Biz & IT

ClickFix may be the biggest security threat your family has never heard of

Biz & IT, clickfix, malware, Scams, Security / Rejus Almole / November 11, 2025

Another campaign, documented by Sekoia, targeted Windows users. The attackers behind it first compromise a hotel’s account for Booking.com or another online travel service. Using the information stored in the compromised accounts, the attackers contact people with pending reservations, an ability that builds immediate trust with many targets, who are eager to comply with instructions, lest their stay be canceled.

The site eventually presents a fake CAPTCHA notification that bears an almost identical look and feel to those required by content delivery network Cloudflare. The proof the notification requires for confirmation that there’s a human behind the keyboard is to copy a string of text and paste it into the Windows terminal. With that, the machine is infected with malware tracked as PureRAT.

Push Security, meanwhile, reported a ClickFix campaign with a page “adapting to the device that you’re visiting from.” Depending on the OS, the page will deliver payloads for Windows or macOS. Many of these payloads, Microsoft said, are LOLbins, the name for binaries that use a technique known as living off the land. These scripts rely solely on native capabilities built into the operating system. With no malicious files being written to disk, endpoint protection is further hamstrung.

The commands, which are often base-64 encoded to make them unreadable to humans, are often copied inside the browser sandbox, a part of most browsers that accesses the Internet in an isolated environment designed to protect devices from malware or harmful scripts. Many security tools are unable to observe and flag these actions as potentially malicious.

The attacks can also be effective given the lack of awareness. Many people have learned over the years to be suspicious of links in emails or messengers. In many users’ minds, the precaution doesn’t extend to sites that instruct them to copy a piece of text and paste it into an unfamiliar window. When the instructions come in emails from a known hotel or at the top of Google results, targets can be further caught off guard.

With many families gathering in the coming weeks for various holiday dinners, ClickFix scams are worth mentioning to those family members who ask for security advice. Microsoft Defender and other endpoint protection programs offer some defenses against these attacks, but they can, in some cases, be bypassed. That means that, for now, awareness is the best countermeasure.

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Researchers isolate memorization from reasoning in AI neural networks

AI, AI alignment, AI architecture, AI behavior, AI memorization, AI research, AI safety, Allen Institute for AI, Biz & IT, computational neuroscience, copyright, generalization, Goodfire, gradient descent, K-FAC, loss curvature, machine learning, mechanistic interpretability, model editing, neural networks, OLMo, overfitting, transformer models, weight matrices / Kris Guyer / November 10, 2025

The hills and valleys of knowledge

Basic arithmetic ability lives in the memorization pathways, not logic circuits.

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or passages from books) and reasoning (solving new problems using general principles). New research from AI startup Goodfire.ai provides the first potentially clear evidence that these different functions actually work through completely separate neural pathways in the model’s architecture.

The researchers discovered that this separation proves remarkably clean. In a preprint paper released in late October, they described that when they removed the memorization pathways, models lost 97 percent of their ability to recite training data verbatim but kept nearly all their “logical reasoning” ability intact.

For example, at layer 22 in Allen Institute for AI’s OLMo-7B language model, the bottom 50 percent of weight components showed 23 percent higher activation on memorized data, while the top 10 percent showed 26 percent higher activation on general, non-memorized text. This mechanistic split enabled the researchers to surgically remove memorization while preserving other capabilities.

Perhaps most surprisingly, the researchers found that arithmetic operations seem to share the same neural pathways as memorization rather than logical reasoning. When they removed memorization circuits, mathematical performance plummeted to 66 percent while logical tasks remained nearly untouched. This discovery may explain why AI language models notoriously struggle with math without the use of external tools. They’re attempting to recall arithmetic from a limited memorization table rather than computing it, like a student who memorized times tables but never learned how multiplication works. The finding suggests that at current scales, language models treat “2+2=4” more like a memorized fact than a logical operation.

It’s worth noting that “reasoning” in AI research covers a spectrum of abilities that don’t necessarily match what we might call reasoning in humans. The logical reasoning that survived memory removal in this latest research includes tasks like evaluating true/false statements and following if-then rules, which are essentially applying learned patterns to new inputs. This also differs from the deeper “mathematical reasoning” required for proofs or novel problem-solving, which current AI models struggle with even when their pattern-matching abilities remain intact.

Looking ahead, if the information removal techniques receive further development in the future, AI companies could potentially one day remove, say, copyrighted content, private information, or harmful memorized text from a neural network without destroying the model’s ability to perform transformative tasks. However, since neural networks store information in distributed ways that are still not completely understood, for the time being, the researchers say their method “cannot guarantee complete elimination of sensitive information.” These are early steps in a new research direction for AI.

Traveling the neural landscape

To understand how researchers from Goodfire distinguished memorization from reasoning in these neural networks, it helps to know about a concept in AI called the “loss landscape.” The “loss landscape” is a way of visualizing how wrong or right an AI model’s predictions are as you adjust its internal settings (which are called “weights”).

Imagine you’re tuning a complex machine with millions of dials. The “loss” measures the number of mistakes the machine makes. High loss means many errors, low loss means few errors. The “landscape” is what you’d see if you could map out the error rate for every possible combination of dial settings.

During training, AI models essentially “roll downhill” in this landscape (gradient descent), adjusting their weights to find the valleys where they make the fewest mistakes. This process provides AI model outputs, like answers to questions.

Figure 1: Overview of our approach. We collect activations and gradients from a sample of training data (a), which allows us to approximate loss curvature w.r.t. a weight matrix using K-FAC (b). We decompose these weight matrices into components (each the same size as the matrix), ordered from high to low curvature. In language models, we show that data from different tasks interacts with parts of the spectrum of components differently (c). — Figure 1 from the paper “From Memorization to Reasoning in the Spectrum of Loss Curvature.” Credit: Merullo et al.

The researchers analyzed the “curvature” of the loss landscapes of particular AI language models, measuring how sensitive the model’s performance is to small changes in different neural network weights. Sharp peaks and valleys represent high curvature (where tiny changes cause big effects), while flat plains represent low curvature (where changes have minimal impact).

Using a technique called K-FAC (Kronecker-Factored Approximate Curvature), they found that individual memorized facts create sharp spikes in this landscape, but because each memorized item spikes in a different direction, when averaged together they create a flat profile. Meanwhile, reasoning abilities that many different inputs rely on maintain consistent moderate curves across the landscape, like rolling hills that remain roughly the same shape regardless of the direction from which you approach them.

“Directions that implement shared mechanisms used by many inputs add coherently and remain high-curvature on average,” the researchers write, describing reasoning pathways. In contrast, memorization uses “idiosyncratic sharp directions associated with specific examples” that appear flat when averaged across data.

Different tasks reveal a spectrum of mechanisms

The researchers tested their technique on multiple AI systems to verify the findings held across different architectures. They primarily used Allen Institute’s OLMo-2 family of open language models, specifically the 7-billion and 1-billion parameter versions, chosen because their training data is openly accessible. For vision models, they trained custom 86-million parameter Vision Transformers (ViT-Base models) on ImageNet with intentionally mislabeled data to create controlled memorization. They also validated their findings against existing memorization removal methods like BalancedSubnet to establish performance benchmarks.

The team tested their discovery by selectively removing low-curvature weight components from these trained models. Memorized content dropped to 3.4 percent recall from nearly 100 percent. Meanwhile, logical reasoning tasks maintained 95 to 106 percent of baseline performance.

These logical tasks included Boolean expression evaluation, logical deduction puzzles where solvers must track relationships like “if A is taller than B,” object tracking through multiple swaps, and benchmarks like BoolQ for yes/no reasoning, Winogrande for common sense inference, and OpenBookQA for science questions requiring reasoning from provided facts. Some tasks fell between these extremes, revealing a spectrum of mechanisms.

Mathematical operations and closed-book fact retrieval shared pathways with memorization, dropping to 66 to 86 percent performance after editing. The researchers found arithmetic particularly brittle. Even when models generated identical reasoning chains, they failed at the calculation step after low-curvature components were removed.

Figure 3: Sensitivity of different kinds of tasks to ablation of flatter eigenvectors. Parametric knowledge retrieval, arithmetic, and memorization are brittle, but openbook fact retrieval and logical reasoning is robust and maintain around 100% of original performance. — Figure 3 from the paper “From Memorization to Reasoning in the Spectrum of Loss Curvature.” Credit: Merullo et al.

“Arithmetic problems themselves are memorized at the 7B scale, or because they require narrowly used directions to do precise calculations,” the team explains. Open-book question answering, which relies on provided context rather than internal knowledge, proved most robust to the editing procedure, maintaining nearly full performance.

Curiously, the mechanism separation varied by information type. Common facts like country capitals barely changed after editing, while rare facts like company CEOs dropped 78 percent. This suggests models allocate distinct neural resources based on how frequently information appears in training.

The K-FAC technique outperformed existing memorization removal methods without needing training examples of memorized content. On unseen historical quotes, K-FAC achieved 16.1 percent memorization versus 60 percent for the previous best method, BalancedSubnet.

Vision transformers showed similar patterns. When trained with intentionally mislabeled images, the models developed distinct pathways for memorizing wrong labels versus learning correct patterns. Removing memorization pathways restored 66.5 percent accuracy on previously mislabeled images.

Limits of memory removal

However, the researchers acknowledged that their technique isn’t perfect. Once-removed memories might return if the model receives more training, as other research has shown that current unlearning methods only suppress information rather than completely erasing it from the neural network’s weights. That means the “forgotten” content can be reactivated with just a few training steps targeting those suppressed areas.

The researchers also can’t fully explain why some abilities, like math, break so easily when memorization is removed. It’s unclear whether the model actually memorized all its arithmetic or whether math just happens to use similar neural circuits as memorization. Additionally, some sophisticated capabilities might look like memorization to their detection method, even when they’re actually complex reasoning patterns. Finally, the mathematical tools they use to measure the model’s “landscape” can become unreliable at the extremes, though this doesn’t affect the actual editing process.

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

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Researchers surprised that with AI, toxicity is harder to fake than intelligence

AI, AI alignment, AI behavior, AI detection, AI research, AI sycophancy, Apertus, Biz & IT, chatbots, computational Turing test, DeepSeek, Duke University, emotional AI, gemma, generative ai, large language models, LLaMA, machine learning, Mistral, NYU, Qwen, Social Media, University of Amsterdam, University of Zurich / Paul Patrick / November 7, 2025

The next time you encounter an unusually polite reply on social media, you might want to check twice. It could be an AI model trying (and failing) to blend in with the crowd.

On Wednesday, researchers from the University of Zurich, University of Amsterdam, Duke University, and New York University released a study revealing that AI models remain easily distinguishable from humans in social media conversations, with overly friendly emotional tone serving as the most persistent giveaway. The research, which tested nine open-weight models across Twitter/X, Bluesky, and Reddit, found that classifiers developed by the researchers detected AI-generated replies with 70 to 80 percent accuracy.

The study introduces what the authors call a “computational Turing test” to assess how closely AI models approximate human language. Instead of relying on subjective human judgment about whether text sounds authentic, the framework uses automated classifiers and linguistic analysis to identify specific features that distinguish machine-generated from human-authored content.

“Even after calibration, LLM outputs remain clearly distinguishable from human text, particularly in affective tone and emotional expression,” the researchers wrote. The team, led by Nicolò Pagan at the University of Zurich, tested various optimization strategies, from simple prompting to fine-tuning, but found that deeper emotional cues persist as reliable tells that a particular text interaction online was authored by an AI chatbot rather than a human.

The toxicity tell

In the study, researchers tested nine large language models: Llama 3.1 8B, Llama 3.1 8B Instruct, Llama 3.1 70B, Mistral 7B v0.1, Mistral 7B Instruct v0.2, Qwen 2.5 7B Instruct, Gemma 3 4B Instruct, DeepSeek-R1-Distill-Llama-8B, and Apertus-8B-2509.

When prompted to generate replies to real social media posts from actual users, the AI models struggled to match the level of casual negativity and spontaneous emotional expression common in human social media posts, with toxicity scores consistently lower than authentic human replies across all three platforms.

To counter this deficiency, the researchers attempted optimization strategies (including providing writing examples and context retrieval) that reduced structural differences like sentence length or word count, but variations in emotional tone persisted. “Our comprehensive calibration tests challenge the assumption that more sophisticated optimization necessarily yields more human-like output,” the researchers concluded.

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Wipers from Russia’s most cut-throat hackers rain destruction on Ukraine

Biz & IT, malware, russia, sandworm, Security, Ukraine, war, wipers / Kris Guyer / November 6, 2025

One of the world’s most ruthless and advanced hacking groups, the Russian state-controlled Sandworm, launched a series of destructive cyberattacks in the country’s ongoing war against neighboring Ukraine, researchers reported Thursday.

In April, the group targeted a Ukrainian university with two wipers, a form of malware that aims to permanently destroy sensitive data and often the infrastructure storing it. One wiper, tracked under the name Sting, targeted fleets of Windows computers by scheduling a task named DavaniGulyashaSdeshka, a phrase derived from Russian slang that loosely translates to “eat some goulash,” researchers from ESET said. The other wiper is tracked as Zerlot.

A not-so-common target

Then, in June and September, Sandworm unleashed multiple wiper variants against a host of Ukrainian critical infrastructure targets, including organizations active in government, energy, and logistics. The targets have long been in the crosshairs of Russian hackers. There was, however, a fourth, less common target—organizations in Ukraine’s grain industry.

“Although all four have previously been documented as targets of wiper attacks at some point since 2022, the grain sector stands out as a not-so-frequent target,” ESET said. “Considering that grain export remains one of Ukraine’s main sources of revenue, such targeting likely reflects an attempt to weaken the country’s war economy.”

Wipers have been a favorite tool of Russian hackers since at least 2012, with the spreading of the NotPetya worm. The self-replicating malware originally targeted Ukraine, but eventually caused international chaos when it spread globally in a matter of hours. The worm resulted in tens of billions of dollars in financial damages after it shut down thousands of organizations, many for days or weeks.

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Google plans secret AI military outpost on tiny island overrun by crabs

AI, AI and work, AI infrastructure, australia, Biz & IT, Christmas Island, Cloud Computing, crabs, datacenters, defense, Google, machine learning, Military, red crabs, SubCom, subsea cables / Kris Guyer / November 6, 2025

Christmas Island Shire President Steve Pereira told Reuters that the council is examining community impacts before approving construction. “There is support for it, providing this data center actually does put back into the community with infrastructure, employment, and adding economic value to the island,” Pereira said.

That’s great, but what about the crabs?

Christmas Island’s annual crab migration is a natural phenomenon that Sir David Attenborough reportedly once described as one of his greatest TV moments when he visited the site in 1990.

Every year, millions of crabs emerge from the forest and swarm across roads, streams, rocks, and beaches to reach the ocean, where each female can produce up to 100,000 eggs. The tiny baby crabs that survive take about nine days to march back inland to the safety of the plateau.

While Google is seeking environmental approvals for its subsea cables, the timing could prove delicate for Christmas Island’s most famous residents. According to Parks Australia, the island’s annual red crab migration has already begun for 2025, with a major spawning event expected in just a few weeks, around November 15–16.

During peak migration times, sections of roads close at short notice as crabs move between forest and sea, and the island has built special crab bridges over roads to protect the migrating masses.

Parks Australia notes that while the migration happens annually, few baby crabs survive the journey from sea to forest most years, as they’re often eaten by fish, manta rays, and whale sharks. The successful migrations that occur only once or twice per decade (when large numbers of babies actually survive) are critical for maintaining the island’s red crab population.

How Google’s facility might coexist with 100 million marching crustaceans remains to be seen. But judging by the size of the event, it seems clear that it’s the crab’s world, and we’re just living in it.

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5 AI-developed malware families analyzed by Google fail to work and are easily detected

AI, Biz & IT, generative ai, guardrails, malware, ransomware, Security / Paul Patrick / November 5, 2025

The assessments provide a strong counterargument to the exaggerated narratives being trumpeted by AI companies, many seeking new rounds of venture funding, that AI-generated malware is widespread and part of a new paradigm that poses a current threat to traditional defenses.

A typical example is Anthropic, which recently reported its discovery of a threat actor that used its Claude LLM to “develop, market, and distribute several variants of ransomware, each with advanced evasion capabilities, encryption, and anti-recovery mechanisms.” The company went on to say: “Without Claude’s assistance, they could not implement or troubleshoot core malware components, like encryption algorithms, anti-analysis techniques, or Windows internals manipulation.”

Startup ConnectWise recently said that generative AI was “lowering the bar of entry for threat actors to get into the game.” The post cited a separate report from OpenAI that found 20 separate threat actors using its ChatGPT AI engine to develop malware for tasks including identifying vulnerabilities, developing exploit code, and debugging that code. BugCrowd, meanwhile, said that in a survey of self-selected individuals, “74 percent of hackers agree that AI has made hacking more accessible, opening the door for newcomers to join the fold.”

In some cases, the authors of such reports note the same limitations noted in this article. Wednesday’s report from Google says that in its analysis of AI tools used to develop code for managing command and control channels and obfuscating its operations “we did not see evidence of successful automation or any breakthrough capabilities.” OpenAI said much the same thing. Still, these disclaimers are rarely made prominently and are often downplayed in the resulting frenzy to portray AI-assisted malware as posing a near-term threat.

Google’s report provides at least one other useful finding. One threat actor that exploited the company’s Gemini AI model was able to bypass its guardrails by posing as white-hat hackers doing research for participation in a capture-the-flag game. These competitive exercises are designed to teach and demonstrate effective cyberattack strategies to both participants and onlookers.

Such guardrails are built into all mainstream LLMs to prevent them from being used maliciously, such as in cyberattacks and self-harm. Google said it has since better fine-tuned the countermeasure to resist such ploys.

Ultimately, the AI-generated malware that has surfaced to date suggests that it’s mostly experimental, and the results aren’t impressive. The events are worth monitoring for developments that show AI tools producing new capabilities that were previously unknown. For now, though, the biggest threats continue to predominantly rely on old-fashioned tactics.

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OpenAI signs massive AI compute deal with Amazon

AI, AI chips, AI infrastructure, Amazon, AWS, Biz & IT, chatbots, datacenters, generative ai, Google, large language models, machine learning, microsoft, NVIDIA, openai, oracle, sam altman / Paul Patrick / November 3, 2025

On Monday, OpenAI announced it has signed a seven-year, $38 billion deal to buy cloud services from Amazon Web Services to power products like ChatGPT and Sora. It’s the company’s first big computing deal after a fundamental restructuring last week that gave OpenAI more operational and financial freedom from Microsoft.

The agreement gives OpenAI access to hundreds of thousands of Nvidia graphics processors to train and run its AI models. “Scaling frontier AI requires massive, reliable compute,” OpenAI CEO Sam Altman said in a statement. “Our partnership with AWS strengthens the broad compute ecosystem that will power this next era and bring advanced AI to everyone.”

OpenAI will reportedly use Amazon Web Services immediately, with all planned capacity set to come online by the end of 2026 and room to expand further in 2027 and beyond. Amazon plans to roll out hundreds of thousands of chips, including Nvidia’s GB200 and GB300 AI accelerators, in data clusters built to power ChatGPT’s responses, generate AI videos, and train OpenAI’s next wave of models.

Wall Street apparently liked the deal, because Amazon shares hit an all-time high on Monday morning. Meanwhile, shares for long-time OpenAI investor and partner Microsoft briefly dipped following the announcement.

Massive AI compute requirements

It’s no secret that running generative AI models for hundreds of millions of people currently requires a lot of computing power. Amid chip shortages over the past few years, finding sources of that computing muscle has been tricky. OpenAI is reportedly working on its own GPU hardware to help alleviate the strain.

But for now, the company needs to find new sources of Nvidia chips, which accelerate AI computations. Altman has previously said that the company plans to spend $1.4 trillion to develop 30 gigawatts of computing resources, an amount that is enough to roughly power 25 million US homes, according to Reuters.

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Two Windows vulnerabilities, one a 0-day, are under active exploitation

Biz & IT, microsoft windows vulnerabilities, Security, zero-day, zero-day vulnerability / Paul Patrick / October 31, 2025

Two Windows vulnerabilities—one a zero-day that has been known to attackers since 2017 and the other a critical flaw that Microsoft initially tried and failed to patch recently—are under active exploitation in widespread attacks targeting a swath of the Internet, researchers say.

The zero-day went undiscovered until March, when security firm Trend Micro said it had been under active exploitation since 2017, by as many as 11 separate advanced persistent threats (APTs). These APT groups, often with ties to nation-states, relentlessly attack specific individuals or groups of interest. Trend Micro went on to say that the groups were exploiting the vulnerability, then tracked as ZDI-CAN-25373, to install various known post-exploitation payloads on infrastructure located in nearly 60 countries, with the US, Canada, Russia, and Korea being the most common.

A large-scale, coordinated operation

Seven months later, Microsoft still hasn’t patched the vulnerability, which stems from a bug in the Windows Shortcut binary format. The Windows component makes opening apps or accessing files easier and faster by allowing a single binary file to invoke them without having to navigate to their locations. In recent months, the ZDI-CAN-25373 tracking designation has been changed to CVE-2025-9491.

On Thursday, security firm Arctic Wolf reported that it observed a China-aligned threat group, tracked as UNC-6384, exploiting CVE-2025-9491 in attacks against various European nations. The final payload is a widely used remote access trojan known as PlugX. To better conceal the malware, the exploit keeps the binary file encrypted in the RC4 format until the final step in the attack.

“The breadth of targeting across multiple European nations within a condensed timeframe suggests either a large-scale coordinated intelligence collection operation or deployment of multiple parallel operational teams with shared tooling but independent targeting,” Arctic Wolf said. “The consistency in tradecraft across disparate targets indicates centralized tool development and operational security standards even if execution is distributed across multiple teams.”

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ChatGPT maker reportedly eyes $1 trillion IPO despite major quarterly losses

AI, AI infrastructure, Biz & IT, chatgpt, CoreWeave, generative ai, IPO, large language models, machine learning, microsoft, NVIDIA, openai, sam altman, SoftBank / Mike M. / October 30, 2025

An OpenAI spokesperson told Reuters that “an IPO is not our focus, so we could not possibly have set a date,” adding that the company is “building a durable business and advancing our mission so everyone benefits from AGI.”

Revenue grows as losses mount

The IPO preparations follow a restructuring of OpenAI completed on October 28 that reduced the company’s reliance on Microsoft, which has committed to investments of $13 billion and now owns about 27 percent of the company. OpenAI was most recently valued around $500 billion in private markets.

OpenAI started as a nonprofit in 2015, then added a for-profit arm a few years later with nonprofit oversight. Under the new structure, OpenAI is still controlled by a nonprofit, now called the OpenAI Foundation, but it gives the nonprofit a 26 percent stake in OpenAI Group and a warrant for additional shares if the company hits certain milestones.

A successful OpenAI IPO could represent a substantial gain for investors, including Microsoft, SoftBank, Thrive Capital, and Abu Dhabi’s MGX. But even so, OpenAI faces an uphill financial battle ahead. The ChatGPT maker expects to reach about $20 billion in revenue by year-end, according to people familiar with the company’s finances who spoke with Reuters, but its quarterly losses are significant.

Microsoft’s earnings filing on Wednesday offered a glimpse at the scale of those losses. The company reported that its share of OpenAI losses reduced Microsoft’s net income by $3.1 billion in the quarter that ended September 30. Since Microsoft owns 27 percent of OpenAI under the new structure, that suggests OpenAI lost about $11.5 billion during the quarter, as noted by The Register. That quarterly loss figure exceeds half of OpenAI’s expected revenue for the entire year.

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NPM flooded with malicious packages downloaded more than 86,000 times

Biz & IT, code dependencies, code repositories, npm, phantomraven, Security / Rejus Almole / October 29, 2025

Attackers are exploiting a major weakness that has allowed them access to the NPM code repository with more than 100 credential-stealing packages since August, mostly without detection.

The finding, laid out Wednesday by security firm Koi, brings attention to an NPM practice that allows installed packages to automatically pull down and run unvetted packages from untrusted domains. Koi said a campaign it tracks as PhantomRaven has exploited NPM’s use of “Remote Dynamic Dependences” to flood NPM with 126 malicious packages that have been downloaded more than 86,000 times. Some 80 of those packages remained available as of Wednesday morning, Koi said.

A blind spot

“PhantomRaven demonstrates how sophisticated attackers are getting [better] at exploiting blind spots in traditional security tooling,” Koi’s Oren Yomtov wrote. “Remote Dynamic Dependencies aren’t visible to static analysis.”

Remote Dynamic Dependencies provide greater flexibility in accessing dependencies—the code libraries that are mandatory for many other packages to work. Normally, dependencies are visible to the developer installing the package. They’re usually downloaded from NPM’s trusted infrastructure.

RDD works differently. It allows a package to download dependencies from untrusted websites, even those that connect over HTTP, which is unencrypted. The PhantomRaven attackers exploited this leniency by including code in the 126 packages uploaded to NPM. The code downloads malicious dependencies from URLs, including http://packages.storeartifact.com/npm/unused-imports. Koi said these dependencies are “invisible” to developers and many security scanners. Instead, they show the package contains “0 Dependencies.” An NPM feature causes these invisible downloads to be automatically installed.

Compounding the weakness, the dependencies are downloaded “fresh” from the attacker server each time a package is installed, rather than being cached, versioned, or otherwise static, as Koi explained:

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Nvidia hits record $5 trillion mark as CEO dismisses AI bubble concerns

AI, AI chips, AI infrastructure, Apple, Biz & IT, Blackwell chips, data centers, Donald Trump, GPUs, Jensen Huang, machine learning, microsoft, Nokia, NVIDIA, openai, palantir, semiconductors, Uber / Tim Belzer / October 29, 2025

Partnerships and government contracts fuel optimism

At the GTC conference on Tuesday, Nvidia’s CEO went out of his way to repeatedly praise Donald Trump and his policies for accelerating domestic tech investment while warning that excluding China from Nvidia’s ecosystem could limit US access to half the world’s AI developers. The overall event stressed Nvidia’s role as an American company, with Huang even nodding to Trump’s signature slogan in his sign-off by thanking the audience for “making America great again.”

Trump’s cooperation is paramount for Nvidia because US export controls have effectively blocked Nvidia’s AI chips from China, costing the company billions of dollars in revenue. Bob O’Donnell of TECHnalysis Research told Reuters that “Nvidia clearly brought their story to DC to both educate and gain favor with the US government. They managed to hit most of the hottest and most influential topics in tech.”

Beyond the political messaging, Huang announced a series of partnerships and deals that apparently helped ease investor concerns about Nvidia’s future. The company announced collaborations with Uber Technologies, Palantir Technologies, and CrowdStrike Holdings, among others. Nvidia also revealed a $1 billion investment in Nokia to support the telecommunications company’s shift toward AI and 6G networking.

The agreement with Uber will power a fleet of 100,000 self-driving vehicles with Nvidia technology, with automaker Stellantis among the first to deliver the robotaxis. Palantir will pair Nvidia’s technology with its Ontology platform to use AI techniques for logistics insights, with Lowe’s as an early adopter. Eli Lilly plans to build what Nvidia described as the most powerful supercomputer owned and operated by a pharmaceutical company, relying on more than 1,000 Blackwell AI accelerator chips.

The $5 trillion valuation surpasses the total cryptocurrency market value and equals roughly half the size of the pan European Stoxx 600 equities index, Reuters notes. At current prices, Huang’s stake in Nvidia would be worth about $179.2 billion, making him the world’s eighth-richest person.

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OpenAI data suggests 1 million users discuss suicide with ChatGPT weekly

AI, AI assistants, AI behavior, AI ethics, AI safety, AI sycophancy, Biz & IT, chatbots, chatgpt, machine learning, Mental Health, openai, sam altman / Kris Guyer / October 28, 2025

Earlier this month, the company unveiled a wellness council to address these concerns, though critics noted the council did not include a suicide prevention expert. OpenAI also recently rolled out controls for parents of children who use ChatGPT. The company says it’s building an age prediction system to automatically detect children using ChatGPT and impose a stricter set of age-related safeguards.

Rare but impactful conversations

The data shared on Monday appears to be part of the company’s effort to demonstrate progress on these issues, although it also shines a spotlight on just how deeply AI chatbots may be affecting the health of the public at large.

In a blog post on the recently released data, OpenAI says these types of conversations in ChatGPT that might trigger concerns about “psychosis, mania, or suicidal thinking” are “extremely rare,” and thus difficult to measure. The company estimates that around 0.07 percent of users active in a given week and 0.01 percent of messages indicate possible signs of mental health emergencies related to psychosis or mania. For emotional attachment, the company estimates around 0.15 percent of users active in a given week and 0.03 percent of messages indicate potentially heightened levels of emotional attachment to ChatGPT.

OpenAI also claims that on an evaluation of over 1,000 challenging mental health-related conversations, the new GPT-5 model was 92 percent compliant with its desired behaviors, compared to 27 percent for a previous GPT-5 model released on August 15. The company also says its latest version of GPT-5 holds up to OpenAI’s safeguards better in long conversations. OpenAI has previously admitted that its safeguards are less effective during extended conversations.

In addition, OpenAI says it’s adding new evaluations to attempt to measure some of the most serious mental health issues facing ChatGPT users. The company says its baseline safety testing for its AI language models will now include benchmarks for emotional reliance and non-suicidal mental health emergencies.

Despite the ongoing mental health concerns, OpenAI CEO Sam Altman announced on October 14 that the company will allow verified adult users to have erotic conversations with ChatGPT starting in December. The company had loosened ChatGPT content restrictions in February but then dramatically tightened them after the August lawsuit. Altman explained that OpenAI had made ChatGPT “pretty restrictive to make sure we were being careful with mental health issues” but acknowledged this approach made the chatbot “less useful/enjoyable to many users who had no mental health problems.”

If you or someone you know is feeling suicidal or in distress, please call the Suicide Prevention Lifeline number, 1-800-273-TALK (8255), which will put you in touch with a local crisis center.

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