives off.

So those memory patterns ride on the carrier waves like a signature of the AI itself. A modern model is a stack of layers, each one feeding its output into the next. The final answer falls out of the top of the stack. The key is that different kinds of layers hit memory in very different ways. Some pull in huge chunks of data at once for heavy processing. Others make short repeated trips to grab a little at a time. Read the carrier waves carefully enough and in principle you can trace those memory patterns backward to reconstruct which layers ran in what order. Pulling this off in practice is another matter.

But working backward from those traces to the actual AI behind them is the hard part. The space of candidates is enormous. Models vary wildly in how many layers they have and what kinds. Each layer brings its own hyperparameters, with the possibilities multiplying until they grow unmanageably large. The researchers estimated that even under a simplified setup of just five layer types across a 100-layer network, the number of possible combinations runs to about 10 to the power of 70. For reference, the observable universe holds roughly 10 to the 24th power stars. Testing every candidate one by one is obviously off the table.

So they set out to fight AI with AI. The researchers built a separate analytical model, trained to take in electromagnetic patterns and guess at the architecture they came from. The trick was to keep the model from trying to read the whole signal in one bite. Instead it works in layers, moving from the broad shape of the waveform down to the fine grain. First the model reads the overall flow of the signal along with its surrounding context, since a single instant of waveform tells you almost nothing on its own. Then it slices the signal into thin time windows and classifies each slice by layer type. Lastly, it estimates the hyperparameters that go with each layer. All three stages were trained together as one piece rather than being bolted on top of each other.

What pushed the technique past the bar was the training data. The analytical AI needed clean and abundant examples to learn from, but real electromagnetic recordings were noisy and patchy — the kind of data it would face in an actual attack. So the researchers turned to something else. DRAM traces are time-stamped records of how a GPU’s memory is accessed while it runs an AI model. Since the GPU’s electromagnetic emissions are nothing more than DRAM activity riding on signal strength and leaking outward, the two are essentially mirror images of each other.

The catch is where they come from. DRAM traces are captured directly inside the GPU, which makes them far cleaner than anything an antenna can pick up from outside. The researchers trained the model on both sources in stages. The AI first built its foundation on clean and plentiful DRAM data, then sharpened its real-world instincts on electromagnetic signals. The electromagnetic data was harder to collect but closer to actual attack conditions.

To test the attack, the researchers ran it against five everyday Nvidia GPUs (RTX 3060, 3060 Ti, 3070, 4060, 4060 Ti). All of it is gear you can buy off the shelf. Their attack kit was equally ordinary. A 5GHz antenna and an electromagnetic receiver were the only equipment, both small enough to fit inside a 20-liter backpack. The goal was to mimic what an actual attacker would do. They had to capture the emissions from across the room with no way of touching the machine.

The DRAM trick paid off. Pretraining on DRAM traces before fine-tuning on electromagnetic recordings beat training on electromagnetic data alone by a wide margin. Layer segmentation accuracy climbed from 92.5 percent to 97.6 percent. The task is to identify which layer each point in the signal belongs to. Accuracy at estimating each layer’s hyperparameters rose from 86.2 percent to 94.2 percent. And the gains held across all five GPUs.

Distance did not kill the attack. Using an RTX 3060 Ti as the test target, the researchers backed the antenna farther and farther away and watched what happened to the numbers. At five meters, layer segmentation accuracy held at 86.7 percent. Hyperparameter estimation remained at 81.7 percent. The researchers estimate the technique stays usable out to about six meters. The signal weakens as you back away, but enough of its traces survive to keep the analysis going.

The same held when they put a wall between the GPU and the antenna. The researchers ran the test through glass, then wood, then concrete. Layer segmentation accuracy stayed at roughly 96 percent in every case. The electromagnetic waves leaking from the GPU weren’t fully blocked by the walls. They passed partway through, holding on to enough signal for the model to read.

ModelSpy has clear limits though. It cannot reach an AI model’s weights, the numerical values learned during training. It cannot pull out the training data or the source code either. What it captures is the architecture, and only the architecture. That does not mean there is no cause for concern. A stolen blueprint alone can be enough for a hacker to design a dangerous attack.

Once an attacker has the layer structure and hyperparameters, they can build a model that behaves like the target. The technique is known as a surrogate model. Instead of going at the real system blind, the attacker can run any number of attacks against the surrogate first. The effective ones then get turned on the actual AI. A model that closely mimics the target’s inner workings turns any attack into something much closer to a precision strike.

Take the adversarial example attack. Imagine someone going after the traffic-sign recognition system in a self-driving car. To the human eye it looks like an ordinary stop sign. Stick a small piece of tape on its face or paint a subtle pattern across it and the AI can be tricked into reading it as a speed limit sign or a straight-ahead sign. A car that misreads its signs can accelerate through an intersection where it should stop, or turn into the wrong lane.

The researchers used ModelSpy itself to put the surrogate-model idea to the test. They built a surrogate from the architecture ModelSpy had estimated, then used it to test adversarial attacks. These are attacks designed to make an AI misjudge what it sees. Attacks built on ModelSpy’s estimate performed almost as well as attacks designed with full knowledge of the real model. The gap averaged just four percentage points.

Copying the AI itself may be on the table too. In a so-called model extraction attack the attacker hammers the target with queries to capture its outputs and trains a replica on what comes back. It is imitation learning in effect with a stolen AI as the teacher. The catch is knowing what kind of model to imitate. Without the architecture, building something that performs as well as the original takes far more data and far more compute. The result is usually off anyway. With the architecture in hand, a close replica is fast and cheap.

A copyable AI is also a leakier AI when it comes to privacy. A surrogate model also sharpens what is called a membership inference attack. This is a way of working backward from a model’s behavior to figure out who and what was in its training data. The attack rests on a simple quirk. An AI responds in subtly different ways to data it was trained on than to data it has never seen. The distribution of its outputs shifts just a little when it encounters something it has seen before. An attacker who can spot that shift can infer whether a specific piece of data was part of the training set.

Once ModelSpy hands them a surrogate that closely matches the target’s architecture, they can do that inference with far greater precision. Sensitive training data makes the threat far worse. Medical AI is the obvious example. A membership inference attack against such a model can be devastating. Imagine a hospital running a diagnostic AI that was trained on its own patients’ records. Once an attacker confirms that a specific person’s record was part of that training set, they learn more than the fact that the person was treated at that hospital. They also learn by implication that the person may have the particular condition that AI was built to diagnose.

The researchers have proposed two countermeasures. The first is electromagnetic jamming: deliberately blanket the GPU’s signal with artificial noise so the real emissions can’t be picked out. The second is an obfuscation technique that runs decoy computations alongside the real ones to mask the traces of actual AI inference. Neither is a perfect solution. Careless jamming can spill over into the Wi-Fi band and knock out office communications. Decoy computations slow the GPU down and drive up operating costs. Still, the two approaches give GPU manufacturers and AI companies a place to start.

ModelSpy suggests that safeguarding AI may have to extend well beyond the computer itself.

“This research demonstrates that AI systems can be exposed to new forms of attack even in the physical environment,” said Prof. Han. “To protect critical AI infrastructure such as autonomous driving and national facilities, it is essential to build a cyber-physical security framework that encompasses both hardware and software.”

The story was produced in partnership with our colleagues at Popular Science Korea.

The post How hackers can break into AI servers with an off-the-shelf antenna appeared first on Popular Science.

Google launched its own email service all the way back in 2004 (remember the hype around a free 1GB of email storage space?). In the years since, it’s become the default email service for many of us—in part because of its close ties to so other Google apps, like Google Drive, Google Maps, and Google Photos.

We’ve also seen plenty of competing products launch over the last two decades, so if you’re thinking about leaving Gmail, you have plenty of other options. Apple and Microsoft are two of the big names that will gladly take over the responsibility of managing your inbox.

Then there’s Proton Mail, part of the Proton suite of products that prioritizes privacy and security. We’ve previously compared Proton Docs and Google Docs, and here we’re going to take a look at how Proton Mail stacks up against Gmail. It may be worth your while to switch, especially if you’re unsure about Google’s privacy policies.

Gmail vs Proton Mail: The basics

Both services are available on the web, and have dedicated apps for Android and iOS. Both have free options, with premium plans also available: Proton Mail gives you 1GB of storage for free, while Gmail gives you 15GB (though bear in mind this is also shared with Google Drive and Google Photos).

Paid plans start at $1.99 a month for Gmail and $4.99 a month for Proton Mail, but it’s hard to do a straight comparison, as a lot of other upgrades are included. Google gives you more AI features as well as more storage room, for example, while Proton gives you more usage across its VPN, Calendar, and Drive tools in addition to the extra cloud storage.

If you prefer to use a third-party email client like Apple Mail or Outlook, this is easily done on Gmail and only takes a few steps. With Proton Mail, it’s more involved: You need to sign up for a premium subscription, and use the Proton Mail Bridge app. This ensures end-to-end encryption, so not even Proton itself can read your emails (this isn’t something Gmail offers by default).

Gmail vs Proton Mail: Key features

When it comes to key features, both Gmail and Proton Mail have plenty to offer, though with Proton Mail your use of labels and filters is restricted on the free plan. It supports folders though, which Gmail doesn’t. And if you pay for Proton Mail, you can set up multiple email addresses to work through one inbox, which again Gmail doesn’t support.

It’s similar with the email scheduling and snoozing features, and automatic email forwarding to another inbox. This is all free in Gmail, and requires a subscription in Proton Mail. There is also an undo send feature on both platforms, free of charge, that you can use to quickly bring back messages you’ve sent in error.

Ideally, you need to be paying for Proton Mail: Otherwise you run into restrictions on filters, folders, and labels, and the number of messages you can send (150 per day). With Gmail, all of this is supported by advertising and data collection This is the distinction Proton focuses on: You’ll never see a single advert inside Proton’s products.

Gmail vs Proton Mail: Interface

Both Gmail and Proton Mail offer a clean, modern-looking app interface that’s easy to navigate around and intuitive in the way it works. Both platforms let you customize the interface too—so you can tailor the look and feel to suit yourself (Gmail does offer more in the way of tweaks, however).

Both email platforms support keyboard shortcuts on the desktop, which can be very helpful for powering through emails and clearing out your inbox. There’s also well-done integration with the other apps offered by these companies—including Google Drive and Proton Drive, and Google Calendar and Proton Calendar.

You could argue that the Gmail app is a little bit more polished, especially on mobile, but there’s not much in it. Both platforms support conversation grouping, where emails from the same thread are bunched together for easy reference (but both also let you turn this off, if you prefer the traditional approach).

Gmail vs Proton Mail: Privacy

While Gmail may be ahead on the scorecard up to this point, it’s here that Proton Mail strikes back. The Proton offering is way ahead here, and offers full end-to-end encryption for your emails, plus password-protected emails, and expiration dates for emails.

Gmail provides some of these features in a more limited way, but they’re not enabled by default, and aren’t as comprehensive as the Proton Mail equivalent. While Google’s email servers are encrypted, Google holds the decryption keys—so messages can be accessed by Google or agencies approved by Google. The full, end-to-end encryption that Proton Mail provides means no one but you can read your emails.

Both these platforms do well in terms of anti-spam and anti-virus protection for your inbox. But on other privacy and security features, Proton Mail wins: The VPN bundled with all plans (even the free one), for instance, and the complete absence of ads.

Gmail vs Proton Mail: Verdict

As you can see, the primary reason to switch to Proton Mail from Gmail is privacy and security. And if that’s what’s most important to you, then you’ll probably be okay with paying a few dollars more a month to get those features, and to make sure you’re not being tracked or advertised to in your inbox.

There’s still a lot to be said for Gmail though. It’s ubiquitous and compatible with a host of third-party apps and tools, it’s got loads of customization options and other features to play around with, and if you can stick under the 15GB storage limit then you get unlimited use of everything for free, too.

You also need to think of the inconvenience cost, of course, and it may take a while before all your contacts are right up to date with your new email address. Of course, if there are some contacts you’d rather not hear from again in the future, then switch away.

The post Gmail vs Proton Mail: Is it worth switching if you care about privacy? appeared first on Popular Science.

These days, it’s nearly impossible to traverse the web without leaving some trace of your activity. That’s thanks to a panopticon of cookies, keystroke loggers, fingerprinting, tracking pixels, and probably some other horrors that haven’t even come to light. Maybe that sounds paranoid, but it’s exactly what researchers in Austria uncovered in bombshell new cybersecurity research.

According to the recently released paper, first spotted by Ars Technica, researchers have uncovered a type of no-interaction attack that websites can easily run to access data stored in your computer.

It’s called FROST, which stands for “fingerprinting remotely using OPFS-based SSD timing.” It’s a mouthful for sure, but it basically allows malicious websites to spy on your computer activity, all without installing any software or tricking you into clicking sketchy email links.

Per the researchers, it works by taking advantage of your computer’s solid state drive (SSD), the internal storage devices which have largely taken over from magnetic hard drives on the consumer market. Whenever you visit a site, your computer’s SSD starts buzzing with activity, allowing webpages to store temporary files for your browsing pleasure.

FROST attacks take advantage of this by creating a massive file — we’re talking several gigabytes — which functionally blocks your computer from moving what it sees as temporary web data out of the SSD. While that mammoth file is being processed, however, the malicious website is able to probe the timing of incoming data from other sites, generating data which can then be analyzed through a machine learning model to predict what else you’re doing online.

While “predict” suggests the attacker is guessing, the FROST method is scary good at identifying what a victim’s doing on their computer. Researchers write that by using this technique, their machine learning model was able to predict which sites a user would access with an accuracy rate of 88.95 percent, and could accurately predict accessed applications 95.83 percent of the time.

Worse, the whole thing works regardless of what browser you use — because it works through your SSD, an attacker can theoretically track your web browsing on Firefox based on a website accessed via Google Chrome. Researchers only experimented with the technique on Mac and Linux devices, but caveated that Windows devices are not immune.

“In principle, it would be possible to train a model on any system activity that reliably generates SSD accesses,” the study’s lead author, Hannes Weissteiner, told Ars.

While FROST represents the kind of vulnerability that probably needs to be patched by web developers, Ars notes that you can mitigate the risks by closing website tabs as soon as you’re done with them. It isn’t much, but it could prevent you from becoming the next victim of a scary new kind of cyberattack.

More on web development: New Website Detects Apocalypse If Billionaire Jets Start Fleeing en Masse

The post Websites Are Spying on Your Solid State Drive appeared first on Futurism.

Monstrous defeats keep coming for Mike Lindell, the notorious entrepreneur behind the MyPillow brand and one-time advisor to Donald Trump.

According to Straight Arrow News, a clique of hackers known as “Play” is claiming to have accessed a huge chunk of private data from MyPillow, which it’s now holding hostage. Per the outlet, which viewed a communique from the gang, the hackers now have access to “private and personal confidential data, clients’ documents, budget, payroll, IDs, taxes, finance information and etc.”

Lindell’s company has been given until Friday, May 29 to respond — or else its data will be published online, the hackers threatened. The amount they’re trying to extort hasn’t been disclosed, and neither the hackers nor MyPillow responded to Straight Arrow‘s requests for comment.

Play first appeared in 2022, when it orchestrated cyber attacks throughout the US, Brazil, Germany, and Switzerland, among others. Their targets tend to be those associated with government functionaries, like the Argentinian judiciary, and an IT firm contracted by the Swiss Federal Department of Finance.

In that vein, a successful attack on Lindell would be a major trophy. The entrepreneur first met Trump in the run-up to the 2016 presidential election, a relationship which blossomed as the would-be president ferried the increasingly crankish Lindell around rallies across the country.

In 2020, Lindell briefly served as Trump’s reelection campaign chair, then nearly ran for governor of Minnesota with his blessings. Later in November of 2022, Lindell ran for Chair of the Republican National Committee, though he lost after receiving only 2.4 percent of the total votes.

Now in 2026, the MyPillow founder is once again running for governor of Minnesota, having filed all the corresponding paperwork — which is more than he did last election cycle. That said, the hack comes as his finances and personal life are now under perhaps more scrutiny than they’ve ever been. Given his ties to Trump, who appears to be backing him again in the 2026 election, there could conceivably be some fascinating details lurking in the MyPillow archives.

Whether Lindell can pony up to keep them hidden remains to be seen: in April of 2025 he admitted that he didn’t even have “5 cents” to his name, owing to an avalanche of civil suits and federal investigations stemming from his political antics.

More on hacking: Riot Games Denies Using Anti-Cheat Software That Bricks Hackers’ Computers

The post The MyPillow Guy’s Entire Business is Being Held Hostage by Hackers appeared first on Futurism.

Rest easy, paranoid gamers. Riot Games says its Vanguard anti-cheat tool won’t “brick” the computers of hackers ruining everyone’s fun in its multiplayer games. And that’s too bad, since cheaters deserve to suffer at least twice as much as the beleaguered gamers that willingly subject themselves to grinding MMR in one of the company’s titles already do.

The brouhaha stems from a Vanguard update the “Valorant” and “League of Legends” maker released last week that targets notoriously hard to detect direct memory access (DMA) cheats, which bypass security measures by using an external device to write directly to a computer’s RAM.

Responding to another post about its new anti-cheat measure, the Riot social media account tweeted a picture of a bunch of rounded-up computer hardware that was reminiscent of a drug bust haul. It was appended with a provocative caption: “congrats to the owners of a brand new $6k paperweight.”

This turned out to be a PR landmine. The tongue-in-cheek post was interpreted as Riot bragging that it now had the ability to remotely brick your computer, creating an explosion of angry posts so overwhelming that the company scrambled to propitiate the mob banging on its gates.

“There’s been a wave of claims by cheaters about Vanguard ‘bricking’ their PCs, so let’s clear that up: Vanguard does not damage hardware or disable your devices,” it wrote in a lengthy X statement less than a day later.

“The photo we posted is a picture of cheat hardware devices that are sold explicitly for cheating in VALORANT (not normal PCs or PC components),” it added. “Through our latest updates, Vanguard now makes those devices worthless for VAL, but does not in any way brick PCs or PC components or PC software.”

congrats to the owners of a brand new $6k paperweight https://t.co/3rjZVQntrc pic.twitter.com/fS3JC0FL0p

— Riot Games (@riotgames) May 21, 2026

The backlash is a reflection of how controversial Riot’s Vanguard software remains years after it was first released in 2020. A so-called kernel-level anti-cheat, it requires gaining the highest level of access to a part of the operating system where its most crucial processes run, a privilege that most software does not ask for. 

While this makes Vanguard adept at rooting out cheats running on someone’s system, it also in the eyes of critics makes it alarmingly invasive. And beyond potential privacy concerns, many users have complained that Vanguard causes all sorts of technical glitches on their machines, though it’s impossible to corroborate all those claims.

Unfortunately for Riot, those critiques aren’t about to die down after its latest DMA update and accompanying disastrous post. And unfortunately for the Vanguard-skeptical, Riot is sticking to its kernel-level guns.

“We’ll keep investing in anti-cheat to protect competitive integrity, and we’ll keep being as transparent as possible about how those systems work,” the company said in its statement.

More on cybersecurity: Google Alarmed by Formidable AI-Powered Zero-Day Cyberattack

The post Riot Games Denies Using Anti-Cheat Software That Bricks Hackers’ Computers appeared first on Futurism.

United flight 236 from Newark to Palma de Mallorca on Saturday night was forced to turn around just an hour after takeoff due to security concerns around a Bluetooth signal. Multiple Redditors claimed to be on the flight and reported that the crew repeatedly requested passengers to turn off their Bluetooth. According to one poster, the crew issued a one-minute warning, saying that two devices were still active.

One Redditor reported flight attendants making comments like, "This little joke is ruining it for everyone."

An archived recording from Air Traffic Control (embedded below) confirms that the root of the issue was the name of a disc …

Read the full story at The Verge.

Cybercriminals created thousands of fake FIFA websites and phishing systems designed to steal World Cup tickets and financial information.

Plus: A ransomware group is now stealing data in person, BusPatrol wants to hand its license plate surveillance data to the cops, and more.

Da chi chiede una “pausa globale” a chi fa lobby tra i politici fino alle proteste fuori dagli uffici delle big tech: ecco chi sono le voci che manifestano contro la tecnologia del secolo

Crypto bigwigs are some of the biggest - and in some cases, easiest targets for kidnappers, conmen, and blackmailers alike

The website, which compares human beings to extraterrestrials, touts arrest numbers from the Trump administration’s sweeping immigration crackdown. But some of its details are really out there.

Authorities in the Netherlands said they dismantled a botnet that comprised more than 17 million devices and were managed by 200 servers in a joint operation by the police and the National Cyber Security Center.

The action, announced Thursday, came about after a security researcher reported the sprawling network to authorities. The host infrastructure was located in the Netherlands.

Used for criminal purposes

“The police then seized several botnet servers from a hosting provider for investigation,” the NCSC said. “The botnet was taken offline by the provider because it was used for criminal purposes.”

Read full article

Comments

© Aurich Lawson / Ars Technica

Authorities in the Netherlands said they dismantled a botnet that comprised more than 17 million devices and were managed by 200 servers in a joint operation by the police and the National Cyber Security Center.

The action, announced Thursday, came about after a security researcher reported the sprawling network to authorities. The host infrastructure was located in the Netherlands.

Used for criminal purposes

“The police then seized several botnet servers from a hosting provider for investigation,” the NCSC said. “The botnet was taken offline by the provider because it was used for criminal purposes.”

Read full article

Comments

© Aurich Lawson / Ars Technica

Authorities in the Netherlands said they dismantled a botnet that comprised more than 17 million devices and were managed by 200 servers in a joint operation by the police and the National Cyber Security Center.

The action, announced Thursday, came about after a security researcher reported the sprawling network to authorities. The host infrastructure was located in the Netherlands.

Used for criminal purposes

“The police then seized several botnet servers from a hosting provider for investigation,” the NCSC said. “The botnet was taken offline by the provider because it was used for criminal purposes.”

Read full article

Comments

© Aurich Lawson / Ars Technica

Researchers show a browser attack that can infer open sites and apps.

A Russian influence campaign hijacked hundreds of Bluesky accounts—many belonging to influential Americans—to spread propaganda, researchers said, in a striking disinformation tactic that weaponized authentic identities rather than relying on fake accounts.

Su piattaforme di incontri come Dating.com e AnastasiaDate, operatori e sistemi di intelligenza artificiale gestiscono profili fittizi, alternandosi nelle chat e utilizzando foto e contenuti generati per mantenere attive relazioni sentimentali inesistenti. Sono le romance scam