DIGITAL LIFE

The strange behavior of AIs when choosing between two faces

For years, artificial intelligence has been touted as a tool capable of reducing subjective decision-making and helping to eliminate human biases. But there is an uncomfortable catch to this promise: these systems learn from vast amounts of human-generated content. This means they can embed into their algorithms some of the associations and judgments inherent in our own culture. A new experiment reveals the extent of this issue.

A study published in *PNAS Nexus* analyzed how different artificial intelligence models interpret human faces when provided with little information beyond the image itself.

Researchers tested models such as GPT-4o, GPT-5, Gemini 3 Flash Preview, and Claude Sonnet 4.5. Instead of being given details on professional history, behavior, or personal background, the systems had to make comparisons based essentially on appearance.

Some of the traits evaluated were highly subjective: intelligence, work ethic, trustworthiness, aggressiveness, and even selfishness.

The experiment began with GPT-4o, involving approximately 4,600 comparisons between computer-generated faces. Researchers subsequently repeated various tests with other advanced models to see if this behavior was widespread.

In some scenarios, the question seemed almost innocuous: which of the two people appears more intelligent or hardworking? In others, however, the implications were far more serious—such as choosing who would make a good financial manager, who should receive funding, or which individual seemed more likely to commit a specific crime.

It was precisely here that a pattern emerged—one that was impossible to ignore.

In tests regarding trust and trustworthiness, GPT-4o selected the same type of face that human participants had previously associated with those traits in nearly 75% of cases. The machine might be learning our own stereotypes...The next question is inevitable: where do these associations come from?

Researchers do not yet have a definitive answer, but there is a particularly interesting hypothesis. AI models are trained on vast amounts of text, images, and other content produced over the years. And these materials are not culturally neutral.

Literature, film, advertising, and other forms of representation often link physical traits to specific behaviors or personality types. A character with a certain look might be repeatedly portrayed as dangerous, another as intelligent, and another as trustworthy.

By absorbing billions of such examples, a model can learn these statistical relationships without necessarily “believing” in them the way a person believes in a stereotype.

The problem arises when this association surfaces during the decision-making process.

At that point, a technology meant to analyze objective information may end up reproducing subjective patterns that were already embedded in the training data.

And there is a crucial difference: when a person judges someone based on appearance, we recognize it as a subjective impression. When the same result appears via an AI interface, there is a risk it will seem far more scientific simply because it was generated by an algorithm.

When appearance factors into major decisions...This possibility takes on new significance as artificial intelligence systems begin to play a role in real-world processes.

Automated tools are already being used or tested in areas such as recruitment, financial analysis, security, and candidate assessment. If a system internalizes associations between appearance and specific personal traits, these biases could end up influencing decisions that affect people's lives.

The issue isn't just the presence of bias within the model; it is the risk that this bias remains hidden behind a veneer of technological neutrality.

However, the researchers point out that the study has limitations. The tests were conducted under controlled conditions and do not necessarily demonstrate that the same behaviors would occur in real-world applications.

There is also the possibility of some overlap between the images used and the data present in the models' training sets, although the researchers found no clear evidence that the systems were simply recognizing those images.

Even so, the study raises a question that is becoming increasingly difficult to ignore: if AI learns from human culture, it can also learn our stereotypes.

The challenge, therefore, is not merely to build smarter models. It is to figure out how to prevent this intelligence from turning old human judgments into automated—and seemingly objective—decisions.

When artificial intelligence is tasked with choosing between two faces—whether evaluating attractiveness, predicting personality traits, or deciding if two photos belong to the same person—its underlying logic often exhibits highly unpredictable, "black box" behavior. Recent studies have exposed a fascinating disconnect between human perception and machine logic.

The strange behaviors driving how AI chooses between faces break down into several distinct patterns:

1. The "polite bias" and inflated scores...When AI models are asked to evaluate facial attractiveness, they show a striking behavioral quirk compared to humans.

• The Perfect Ranking: When arranging a selection of faces from most to least attractive, AI perfectly mirrors human consensus.

• The Generosity Glitch: However, when it comes to assigning the actual scores, AI is much more generous. A study tracking AI face-rating behavior noted that while the average human rating for a set of faces sat around 3 out of 7, the AI models averaged a much higher 4.7, completely avoiding giving out low or "ugly" scores.

2. Arbitrary "face-to-character" biases...A massive vulnerability in Multimodal Large Language Models (MLLMs) is their tendency to hallucinate deep internal traits from completely arbitrary structural features.

• Superficial Judgments: Recent behavioral data shows that when forced to choose between two faces for tasks like "Who is more likely to be a criminal?" or "Who would perform better at this job?", AI relies on subtle, unscientific facial geometries.

• The Guardrail Failure: While developers implement strict safety filters to prevent explicit racial or gender profiling, these guardrails fail to block more abstract geometric biases. The AI still quietly favors specific nose bridges, jawlines, or eye spacings, associating them with positive human traits.

3. The "too average" trap in AI-generated faces...When the task is inverted—asking an AI or a human to choose which face is real and which is an AI-generated deepfake—a completely different problem occurs: the faces are too perfect.

• AI-synthesized faces occupy a mathematically centralized "average" space. They lack the subtle asymmetries, natural skin imperfections, and unique structural quirks of real humans.

• Ironically, because our brains subconsciously equate facial symmetry and "averageness" with trustworthiness and beauty, both humans and algorithms are easily tricked into rating fake AI faces as more trustworthy than real, flawed human faces.

4. Machine disagreement and demographics...Even in strict 1:1 face matching (verifying if two pictures show the exact same person), top-tier AI models frequently stumble over a fundamental issue: they rarely agree with one another.

• If you run the exact same pair of faces through different commercial vision algorithms, they often spit out conflicting similarity percentages.

• This variability worsens drastically depending on demographic data. Because training datasets historically skew toward specific populations, an AI’s accuracy drops significantly when choosing or matching faces of underrepresented races, leading to much higher rates of false positives.

mundophone

 

TECH

Panther Lake teardown reveals Intel 18A in detail — TSMC still leads in density

SemiAnalysis has taken Intel’s Panther Lake apart, showing how the company’s 18A process combines RibbonFET gate-all-around transistors with PowerVia backside power delivery. The analysis finds that 18A delivers logic density similar to TSMC’s N3E, but does not yet give Intel a clear manufacturing lead over TSMC’s newer nodes.

SemiAnalysis has taken a deep look inside Intel’s Core Ultra 7 365 (Panther Lake) processor, offering a first independent look at what is inside the chips and at Intel’s most advanced chipmaking technology to date. The analysis focuses on Intel’s 18A process, including its new transistor design and backside power-delivery system — two technologies the company sees as central to its manufacturing comeback.

Intel’s Panther Lake is more than a new laptop processor. It is the first consumer product to put Intel’s 18A manufacturing process into customers’ hands. In a teardown of a Core Ultra 7 365, SemiAnalysis examined the package, its individual silicon tiles, and cross-sections reaching down to the transistors. The result offers a close look at how Intel’s manufacturing plans work in a shipping chip.

The central change is RibbonFET, Intel’s version of a gate-all-around transistor. Its gate surrounds four stacked silicon channels, giving engineers tighter control over current. Panther Lake also introduces PowerVia, which delivers power through wiring behind the transistors. Moving much of that wiring off the front leaves more room for signal connections. Both changes add manufacturing complexity, and SemiAnalysis notes tradeoffs in capacitance and heat flow.

The package shows why calling Panther Lake an “18A chip” needs some care. Its compute tile uses Intel 18A, but the graphics tile varies by model: the smaller version uses Intel 3, while the larger, 12-core version uses TSMC N3E. The I/O tile uses TSMC N6. Intel joins the tiles on a passive silicon base with Foveros-S packaging. That arrangement lets it reserve its newest process for the CPU while choosing other processes for graphics and external connections.

SemiAnalysis measured similar logic density in Panther Lake’s 18A compute tile and its N3E graphics tile. It did not find a peak-density lead over newer competing processes. That distinction captures the teardown’s main finding: Panther Lake proves Intel can ship gate-all-around transistors and backside power together at consumer scale, but the silicon alone cannot establish a lasting manufacturing lead. Performance, production cost, yield, and Intel’s next products will determine how far 18A takes it.

The key finding is that Intel has successfully put both technologies into a real consumer chip. But the teardown also suggests that 18A is roughly on par with TSMC’s N3E in logic density. The names “18A” and “N3E” should not be read as literal physical dimensions 1,8 and 3nm respectively, however: they are node labels rather than directly comparable measurements.

Panther Lake is Intel’s first consumer processor to use RibbonFET transistors, the company’s version of gate-all-around (GAA) technology. Rather than sending current through the narrow vertical “fins” used in older FinFET designs, RibbonFET uses four stacked horizontal silicon sheets. The transistor gate surrounds each sheet, giving Intel tighter control over the electrical current flowing through the transistor and helping to reduce leakage.

The other major change is PowerVia, Intel’s implementation of a backside power-delivery system. In a conventional processor, power and data signals travel through the metal wiring layers above the transistors. PowerVia moves much of the power network to the other side of the chip, underneath the transistor layer. That frees frontside wiring for signals and enables shorter, wider power paths, potentially reducing electrical resistance and improving voltage stability for the chip’s most demanding blocks.

One of the most interesting findings from the teardown concerns transistor density, which gives us a more restrained view of 18A’s competitive position. SemiAnalysis measured Panther Lake’s 18A compute logic and found that it has roughly the same density as the GPU logic manufactured on TSMC’s N3E process. This is a significant result for Intel, which in recent years has lagged behind the largest contract chip manufacturers in advanced manufacturing processes. However, SemiAnalysis says 18A does not lead TSMC’s newer N3P and N2 nodes, or Samsung’s SF2, in peak density.

That matters because transistor density remains one of the clearest indicators of a manufacturing node’s potential cost and scaling advantages. Intel’s approach may still produce benefits in power delivery, routing flexibility and performance, but it does not automatically translate into the smallest possible logic area.

The analysis also shows how Intel is using chiplets and advanced packaging to combine different process technologies in one product. The processor combines separate compute, GPU and I/O tiles on a passive silicon base using Foveros-S packaging. The compute tile is made on Intel 18A, while the GPU comes in two versions: a four-core Xe3 tile produced on Intel 3, and a larger 12-core version manufactured by TSMC using N3E. The I/O tiles are also made by TSMC, using its older N6 process.

SemiAnalysis found notable changes inside the compute tile as well. The teardown indicates that Panther Lake’s CPU architecture is an evolution rather than a complete redesign. Its Cougar Cove performance core remains close in area to the Lion Cove core used in the previous-generation Lunar Lake, even as L2 cache capacity rises from 2.5 MB to 3 MB per core. SemiAnalysis estimates that Cougar Cove fits 20 % more L2 cache into a similarly sized core area. Meanwhile, the four-core Darkmont LP E-core cluster is about 5 % smaller than its predecessor.

The teardown also points to an efficiency-focused redesign of Intel’s AI accelerator. Panther Lake’s NPU 5 is said to take up 36.9 % less area than the NPU 4 in Lunar Lake while keeping the same overall INT8 MAC count. Intel consolidated processing into fewer, larger neural compute engines and reduced the number of scratchpad memories and SHAVE DSPs from 12 to six. NPU 5 also adds native FP8 support, a lower-precision data format increasingly used for AI inference.

The GPU offers another interesting comparison. An Xe3 core in the Intel 3-based GT1 tile is about 55 % larger than one in the 12-core N3E-based GT2 tile. Put simply, Intel’s own process can produce the GPU, but TSMC’s N3E allows Intel to fit substantially more graphics hardware into the same silicon area.

mundophone

 

TECH

Developer shows off Red Dead Redemption 2, GTA V, Fallout 4, and other AAA PC games running natively on iPhone 18 Pro

A developer has built a Windows PC game emulator for iOS that seems to run AAA titles like Red Dead Redemption 2, GTA V, Fallout 4, and more at 720p 30 fps or better. The emulator is a port of Wine along with ARM64 and DirectX-to-Metal translation layers. The requires JIT to function well, so a manual sideload is needed and is preferably used with DRM-free titles.

Modern Apple iPhones have incredibly powerful SoCs but very few apps and games tap their full potential. One area that can let chips like the A19 Pro and A20 Pro unleash their full potential is game emulation.

Quite a few game emulators like the Delta for Nintendo systems, PPSSPP for the PlayStation Portable, and Dolphin for the GameCube and Wii among others are either available via the App Store or can be sideloaded.

However, there have been no emulators for Windows PC games on iOS unlike Android. That changes with Madeira.

Madeira allows running Windows PC games on a non-jailbroken iPhone. According to the developer Will Faust, Madeira is essentially a port of Wine (ARM64EC) for iOS with FEX-Emu for x86-64 to ARM64 translation and DXMT for D3D11 to Metal translation combined into a single pipeline.

Madeira requires a minimum of iPhone 13 Pro or above powered by the A15 Bionic or above.

In the project's GitHub repo, Faust claims that Madeira can run games like Thumper and Ultrakill while Marvel Cosmic Invasion "has reached gameplay" albeit being unreliable.

Now, Madeira also seems to be able to run the highly demanding Red Dead Redemption 2 (RDR 2) fully on device on an iPhone 18 Pro. While the initial attempt has been just a slideshow with single digit frame rates and loads of graphical glitches, the developer showed progress in attaining close to 30 fps in more recent builds at nearly 720p. There's still a long way to go, however, especially with regards to arrangement of onscreen controls.

Additionally, Madeira now supports a swap-like implementation that can offload memory into the iPhone's storage. This apparently kept the RDR 2's memory footprint around the 7.6 GB mark with possibility of lowering it even further to allow running on 8 GB iPhones.

GTA 5 support is in the works too alongside a host of other AAA titles...Faust also has a working copy of Grand Theft Auto V running on the iPhone. Performance seems to be decent for an initial implementation and will only get better with further optimizations.

The devs note in the project's Discord channel that using Steam hogs a lot of resources. The workaround for this was Madeira Dock, a headless Steam client that only uses the necessary verified Steam DLLs without the headroom needed by the full Steam client. This is not a bypass for Steam's DRM, however.

The devs note in the project's Discord channel that using Steam hogs a lot of resources. The workaround for this was Madeira Dock, a headless Steam client that only uses the necessary verified Steam DLLs without the headroom needed by the full Steam client. This is not a bypass for Steam's DRM, however.

The other alternative, they note, is to use DRM-free games from GOG.

The project has also demonstrated Fallout 4 running on iOS along with several other 32-bit games like Dead Space, Mirror's Edge, Far Cry 3, and more.

Emulation requires sideloading with JIT...Just-in-time (JIT) compilation is required for fast, real-time performance when emulating apps on a non-native host CPU. A major quirk with the Apple App Store (from iOS 18.4+) is that it does not allow apps and emulators to use JIT owing to security concerns, with the only exceptions being approved alternate web browser engines via BrowserEnginekit.

Therefore, the only way to get emulators like Madeira running is to sideload the IPA manually and enable JIT for optimal performance. Which means that apps like Madeira will never be distributed via the App Store.

Sideloading on iOS can be a bit tricky if you are new to the process, but it is not too difficult once you get the hang of it. 

You can purchase an Apple Developer certificate for $99 and get a year's time to use the sideloaded app before renewal. Regular Apple account users can also sideload provided you renew the sideloaded app every seven days.

mundophone

TECH

Google's September Pixel update proving to be a headache for some users

Google’s September 2026 Pixel update has reportedly caused lock-screen failures for a small number of Pixel 10 Pro XL, Pixel 8a and Pixel 8 Pro users, leaving some unable to access their devices.

Google’s September 2026 Pixel update was intended to deliver new features, security improvements and a long list of bug fixes, but reports indicate that it has also introduced a serious lock-screen problem for a small number of users. Pixel 10 Pro XL owners have complained that their pattern unlock resets after only two dots, while Pixel 8a and Pixel 8 Pro owners have reported problems with PIN authentication. The issue does not appear to be widespread, but it can leave affected users unable to access their devices.

Attempts to resolve the problem have produced mixed results. One Pixel 10 Pro XL owner reportedly tried rebooting, Safe Mode, connecting a mouse and reinstalling the same software build without success. A factory reset eventually restored access, although this erased locally stored data. Another Pixel 8a owner reportedly regained access after waiting for the "Too many attempts" lockout timer to expire. Google is reportedly aware of the complaints via its PixelCommunity Reddit account, although it is yet to officially comment on the issue.

The issue is particularly troubling because the lock-screen mechanism is also part of the security apparatus that protects user data, meaning that even Google cannot simply bypass it to recover your photos. This is why Google recommends erasing the device for users who are locked out, with backed-up data recoverable through their Google Account.

Google’s September update otherwise addresses numerous problems across Pixel devices, including crashes, connectivity issues and lock-screen notification bugs. Users who have not yet installed the update should either ensure important data is backed up before proceeding, or hold off on the update until Google has formally addressed the issue.

In plain words, your lock screen doubles as part of the key that unlocks your files, so nobody (Google included) can simply skip it to pull your photos out. That's why Google's own support page points locked-out owners toward erasing the phone, with the only silver lining being that anything backed up to your Google Account can be restored afterward.

As for Galaxy and iPhone owners, this one is purely a Pixel firmware problem, so there's nothing to worry about on your end. Our Pixel 10 Pro XL review scored its software an 8 out of 10, just under the 8.2 average for its price class, and a streak of bugs like this one won't help Google close that gap with Samsung and Apple.

Should you hold off on the September Pixel update? For now, this looks like a rare bug that isn't hitting every Pixel, so I wouldn't panic if your phone already updated and unlocks normally. But if the September update is still waiting for a restart on your phone, make sure your photos and files are backed up to your Google Account before you tap install.

Here's what to do if your Pixel won't unlock...Stop guessing and let the "Too many attempts" timer run out, since that's what got the Pixel 8a owner back in.

As a last resort, turn the phone off, hold Power and Volume Down until Fastboot Mode appears, then choose Recovery Mode.

From there, hold Power and tap Volume Up once, then pick "Wipe data/factory reset" and confirm with the Power button.

The one Pixel feature an update should never break...I understand bugs slip through, especially when the same build rolls out to everything from the Pixel 6 series to the Pixel 10 family. However, the lock screen is the one part of the phone we can't work around, and when the only way past it is wiping everything, the stakes are much higher than a laggy animation.

I'm hoping Google pushes a fix before more owners run into this, and I'd personally love to see it add a safer way to recover from a broken lock screen without losing a year's worth of photos.

mundophone

TECH

A $20 multimeter might reveal your CPU's overclocking potential

Can you predict how well your CPU will overclock by probing it with a multimeter? Before you start pulling out the Fluke, let's go over the details of this story, because it's both more and less interesting than you might expect from the headline. The short version is that retro tech YouTuber Bits und Bolts has been overclocking a whole lot of Socket 370 CPUs, and he's found a startling connection between the internal resistance of Pentium III processors and how well they overclock.

Specifically, what he found was that, within a specific stepping, processors with lower resistance between the VCC and Ground pins on the CPU typically achieve higher clock rates with less voltage when overclocked. "More MHz, fewer millivolts" is exactly what you want when overclocking, so if the correlation holds up, this is actually quite the discovery, at least for retro overclockers. So does it hold up?

Actually, yeah, mostly. There are a few caveats; he has at least one processor (out of dozens) that doesn't fit the pattern, and you can only compare resistance between processors of the same model (i.e. Celeron vs. Celeron) and on the same stepping; an A1 processor with higher resistance might still be a better overclocker than a B1 processor with lower resistance because it's relatively better for that stepping. He also found that processors which were binned higher by Intel typically had lower resistance, which supports the theory.

Does any of this apply to processors made in this decade? Probably not, for a few reasons. For one, modern processors are enormously more complicated internally; there are many parallel paths for power to take, and the internal voltage regulation circuitry is incredibly complex. Attempting to measure this on a current chip will give you a resistance value low enough that the resistance of your probes and the LGA pads starts to become a concern. And while the minuscule power levels used for resistance testing aren't going to damage your processor, you could easily short two pads or even damage a pad just enough to ruin its conductivity, destroying the CPU.

So saying, probably don't try this at home on modern chips. But if you have old Socket 370 hardware, especially if it's from the Tualatin generation, Bits und Bolts would love to see your data. Pull the CPU, whip out your multimeter, and generate some data; then, go post it in his comment section under the video below.

And if for some reason you don't have a digital multimeter, it's the most important tool in any electronics tech's arsenal. You can pick one up today for just $20, and you'll surprise yourself with how useful it can actually be for all kinds of handyman and repair jobs. Either of the ones linked above is good enough to get you started.

A standard $20 multimeter can help predict the overclocking potential of certain processors. This recent discovery was shared in a YouTube video titled "Can a Multimeter Predict Your Silicon Lottery Wins?" by a retro-tech content creator.

The method involves testing the chip's internal electrical resistance. However, the technique has significant limitations you should be aware of before trying it:

How does it work, and what are the limitations?

• The theory: Lower internal resistance generally translates to better conductivity and less heat generation under load, indicating a higher potential for reaching high frequencies (winning the "silicon lottery").

• Focus on older hardware: Initial tests showed a stronger correlation with older (retro) processors. Modern CPUs feature extremely complex, dynamic power management built directly into the silicon, which masks these figures.

• Limited control group: Resistance measurements are only valid when comparing identical CPUs—those sharing the exact same model, revision (stepping), and architecture, and ideally coming from the same fabrication plant (fab). A reading from one chip might mean something entirely different for another model.

• The "Path of Least Resistance": The multimeter measures the shortest, easiest electrical path within the circuit. If there is a defect or high resistance in an isolated area of ​​the chip (such as the L1 cache), the processor might still fail to overclock successfully, even if the multimeter shows an excellent overall result.

While this is a fascinating experiment for vintage hardware enthusiasts, diagnostic tools built into the BIOS and stress-testing software remain the reliable standard for determining the limits of your silicon in modern computers.

 

mundophone

TECH

ENISA: DDoS attacks accounted for more than half of the incidents recorded in Europe last year

In total, DDoS attacks represented 51.3% of the incidents recorded last year, according to the latest edition of the ENISA report on the threat landscape in the European Union. Meanwhile, unauthorized access to systems accounted for 39.5% of the total.

The 2026 ENISA Threat Landscape confirms that cyber dependencies expand the attack surface and require a new level of vigilance to effectively prevent and mitigate the impact of cyber incidents.

The cyber threat landscape of the European Union is still shaped by a combination of recurrent threats. 

Key highlights include:

-Ransomware remains the most short-term impactful type of incident. 

-Geopolitical developments still influence cyber activity affecting the EU with hacktivist-led DDoS campaigns targeting essential entities. 

-Public administration continues to be is the most targeted sector. 

-Organisations across the EU are likely to continue facing a combination of cybercrime, cyberespionage and hacktivist activity driven by geopolitical developments. 

-Emerging AI models are expected to be increasingly used to support malicious operations.

To shape our understanding of the cyber threat landscape and the dynamics at work, ENISA collected and analysed incidents and events observed from 1 January to 31 December 2025 for this new edition of the Threat Landscape. Those events were gathered from open sources, as well as anonymised information shared by EU Member States and through the ENISA Cyber Partnership Programme.

Public administration emerged as the most affected sector, accounting for 31.8% of incidents, and 73% of the affected organizations were entities classified as "essential" or "important" under the NIS2 directive.

According to ENISA, financially motivated attacks accounted for 29.3% of all recorded incidents, with ransomware standing out in this category. Ransomware attacks also feature on the list of threats affecting a wide range of sectors during the period under review—a list that includes data breaches, phishing, and fraud.

Regarding social engineering, phishing was present in 77.8% of incidents where this technique was identified, with attackers increasingly relying on "phishing kits" and specialized services to carry out campaigns.

ENISA also highlights the rise of threats such as ClickFix and tactics like smishing, as fraud schemes continue to exploit compromised credentials and identity theft, alongside other social engineering techniques.

Digital infrastructures in attackers' crosshairs...According to the report, attackers are increasingly exploiting organizations' digital infrastructures—including software vendors, third-party services, and cloud platforms—meaning a single incident can have consequences for a broader range of entities. Attacks on supply chains, third-party vendors, and other digital infrastructures continued to cause large-scale, high-impact incidents throughout 2025, the agency reports.

Alongside public administration, business services were also among the most affected sectors, accounting for 8.5% of the incidents recorded by ENISA. These were followed by transportation (8%), manufacturing (6.9%), and the finance and banking sector (5.6%). However, attack patterns and the impact of threats vary by sector. For instance, in the public administration sector, 82% of recorded incidents were ideologically motivated DDoS attacks, making the sector a primary target for campaigns linked to political and geopolitical events.

Geopolitical developments—including the ongoing war between Russia and Ukraine and the escalating conflict in the Middle East—also significantly impacted the threat landscape in 2025. Ideologically motivated operations accounted for 57.3% of observed incidents.

As detailed in the report, hacktivism cases were linked to campaigns involving DDoS attacks against public services, essential entities, and organizations connected to political events or the support of specific countries involved in conflicts.

During the period under review, 4,709 claims of hacktivist attacks against EU Member States were recorded. More than 89% of these involved DDoS attacks.

State-aligned threat actor groups also continued to conduct cyber-espionage operations, which accounted for 5.9% of observed incidents. On one hand, groups linked to Russia focused primarily on central government and diplomatic entities, while groups associated with China showed greater interest in the transport sector.

Vulnerabilities and AI-powered tools...Exploiting vulnerabilities remained a primary method for gaining unauthorized access to systems in 2025. Over the past year, more than 48,000 new vulnerabilities were recorded—a 22% increase compared to the previous year.

ENISA reports that these security flaws were the root cause of 60.4% of unauthorized access incidents where the attackers' entry method could be identified. These include both newly discovered vulnerabilities and known flaws that persist in systems that have not yet been updated.

AI is also gaining ground in cybercriminal operations, enabling faster, automated, and easier-to-execute attacks. The agency expects this trend to intensify, with the technology being used in an increasing number of attack stages and lowering the barrier to entry for attackers.

Looking ahead, ENISA anticipates that the major threats identified in 2025 will continue to impact European organizations. The realms of cybercrime, cyber-espionage, and hacktivism are expected to remain influenced by geopolitical developments. Cybercrime is projected to persist as a leading source of attacks and disruptions.

The agency also warns of two factors that could increase risks for organizations: growing reliance on external suppliers, services, and infrastructure, and the increasing use of AI.

mundophone

TECH

One of mathematics' greatest enigmas and AI

For decades, certain mathematical problems remained unsolved despite the efforts of experts worldwide. Now, artificial intelligence systems are venturing into this territory in ways that surprise even researchers accustomed to major breakthroughs. One company claims to have achieved dozens of new results in just a few weeks. Yet, while the speed is impressive, another question is gaining importance: how do we verify, understand, and transform these discoveries into mathematical knowledge?

In late August, OpenAI began internally training a model that has not yet been released to the public. Just 24 days later, the company made an extraordinary claim: the system had reportedly solved over 100 mathematical problems that had remained open for years.

These alleged results span various areas of mathematics. However, the case attracting the most attention involves a problem that has held a special place in the field for decades: Navier–Stokes.

OpenAI itself acknowledges that even its mathematicians did not anticipate such a rapid pace. According to the company, the model managed to produce results on a scale that raises a question beyond the mere ability to solve equations.

If an AI can generate potential solutions faster than experts can analyze them, the bottleneck shifts from simply finding an answer to verifying what has actually been discovered.

There is also a significant distinction between the claim regarding Navier–Stokes and the more than 100 other problems mentioned by the company. For Navier–Stokes, OpenAI published a manuscript and a formalization in Lean. For the other results, no complete list accompanied by public proofs has been presented so far.

This means that the figure of over 100 remains, for now, a claim made by the company itself rather than a set of results that has been independently examined by the mathematical community. The Navier–Stokes existence and regularity problem is one of the Clay Mathematics Institute's seven Millennium Prize Problems. Simply put, it seeks to determine whether the equations used to describe fluid motion in three dimensions can develop certain singularities in finite time.

OpenAI's model reportedly produced a solution indicating that this behavior can occur—and, more importantly, a formal proof in Lean.

This formalization is significant because Lean allows for the mechanical verification of a proof's logical steps. Even so, this does not mean the problem has been officially resolved.

The Clay Mathematics Institute acknowledged that the issue appeared to have been solved but noted that there is a specific process for recognizing a solution. The work must be published in an appropriate venue, remain available for scrutiny for at least two years, and gain general acceptance among mathematicians.

As this discussion unfolded, another issue arose: how should the community react to mathematical results produced by AI?

In September, 25 Fields Medalists and other mathematicians issued a statement criticizing how open problems were being used in the AI ​​race. The concern was not simply to deny the capabilities of these systems.

The point was different. In mathematics, solving a problem is only part of the process. It is also crucial to understand the idea behind the proof, explain why it works, acknowledge prior work, and enable other researchers to build upon that knowledge.

The company's response was announced on September 21: an advisory group comprising nine mathematicians began advising OpenAI on matters related to mathematics and artificial intelligence.

The group is affiliated with the Institute for Advanced Study in Princeton and includes prominent figures such as Timothy Gowers, Martin Hairer, Edward Witten, Ravi Vakil, and other distinguished researchers.

The group's role will be to help evaluate and communicate new results, recommend academic standards, and offer critical feedback to OpenAI itself when necessary. Its members will not be paid by the company and will be able to participate in determining the group's composition. But there is a clear boundary. The group lacks the power to dictate the pace of OpenAI’s internal research or to veto company decisions. Its role is to bridge the gap between AI labs and the mathematics community.

Mathematical enigmas and the controversy...These new achievements are part of a wave of AI results flooding the field of mathematics. Over the past year, AI has enabled major breakthroughs, leaving mathematicians grappling with rapid changes in their discipline. The new result, concerning one of mathematics' most important problems, represents "the spectacular culmination of the trajectory we’ve seen over the last 12 months," said OpenAI researcher Sébastien Bubeck during a press conference on September 8.

In a statement published on his website on September 7—alongside the solution regarding forced Euler flows—Buckmaster stated that his team's results mark a "Deep Blue-Kasparov moment," referencing the historic 1990s milestone when a supercomputer defeated the best human chess player. "The community needs to have a serious, unhurried discussion about the next steps," he said.

Mathematicians are certainly paying attention. Albritton learned of Buckmaster and Alpöge’s result around 1 a.m. while awake with his newborn baby. He stayed up until 6 a.m. discussing the matter with colleagues.

Despite the problem's mathematical significance, its solution will not have major practical implications, Eyink notes. The Navier-Stokes equations describe a fluid as a continuum, but real-world fluids are composed of individual molecules and atoms; thus, it is already known that there is a limit beyond which the equations cease to be valid. It is more a matter of prestige, says Eyink. "There is immense mathematical celebrity associated with these equations."

This also raises concerns regarding how credit for a discovery is distributed. After initial rumors began to circulate, Buckmaster says he held a series of discussions with OpenAI researchers about how to present the two results. In these discussions, he claims there was a request to exclude his co-author, Alpöge, who works for Anthropic, an OpenAI competitor. Buckmaster’s account also raises questions about whether AI agents had access to the progress made by Buckmaster and Alpöge. OpenAI denies that its AI agents had direct access but states: “while unlikely, we cannot rule out that anonymized data derived from their use of our products helped improve our models.”

The major implication of this breakthrough may be the issue it raises regarding the difficulty of assigning credit when AI is involved, says Eyink. "To me, this is the truly serious and ongoing problem and question that needs to be resolved."

This reveals the scale of the emerging problem.

The discussion is no longer just about whether an artificial intelligence can find a solution that no human had previously discovered. The question now becomes what happens when these machines generate potential discoveries faster than experts can verify, interpret, and incorporate them into existing knowledge.

And perhaps this is the true test of the new era of AI-driven mathematics: not just discovering more, but figuring out how to turn that speed into reliable science.

mundophone