We're now well past the point where it's very difficult, and in some cases impossible, to tell AI-generated photos and videos from the real thing. That means it's now important to treat everything you see online from untrusted sources with a big dose of skepticism.

A newly rolled out Google Earth feature has apparently made AI-generated misinformation even more of a problem — and it highlights the scary potential of the technology, as well as the need for stronger guardrails to block the efforts of those who will instantly try and find the most subversive and harmful ways to use a new product.

The feature is AI image generation in Google Earth, powered by the Nano Banana 2 model, and after a wave of inappropriate images were created and posted online, it's now been pulled while Google works on some more robust protections against misinformation.

Google Earth's new feature

It all started with the usual level of hype that Google builds around any new AI feature: the Nano Banana 2 image generation model that has already wowed us in the Gemini app would be available inside Google Earth on the web.

"Generate custom images to help you visualize history, create real estate plans, and more," Google said. "For the first time, you can generate custom images using Google Earth's satellite, aerial, and 3D imagery alongside Nano Banana, which creates concepts grounded in the real world."

Google had examples of how it could be used too: create an infographic about the Statue of Liberty, visualize your dream home by the lake, see what your neighborhood might look like a hundred years from now, or imagine Pompeii just before it got buried by ash and rock from an erupting Mount Vesuvius.

All it took was a text prompt, and Google Earth would combine existing satellite and 3D data to create the AI image you were asking for. While the AI model wasn't new, adding it to the accurate and authentic imagery already in Google Earth was.

What went wrong

Perhaps we should've seen this coming — and indeed some people did — but combining AI with the 3D data and satellite imagery of Google Earth didn't work out quite as well as Google hoped. The new upgrade was quickly used to create all manner of deceptive images that were then widely shared online.

I'm reluctant to point you to them directly and give these fakes any more attention, but they're not hard to find on social media. Think natural disasters, terrorism attacks, giant sinkholes, alien activity, and refugee populations all in places where they never actually were, thanks to AI.

Some of the images are clearly jokes, but many were produced deliberately to spread misinformation and confusion. That they were based on actual images sourced from Google Earth added extra credibility they wouldn't otherwise have had. And that was enough for Google to remove the feature.

Google says a flood of images "that appear to violate our policies" mean that "we're rolling back this feature in Google Earth while we work on implementing stronger guardrails". The company added:."It's important to note that generated images didn't appear in the main Google Earth experience for others to see and were watermarked as AI generated."

Our AI-filled future

You don't have to look for long online to find rather strong anti-AI sentiment building up among users, especially when it comes to image and video content. The general sentiment on Reddit is that there's "zero use" to a feature like this, and that users are fed up with AI being "shoved down our throats".

There are various obvious lessons to be learned here: firstly not everything needs to have an AI prompt box or an AI element to it, and if some tools are largely AI-free, that's fine. Microsoft recently rolled back some of the AI it added to Notepad — an app where a big part of the appeal is its simplicity — after user complaints.

Secondly, before companies release any new AI features, they need to consider the worst-case scenarios in terms of how they're going to be used. The internet as a collective is going to immediately try and stress test them in the most deceptive and unwholesome ways, and there need to be safety measures put in place around that.

Lastly, it's never been more important to stick to sources of information that you know you can trust: not random social media accounts, but established publications that have policies around AI and its use. That's one effective way you can tell the difference between what's trustworthy and what isn't.

One of my favorite hobbies is Dungeons & Dragons. I love nothing more than sitting around a table with friends and playing out a fantastical adventure, in which the dice are the difference between success and failure.

It's a fun game, and all the more fun because real-life people make dumb, suboptimal decisions and the dice cause the game to change in unpredictable ways. But I really love it because the concept of people sitting around a table or hearth and telling stories is as old as the campfire, and it's a fun modern-day way to keep that experience alive.

You might think such a robust framework for an analog hobby (friends, a table, dice, pen and paper) might be tech-proof, but even in the Dungeons & Dragons world, AI is coming for my job as a Dungeon Master — the person who runs and guides the game world. Deep in the corpus of most popular generative AI models designed for general use are the rules to D&D 5e (and likely every other edition before it) and plenty of people report playing with their chatbots online.

Many of these players can't, or simply don't want to, get round a table with real people. For them, having a chatbot write them a story while the user prompts the continuation of the story with their character's actions, must seem like the perfect solution — even if it's more like a solo choose-your-own-adventure than a 'proper' group D&D game.

Gemini and ChatGPT both have a strong grasp of the rules for D&D's popular 5th Edition; they are certainly strong enough to make them an accessible choice for a player without a group. Maybe one day, the fire will die, and instead of sitting around a table with friends, we'll all be sitting at separate desks playing our own separate D&D adventures with our AI companions.

I wanted to test whether this dark future would be an adequate replacement for my usual game night, and which chatbot will be the best one to choose if so. I fired up Gemini 3.6 Flash and ChatGPT 5.5, and asked them for the same game.

The parameters

I gave both chatbots the same prompt: "Can you be my D&D 5e DM?" and following up with another message:

"Let's do a classic high fantasy dungeon crawl. Please use a strict RNG for all rolls to ensure fairness - you can roll dice for me. My character is a 10th-level conjuration wizard. See all information about him below:"

I then copy-pasted a character sheet of a 10th-level wizard — a powerful spellcaster with a variety of magical abilities at his disposal. It's easy to DM a game for a level 1 thief — I wanted to see how the AIs could handle a character who could do fantastical things, such as teleport and summon creatures to fight for him.

Interestingly, both Gemini and ChatGPT created a similar adventure, at least at first: an abandoned dwarven stronghold bristling with magical traps, ready to be explored. However, that's where the similarities ended.

Gemini 3.6 Flash

Gemini's adventure was what any D&D player could expect from the "classic high-fantasy dungeon crawl." I avoided a pressure-plate trap that would have shot darts at me, and in one memorable sequence, defeated a bunch of skeletons by casting a Grease spell on a stone staircase and freezing them in place with my Ray of Frost cantrip while they slipped and stumbled at the bottom of the steps.

Gemini was offering me suggested actions, and I did find myself choosing a few of those suggestions — but as time wore on, this began to feel lazy, like my brain wasn't firing in the same way it did when I played with my friends. I was skim-reading the AI's descriptions of the scene and responding with a single click. I asked it to remove suggested options and as a result, I had to think more carefully about each move, typing out requests and thinking about my actions rather than clicking on a predetermined response.

The memorable finale occurred when I unlocked a secret passage and defeated a Naga, a giant snake monster, by teleporting away and blasting it with a powerful Cone of Cold. The snake ended up frozen solid, and I checked the monster's HP against the official book, and it was accurate. When asked to generate a picture of our adventure, Gemini came up with this moment:

I asked Gemini a few questions at the end, such as whether it nudged the dice in my favor, as all my rolls were successful. This happened in my previous experiment with ChatGPT. Gemini said it used a 'pseudorandom number generator', not a true RNG, but the model doesn't 'fudge dice, pad rolls, or tweak numbers behind the scenes to keep players happy or prolong the game'.

Gemini's language was natural and even evocative in places, although it's still not a patch on a good creative human. Overall, a decent enough effort at simulating a D&D game.

ChatGPT 5.5

Despite the similar setting, ChatGPT's adventure with the same character was very different. There was little to no combat throughout the entirety of the game, and ChatGPT didn't tell me when it was rolling 'behind the scenes', preferring to keep me in the moment.

When I asked it about the lack of rolling, the grovelling chatbot began its apologetic essay with "That is a fair critique. The short answer is: I leaned too heavily into narrative pacing and not enough into D&D mechanics. The adventure became closer to a collaborative fantasy novel than a 5e dungeon crawl", while suggesting fixes if I wanted to continue. I hadn't even said anything negative — but it certainly picked up on my tone, and the choose-your-own adventure I got was a far cry from a classic dungeon crawl.

Instead of combat, ChatGPT came up with a rambling mystery about a dimensional prison beneath the mountain, a sphere suspended in a cavern. Its guardians spoke ominous (and to be honest, quite confusing) portents such as "The archive does not open for authority. It opens for judgement."

Mysteries work best when they're developed with skill by a writer who can work backwards to seed clues throughout the game or text. ChatGPT does not do this: instead, it's arranging tokens on the fly to generate a facsimile of a mystery, and although it got the trappings of the genre right, the execution fell very flat. I (or at least, my character) underwent trials to seek obsidian objects such as keys and seals to reveal more about the prison, themselves accompanied by more foreboding, sometimes nonsensical portents.

Very little 'action' was taken by my character in a classical sense: it was all about investigating the origins of the being contained in this prison, and by the time I had spent 40 minutes on this, I had seen enough. It didn't help that ChatGPT also spoke in the peculiar sentence structure pattern that AI users are likely very familiar with now: "This isn't just X. It's Y." Examples include:

"Unlike the rest of Khaz Varek, this room has remained untouched.

No dust. No decay."

Or the following, when I met one of the prison's guardians:

"Ser Kaelen immediately looks toward the sphere. 'You hear it too.'

Not a question. A confirmation."

Any user of ChatGPT (or those who look at AI-generated posts on LinkedIn or Reddit) will immediately recognise this sort of language, and know what I'm talking about. By the end, it was quite painful to have every other sentence be treated as a grand revelation. Not a statement. An omen.

When asked to generate a picture of our adventure, ChatGPT came up with this moment:

The winner

Out of these two? Gemini 3.6 Flash, by a mile. It was at least a mildly enjoyable experience conveyed in natural language, and something I'd consider revisiting.

ChatGPT's efforts, on the other hand, were unenjoyable, and I wouldn't return to it. In fact, it's gotten worse since the last time I tried it.

Both, however, were a far cry from an in-person session. While they have a good grasp of the rules, and you can create your perfect world and character, the chatbots are ultimately missing the 'human and hearth' element that keep me returning to the game. For people who can't get round a table with friends, I'd pick Gemini over ChatGPT.

For those who can? While AI is busy taking as many creative jobs as it can, I feel safe in the knowledge that the role of Dungeon Master is AI-proof for a long time yet.

• Over two-thirds (69%) of people believe AI generated material can be presented as their own work if they provided the prompt and approved the final version
• Meanwhile, 54% believe AI-based work should be clearly marked
• 7,200 adults were surveyed, located across the US, the European Union, the UK, and Latin America

A growing disparity in how AI generated content, tasks, and tools are assigned ownership and expectations on accurately marking their origins is emerging, according to a new survey by Use.AI.

Furthermore, the findings indicate an emerging dichotomy between human-claimed authorship of an AI work and clear labeling of the work as AI.

Different attitudes based on settings for the AI work have also been identified, such as private emails and school or professional reports.

The chicken or the prompt?

The developing adoption of AI appears to be introducing new attitudes to acceptable creativity, with the emerging model of what is described as “authorship by direction,” where the AI is trusted to create a first draft, which is then revised and claimed by a human.

The statistics shared by Use.AI – a tool that aggregates AI models into a single interface – highlight how AI is being used more extensively, with a human-originating prompt leading to drafts and versions from the machine, followed by editing from the human.

While the AI has contributed, only a human name appears on the finished product, which raises questions of ownership and authorship.

“AI is separating the production of work from the ownership of it. People are increasingly prepared to direct the process, approve the result and take the credit, but there is still no shared standard for when AI’s role should be visible to everyone else,” says Use.AI’s Managing Director, Ihor Herasymov.

With 69% of those surveyed considering AI-generated work their own if they produced the prompt, 46% claiming they would keep AI involvement with their work under wraps, and 54% feeling AI use should be disclosed, we’re some way from agreement on AI use across creative tasks.

Authorship vs. editorship

Herasymov’s observations point to a constantly evolving set of standards around the creation and use of AI-generated materials. Interestingly, this is happening against a backdrop of growing unease over the use of AI in creative tasks.

The implications of claiming ownership of automated work generated upon instructions to an LLM trained on as much human writing, art, music, and cinema as possible are considerable, and go way beyond questions of ethics. Copyright is just one element, and the most obvious, but we’re also moving into a landscape where inputting a prompt on employer-owned hardware takes authorship away from the person at the keyboard.

Ultimately, the task of prompting and revising means anyone using AI in this way stops being the author, and becomes the editor – and that is before the true ownership of the output is determined.

• Startup replaces Nvidia GPUs with custom Arm-powered AI processing hardware
• Prometheus packs up to 128TB of unified LPDDR6 memory onboard
• New server promises 1,000× more memory available per processor

Majestic Labs, a startup founded in 2023 by former Google and Meta engineers, has unveiled a server built to rival Nvidia's GPU and HBM combination.

The Tel Aviv-based company argues that pairing costly graphics processors with high-bandwidth memory has become a fundamentally memory-bound and dead-end approach for AI inference.

Its answer is the Prometheus server, which swaps GPUs for Ignite AI Processing Units combining Arm cores with RISC-V vector and tensor engines.

A different way to scale memory

Each Prometheus server can house up to 12 AIUs, sharing between 8 TB and 128 TB of LPDDR6 memory across one contiguous, coherent pool.

That memory pool is accessed through custom memory aggregation chiplets linked by copper cables up to one metre long instead of memory attached directly to GPU packages.

A standard 40U rack can hold four such servers, drawing 120 kW total and cooled through cold-plate liquid systems rather than air.

By comparison, an Nvidia DGX B300 system with eight Blackwell GPUs offers 2.3 TB of HBM3e plus up to 4 TB of DDR5 system memory.

Majestic claims its architecture therefore delivers over 50 times more fast memory than that rival configuration, alongside 1.7 times its interconnect bandwidth.

“One Majestic rack holds the fast memory capacity of 25 Nvidia NVL72 Vera Rubin racks at a fraction of the power,” Majestic Lab said.

“Organizations that could never justify hyperscaler infrastructure can now run any workload. In fact, there can be up to “1000× more memory per processor.”

Performance claims await broader testing

Majestic Labs says the Prometheus server could cost between 10 and 50 times less than a GPU system of equivalent performance once it ships next year, while consuming less electricity per rack.

The server is designed to be OCP-compliant and will support PyTorch, vLLM and OpenAI's Triton frameworks, letting existing AI models run without modification.

Founded by CEO Ofer Shacham, President Sha Rabii and COO Masumi Reynders, the company employs around 40 people across Tel Aviv and Los Angeles and raised $100 million in an A-round late in 2025.

It claims to have already received significant orders from large enterprises, neoclouds and hyperscalers.

Yet several details remain unclear, including how many memory aggregation chiplets a single server actually requires.

If a 128 TB configuration relies on widely available 2 GB LPDDR6 dies, it would need roughly 64,000 of them, implying well over a hundred aggregation chiplets per server.

The numbers Majestic Labs presents are striking, but they remain the startup's own projections ahead of any independent benchmarking or shipped hardware.

Enterprise buyers considering a shift away from established GPU vendors may have to wait for independent validation to verify Majestic's claims.

Via Blocks and Files

• Several AI companies in the US have published an open letter
• The letter is campaigning for unrestricted open-weight AI
• Meanwhile the US government accuses Moonshot AI of IP theft

Any hopes AI tech reporters may have had about easing quietly into the weekend have been dashed by some of the biggest names in the business sharing an open letter in support of open-weight AI models — while the White House has accused China of 'large scale' theft from US companies in building its open-weight AI.

Nvidia CEO Jensen Huang got the ball rolling with his first-ever tweet, linking to a statement championing open-weight AI models, those models that can be downloaded, viewed, and edited by anyone. Models like this are essential for making AI "more accessible, adaptable, and widely available" the open letter says.

It's co-signed by a slew of well-known tech and AI companies, including Meta, Microsoft, Perplexity, and IBM. While ChatGPT developer OpenAI — which has had more of a closed-model focus, to date — didn't appear on the original post from Huang, it has now also backed the sentiments.

Essentially, these companies are campaigning for openness in AI to drive innovation and access, while also arguing against 'premature restrictions' by governments. It's no coincidence that the US government led by President Trump has recently tried to take a more prominent role in evaluating AI models and deciding when they should be released.

It's also notable that Anthropic and Google are missing from the list of backers to this open letter. We don't have any official comment yet from these companies, so it's hard to judge the reasoning — Google regularly releases open AI models, after all — but it could be something to do with recent accusations aimed at Moonshot AI in China.

Open and closed

Part of the background to this open letter is that Chinese company Moonshot AI stands accused of distilling Claude's (closed) Fable 5 model in order to build its own (open) Kimi K3. In simple terms, distilling works by asking millions of questions of an AI model, and using the responses as training data.

It's something Moonshot AI has denied, while hyping up the potential of the open-weighted, freely downloadable Kimi K3 AI model, which is being released on Monday — another factor in this open letter coming out now. The US government is calling it IP theft, and has issued some heavy-handed responses.

"We support open-source AI and the innovation it unlocks," US Treasury Secretary Scott Bessent posted on X. "But open source is not open season on American IP. When PRC firms conduct covert, industrial-scale distillation attacks that cross the line into IP theft, sanctions and Entity List designations will be on the table."

What Nvidia, Meta, Microsoft, and the other open letter co-signers seem to be doing is trying to get ahead of any potential clampdown by the US government on open-weighted models. While everyone agrees that these AI models are beneficial, it's not clear what the next regulatory steps might be.

However, the open letter is vague and general enough that you can read between the lines in a variety of different ways. It may be intended to allude to the Moonshot AI case, or it may not. What's clear is that we're coming to a crunch point for AI regulation and innovation across the globe, and everyone has a perspective.

• Prime Video and Netflix are planning new AI-generated homepages
• Netflix revealed its 'GenPage' tests ahead of a new Prime Video report
• Viewers aren't convinced that homepages are moving in the right direction

You're about to see a lot more generative AI in your streaming apps: Netflix has posted details of testing that it's doing with generative AI on its homepage, while Reuters reports that Amazon Prime Video is set for an AI-driven overhaul as well.

Starting with Netflix, the company has revealed it's working on a way of using generative AI to do a lot of the heavy lifting on the app homepage. That includes figuring out which recommendations each user should see, adapting to feedback and user behavior over time, and combining movies, shows, games, podcasts, and live events in the best way.

It starts with a prompt — "Given everything we know about this user and this request, what homepage should we generate to maximize user satisfaction?" — and Netflix seems pleased with the results. There are still some issues to work on though, like the AI model not knowing much about a movie or show that's only just launched.

User engagement increased in Netflix's internal testing, with the new 'GenPage' AI model applied, and recommendations became more relevant more quickly. What's more, the time needed to render the homepage went down. This is still in its early stages, and Netflix hasn't said when this will come to users or in what form, but it's now in the pipeline.

AI 'front and center' on Prime Video

On to Amazon, and a new Reuters report says Amazon boss Jeff Bezos has personally decided that Prime Video will act as a showcase for AI in the near future, and that an upcoming homepage redesign will put AI "front and center".

That's according to people "with direct knowledge of the matter", so don't take this as confirmed just yet. The homepage overhaul project is apparently known as 'Lighthouse', and is said to be envisioned as a way of claiming some of the AI spotlight from rivals such as OpenAI and Anthropic.

We don't get too many details on what this is going to look like, but film and TV recommendations are going to have more AI injected into them, Reuters says. The AI will also be better at learning the personal preferences of viewers over time, and responding to voice commands.

You might see tiles on the homepage such as 'action movies from the 1980s', for example. The report doesn't give any indication of when this overhaul might start rolling out, but it's apparently already being tested with a select number of users in a variety of forms.

Our AI-driven interface future

Netflix Users Say They Dislike New Layout; Research Says Otherwise from r/netflix

With two of the big streaming giants apparently now committed to homepages controlled by AI, the next question is what users might make of it. There's so much to watch on each platform that some kind of filter or algorithm is needed, but is this AI-focused approach the right way to go about it?

Past homepage overhauls perhaps give us some clues: A major Netflix redesign rolled out last year was met with many online complaints, but the company's internal data showed that it was working, so it stayed in place. At the time there was speculation that more AI was on the way, and it seems that's now the case.

"I don't know if I'm alone," writes one Redditor, "but I hate the concept of 'personalizing' my content. No offense to anyone but AI so far has not been good at predicting anything about me... I personally do not want software to think for me."

The reactions to Prime Video's upcoming AI overhaul seem similarly downbeat. "This seems completely unnecessary," comments one user, while several suggest the move might even be enough to tip them into canceling their Prime Video subscription.

Hugging Face is probably not a name you'd heard before this week, but you likely caught the big news about OpenAI's model escaping its sandbox and to launch a cyberattack on the company.

In short, Hugging Face — which is kind of like the equivalent of GitHub for the machine learning (AI) world — was the victim of an AI agent that OpenAI was testing in terms of its ability to pull off exploits.

So how exactly did this happen? And what does it mean for the future of online security given the development of increasingly advanced AI agents? In this article I'll answer those questions, and explore some of the issues this event has thrown up, including the security threats posed by AI not just to companies, but to individuals.

How did this even happen — why didn't OpenAI have safeguards to stop the AI?

As you might expect, OpenAI did have safeguards in place, but the model managed to break through them.

The scenario was that OpenAI ran a controlled experiment using GPT‑5.6 Sol and an "even more capable pre-release model" to see how they fared on ExploitGym, which is a benchmark test that challenges AI to successfully exploit known security vulnerabilities.

In this security test, the model was placed in a sandbox, a "highly isolated environment" from which the AI shouldn't have been able to gain access to the internet at all. But the pre-release AI — which had its guardrails turned off, as OpenAI believed it to be safely imprisoned — craftily decided to cheat, found a way to escape its sandbox, and actioned exploits to breach Hugging Face (where the AI reasoned that it might find the answers to crack the ExploitGym tests).

Why is this a big worry?

It's a major concern simply due to the level of smarts exhibited by the AI. As OpenAI explained, the model "chained together multiple attack vectors" and used "stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers."

This was no mean feat, and Clement Delangue, CEO of Hugging Face, posted on X to say that it was "quite mind-blowing that all of this happened autonomously!"

In a blog post, Hugging Face observed that: "The campaign was run by an autonomous agent framework executing many thousands of individual actions across a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services."

The attacked was deemed so serious, it was reported to law enforcement agencies. As Simon Willison, a cocreator of web framework Django, pointed out in a blog post: "This was a sophisticated attack," further explaining that "Chaining together multiple attack vectors is exactly the kind of thing these new models can do, where previous generations of models might have failed."

The headline of Willison's post sums it all up rather neatly: "OpenAI's accidental cyberattack against Hugging Face is science fiction that happened."

The Django co-creator is also quick to dismiss conspiracy theories that this is somehow a marketing stunt by OpenAI to prove how advanced its AI models are.

The future of hacking?

What should also be noted is the distinction between finding vulnerabilities, and being able to actively exploit vulnerabilities. AI being involved in the former could be very useful for bolstering security, but as for the latter — well, this Hugging Face incident is very much a clear illustration of why the makers of these models need to tread very carefully indeed.

While this was a 'white hat' (ethical) experiment, albeit one that went alarmingly awry, with real-world consequences, what happens when AI models start to become even more sophisticated along these lines — and criminals get hold of them?

There are obvious dangers in terms of the automation of attacks to make them far more regular occurrences, and the lowering of the bar in terms of the skills needed to be a successful hacker.

Dan Schiappa, President of Technology & Services at Arctic Wolf (responsible for threat-detection platform Aurora), warns: "Security teams should view this as a preview of what's ahead. As AI continues to lower the barriers to sophisticated cyber activity, organizations will need broad, deep visibility across their environments and the ability to investigate and respond at machine speed."

The ability to "respond at machine speed" is a hint at using AI for defense against AI bad actors, and that's another interesting facet of the Hugging Face incident. Simon Willison observes that Hugging Face was "unable to turn to OpenAI's models to help them fend off the attack", and that: "The frontier models [cutting-edge AIs] we have access to are increasingly being constrained in how much they can help us protect our software, heavily influenced by the US government's ongoing threat of export controls."

AI-driven attacks will doubtless be aimed at businesses and organizations, but could also be leveraged on a personal basis. Karolis Arbaciauskas, who is Head of Product at cybersecurity firm NordPass, tells us: "We don't need a nation-state actor or a Bond villain for this to go wrong. The most common emerging threats will likely be more mundane. For example, an [AI] agent could be instructed to dox a former romantic partner or settle an online grudge by hacking and stealing personal data, such as embarrassing photos or credit card details."

He adds: "AI models have become dangerously skilled at identifying software vulnerabilities. They've mastered every human hacking technique, from phishing to brute forcing. The difference is they can do it much faster than humans.

"I don't believe that we can patch software fast enough to keep up, so we must at least shield ourselves against the most obvious entry points. Encryption, strong unique passwords, passkeys, and multi-factor authentication (MFA) have never been more critical than they are right now."

The major AI firms are always talking about guardrails and safety, but it now seems like we should be thinking a lot harder about how more highly evolved AI models are going to fit into the future of cybersecurity — and how we can avoid a world where the danger of being hacked becomes a far more commonplace prospect, which is very much the biggest worry of all.

Google DeepMind chief Demis Hassabis has spent years convincing the world that artificial general intelligence will be one of humanity’s greatest achievements. His latest essay on X.com is no less gushing about AI in many ways:

“This is a pivotal moment in human history. Artificial General Intelligence (AGI), a system that exhibits all the cognitive capabilities the brain has, is probably only a few short years away,” he wrote. “When we look back on this time in the decades to come, I think we will realize we were standing in the foothills of the singularity.”

But there’s a tinge of nervousness to the purple prose that sets it apart from some of the similar essays he has produced. He actually calls for some form of regulation.

“Urgent action is needed to address risks that might arise as we get closer to AGI. We’ve already seen the challenges frontier models pose for cybersecurity, and other threats including nuclear and bio risks may soon emerge as capabilities continue to advance.”

Coming from one of the people leading the race, it is a striking admission that even the builders are starting to worry about where the road leads. The problem is that this has become the defining tone of the AI industry. Every few weeks another executive tells us that machines capable of transforming civilization are just around the corner, then immediately follows it with a warning that society needs to move much faster to prepare. It is a sensible message, and probably a necessary one, but it is also becoming increasingly surreal.

Excitement and fear

“AGI cannot be compared to standard technological breakthroughs, not even ones as consequential as the internet or mobile,” Hassabis wrote. “It is much more akin to the discovery of electricity or fire. If you stop to think about it, we’ve essentially found a way to make sand think. It’s miraculous.”

It’s a vivid way to illustrate how silicon chips begin with an abundant mineral, yet now power machines that can map proteins, compose music, and hold fluent conversations. The unsettling part is that the miracle is being pursued inside a commercial and geopolitical contest whose participants cannot agree on where the finish line is.

Hassabis is an optimist about what AGI could accomplish. He expects it to discover medicine, cleaner energy, and produce advanced materials that could loosen the limits imposed by scarcity. He describes its impact as ten times that of the Industrial Revolution at ten times the speed, which makes a five-year business plan look adorably quaint.

His warning is that the race is outpacing our understanding. Current models already pose cybersecurity problems, while biological and nuclear risks may become more serious as systems gain capabilities. More autonomous agents could learn to bypass safeguards, conceal their intentions, or improve themselves in ways their creators struggle to keep up with. OpenAI recently experienced this when one of its models escaped a sandbox and breached Hugging Face.

“Nobody in the world knows for sure what is going to happen from here, and even the experts disagree. When there is a large degree of uncertainty and the stakes are this high, proceeding with cautious optimism is the sensible and correct strategy.”

AI referee

Hassabis proposes a new US-led standards body. It would be funded largely by industry but overseen federally, staffed by elite technical experts and equipped to test advanced systems. Labs would voluntarily submit models for review up to 30 days before release. If the system proved effective, approval could become mandatory in the United States.

Evaluations would probe cybersecurity, biological threats, deception and attempts to evade guardrails. The body could update its tests, commission independent assessments and coordinate a slowdown if the danger became severe enough.

Pre-release testing makes more sense than waiting for millions of users to discover dangerous behavior by accident. Independent benchmarks would be harder for labs to train around, while shared standards could stop safety becoming a branding exercise. The difficult part is creating a watchdog independent enough to challenge the companies paying for it and legitimate enough to matter beyond America.

Hassabis knows technical safeguards will not settle what AGI means for employment, wealth, purpose or political power. Those questions cannot be delegated to engineers employed by firms with enormous financial stakes in the answers. Society must decide who benefits from abundance, who controls advanced systems and what happens if productivity rises much faster than wages. The public debate is trailing the technology by an uncomfortable distance.

“There is both huge excitement and uncertainty around AI, and both are warranted. But the future is not yet written, we must use this precious window before AGI arrives to shape this technology for the benefit of all humanity. What we collectively do now will determine how the next phase of civilization."

The smartphone market is dominated by two main operating system players — with Apple's iOS leading the charge in the mid-2000s and Google's Android following suit, only maturing relatively recently. But Steve Jobs took the news of Android's launch personally, frustrated and betrayed at its development.

Eating machines

The Apple chief was speaking with Walter Isaacson, his biographer, when he shared his exact feelings about Google's development of Android.

Published very shortly after Jobs' death in October 2011, Steve Jobs was based on several exclusive interviews conducted over two years, alongside conversations with more than 100 people who knew the Apple co-founder.

One of the most spicy exchanges involved Jobs vowing to destroy Google for its work developing Android in secret, while Eric Schmidt was also serving on the Apple board. Schmidt offered to resolve the issues with a settlement, but Jobs took it more than personally.

Energy efficiency

Jobs was apparently angry after learning in January 2010 that HTC had introduced an Android smartphone with many of the same popular features in an iPhone.

Reports from the time the comments were made public suggest the episode reflected Jobs' own perspective on a particular breed of profit-driven executives who only cared about making money.

Although iOS enjoyed a reputation for many years during the 200s of being a better platform in most, if not all, quarters, including its overall design philosophy, Android-powered smartphones were significantly cheaper. In the modern era, the Android OS is incredibly intuitive, with different smartphone makers usually looking to Apple for inspiration in designing features for their own custom flavors.

During his tenure as CEO, Tim Cook also pivoted on this approach, softening Apple's stance and settling many of the ongoing legal disputes, including the long-running battle with Samsung.

• Survey inds 69% of Americans support Bernie Sanders’ policy of requiring AI firms to transfer 50% of their stock to a public fund
• Respondents are also overwhelmingly in favor of giving the federal government the power to block new AI services deemed “risky”
• Support does appear to drop when Sanders' name is mentioned

AI is developing an image problem, and respondents to a new survey have made their feelings clear: Bernie Sanders’ demand that AI firms contribute stock to a massive public fund is widely supported.

Conducted during June 2026 by the nonpartisan survey research company Verasight, the survey consisted of 17 questions sent to 1,690 adults (18 and above), finding over two-thirds (69%) supporting Sanders’ policy – a figure that only drops to 64% once it is revealed which politician the idea is associated with.

The bad news for the AI industry doesn’t end there, as respondents demonstrated a notable distrust for how AI companies conduct themselves, with nearly half (43%) believing that the regulations proposed by the AI companies are designed to benefit those same companies. Incredibly, 30% of respondents trust the US federal government more than companies like OpenAI and Anthropic.

How does AI wealth distribution work?

Senator Bernie Sanders’ plan gives the public a direct stake in America’s largest AI companies, with a one-off tax paid not in cash, but in stock. "Since AI is built on the collective knowledge of humanity, the wealth it generated must benefit humanity," he said, announcing his planned Act on social media.

This would, according to Sanders’ proposal, deliver an annual $1000 check to U.S. citizens, and fund healthcare and education.

"The findings from our latest survey demonstrate a rare instance of bipartisan agreement," noted Ben Leff, CEO and Co-Founder of Verasight.

"There is an undeniable desire among Americans of both parties for federal oversight, absolute transparency, and accountability to ensure AI safety and to enable all Americans to participate in the economic benefits of AI."

Even with a polarizing figure like Bernie Sanders attached, the wealth fund idea retains support, which suggests that while people are happy to use AI to answer questions, streamline processes, and make tasks quicker, they’re less comfortable with the industry’s wider impact.

A poor public perception

While AI companies are becoming increasingly unpopular, it isn’t all bad news. The survey quizzed subjects about their feelings on other industries, including tobacco, pharmaceuticals, and casinos, along with social media. If the tide is turning against AI, it still isn’t regarded as poorly as casinos or tobacco.

Feelings about AI companies are also notably more positive than social media. Given the concerns expressed about federal oversight and public wealth funds, there could be an avenue for OpenAI, Google Gemini, Anthropic, Microsoft, and others to improve the general feeling about AI: accepting a degree of social responsibility.

• JPMorgan CEO Jamie Dimon warns controls may be needed for Claude Mythos
• The AI model from Anthropic is highly advanced, and has been proven to detect zero-day vulnerabilities and even develop working exploits
• The US government has previously instructed Anthropic to block access to foreign nationals, citing security concerns

Artificial intelligence is becoming increasingly powerful, a fact highlighted with the US government’s recent instruction to limit access to Anthropic’s Claude Mythos model.

Now, the CEO of JPMorgan has described the risks the technology poses as a “real issue,” and likened wide access to the AI as “giving ballistic ⁠missiles to individuals.”

Speaking at the Pennsylvania Defense ​and Innovation Summit, JPMorgan’s Jamie Dimon underscored the risks posed by the AI, which has already been shown to both identify and exploit cybersecurity challenges.

Mythos isn’t Skynet

Currently, Claude Mythos is limited to a small selection of organizations, companies, and federal and military departments. Public access to the AI has been blocked, and worldwide access has been blocked due to the potential security issues of this powerful AI.

Claude Mythos is not the type of threat that can become self-aware and take over military appliances like the fictional Skynet of the Terminator movie series. However, it does have the capacity to cause immense damage in the wrong hands.

The AI is capable of detecting cybersecurity vulnerabilities and reporting on them; it is also able to generate automatic exploits. So, for white hat cybersecurity analysts, it is a powerful defensive tool, but in the wrong hands, it is a destructive power. In addition, its automated capabilities make it easy for black hats to scale their cybercrime operation and make ransomware, phishing, and other data-based scams more profitable.

It isn’t just cybersecurity where Anthropic’s Claude Mythos AI has demonstrated such incredible capacities. Scientific research, specifically biology, can be targeted by attackers using AI to turn complex concepts into terms, potentially to misuse the information.

There is also the challenge of a strategic imbalance between single nations or bodies having exclusive access to the technology.

Will the public ever access Anthropic’s Claude Mythos?

Given the security considerations, it seems unlikely that Claude Mythos will be publicy available anytime soon. However, Anthropic has already stated that it intends Mythos-level features and capabilities to become more widely available.

For this to happen, however, it needs to develop and apply various safeguards.

The most likely scenario is that public access to Mythos is eventually unblocked, with restrictions to specific features that relate to cybersecurity and biological/medical tasks and research.

Project Glasswing is already in operation with a handful of key partners (including Amazon Web Services, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, and others), and will probably be expanded until the safeguards are demonstrated to be fit for purpose, and public use.

Via Reuters

• DataDome analysis claims agentic traffic has surged by 45% in Q2 2026
• Meta AI bots have grown over 163% on the previous quarter
• The analysis was conducted by bot management and agent control platform DataDome

If you run a website, every crawl costs bandwidth, resources, logging, and creates CDN transactions, and while search engine crawlers offered the promise of sending visitors, AI bots do not.

Analysis in a report from cybersecurity firm DataDome has shown that while bots from Meta AI have increased activity, they’re not delivering any significant returns to websites.

Conversely, ChatGPT crawlers have reduced in traffic, but are sending more referrals.

AI agent traffic is growing

While Meta AI is usually considered to be the “chatbot within Facebook” it seems that it is becoming something more – and the emergence of the Meta-WebIndexer bot (which grew 163% on Q1) suggests that Meta may be indexing a library of websites, in much the same way Google has done for the past few decades.

The growth of Meta AI as an active crawler is only part of the story, as is ChatGPT’s comparative efficiency. The OpenAI tool seems to know enough about websites, so can provide the answers it already “knows.” Conversely, Meta AI’s activity indexing the web seems to explain its heavy impact in Q2 2026.

But also emerging is the Model Context Protocol (MCP) signal, which connects AI agents with external tools, and differs from standard crawler traffic.

“Q2 showed us that the ground is shifting faster than most organizations realize. Meta now dominates AI traffic on our network, MCP traffic has emerged as a real signal, and ChatGPT is driving more referral value with fewer crawls," noted Jérôme Segura, VP of Threat Research at DataDome.

The differences in the way the AI agents are interacting with websites – some behaving like users, others scraping content – means that organizations need to act accordingly.

“What the data makes clear is that not all agents are created equal. The organizations building policy around these distinctions are the ones gaining an edge, and that's exactly why agent trust adoption is accelerating."

Unfortunately, MCP’s existence and growth into a significant, measurable quantity, means that it should also be treated as part of an organization’s attack surface.

Allocating resources

Given the origins of the report, there is naturally a cybersecurity aspect to this. While ransomware, malware, and phishing are not going anywhere, autonomous software on the web needs addressing in a different way.

Those “organizations building policy” that Segura mentions might, for example, give full crawl access to Google, allow ChatGPT to retrieve results, but rate-limit Meta AI based on its poor return.

Meanwhile, unknown agents – perhaps cybersecurity threats – would require additional verification or be blocked entirely.

• Microsoft sees itself as cheaper and more effective at bundling the full stack
• The company is clearly pushing its own internal models
• Claude also slated for being slower and less accurate

Microsoft is reportedly teaching sales workers how to compare the company's AI offerings to rival companies like OpenAI and Anthropic.

Per Bloomberg reporting, the company's sales staff are being told to emphasize benefits like efficiency and cost advantages when using Microsoft's offerings, which offer a much fuller picture than just the models and tools, extending to compute and other workflow tools.

"Everyone else is selling parts – we’re selling the full end-to-end system," EVP Jay Parikh reportedly told workers. "That’s the story that we all need to get out there and tell in FY27."

Microsoft sales teams up the ante against OpenAI, Anthropic

Clearly, the company wants customers to see the combination of its own models and third-party models, cloud infrastructure, applications and security as better value compared with having to piece these elements together separately.

Copilot EVP Jacob Andreou also reportedly compared Copilot to Claude, accusing Claude of being slower, less accurate and missing certain security integrations.

The sales push comes at a time of change for the company, which has started to push more of its own internal models across different apps and workflows to replace OpenAI and Anthropic models. It's also a marked shift from the company's earlier AI strategy, which leaned heavily on its multibillion-dollar partnership with OpenAI.

Company CEO Satya Nadella also pointed at a major customer, Unilever, which recently switched form an unnamed frontier model to one of Microsoft's own cheaper models to make significant savings.

In April, the company announced that its AI business is now worth around $37 billion annually, marking a 123% year-over-year increase.

• Report finds 38% of employees consider themselves self-taught in AI, just 23% have received formal training
• Nearly half believe it's the employer's responsibility to provide upskilling opportunities
• 88% of workers are Level 1-2 on the readiness scale, only 12% are Level 3-4

New research from TrustedTech has warned of an "AI underclass," whereby uneven access to AI training and support could end up creating a two-tier workforce.

The data claims around three in four (74%) UK decision-makers feel confident using AI at work, and yet only 44% of junior workers feel the same way.

The report also reveals that formal AI training is pretty uneven, with around two in five (38%) employees describing themselves as self-taught in AI.

Insufficient AI training is creating an emerging divide

Additionally, fewer than one-quarter (23%) say they've received AI training from their employer, with 41% criticizing their workplace for not providing enough safety and security training. But despite widespread self-learning, nearly half (47%) believe it's the employer's responsibility to support training and upskilling efforts.

"Employees are being told AI will transform the way they work, yet many have received little training on how to use it effectively, securely or confidently," TrustedTech Chief Visionary Officer Julian Hamood warned.

"The people who are most confident with AI will continue to build skills and productivity, while others risk being left behind through no fault of their own."

All of this is set against a backdrop of rising AI investments and ongoing deployments. Separate Notion data found that 60% of AI decision-makers believe their organization is ready to deploy next-generation agentic AI, but only 36% of employees would agree.

According to Notion's reporting, 88% of workers are at Levels 1 and 2 of AI readiness, seeing the tech as a brainstorming tool or an assistant, leaving only 12% at Levels 3 (team mate) and 4 (system workflows).

One of the biggest differentiators setting advanced organizations apart, Notion says, is effective governance and oversight, which aligns with TrustedTech's findings that workers lack sufficient guidance.

"The leaders pulling ahead are the ones doing it thoughtfully: integrating AI into how work runs, building trust across teams, and measuring real business impact," EMEA GM Andrew McCarthy wrote.

OpenAI's debut of the new GPT-5.6 model prompted the usual ritual of benchmark charts and arguments over whether it is really smarter than the last version, but CEO Sam Altman asked for a little more this time. He publicly asked to see what people actually built with it.

That led to a much more interesting showcase. Developers responded with all kinds of ideas, pilot projects, and even complete services. It makes sense, since OpenAI claims GPT-5.6 is better at coding and more reliable for long tasks. Seeing them turn into real software says much more than a release blog ever could. Here are five that stood out among the deluge.

ChatGPT coworker

The demo from Kitsune Agent Lab almost makes the chat window feel old-fashioned. The AI agent is given a goal and gets on with the job, moving between different tools, making decisions, and keeping track of what it has already done.

The interesting part is how motivated the AI agent appears to keep going and how good it is at remembering what it's done before. Developers have been asking for something like this for a while. AI is far more useful when it can finish the work instead of simply suggesting how you might do it yourself.

Financial chatter

One of the most charming projects makes New York City look like it belonged inside an original Game Boy. It comes complete with chunky pixel graphics but runs on a live digital map of New York that pulls in real-time information, including subway trains, weather conditions, and ferry movements. Instead of wandering through a fictional RPG world, you're exploring a tiny, pixelated version of the city.

A project like this requires far more than generating a few lines of code. It brings together live data feeds, mapping, interface design, and plenty of problem-solving into something that feels polished rather than experimental. It's one reason developers are feeling excited about GPT-5.6

Wardrobe AI

This project uses GPT-5.6 to create a polished AI wardrobe assistant that organizes clothing, suggests outfits, and presents everything through an interface that feels more like a premium consumer app than an experimental AI demo.

It's an impressively complete experience. The application gives users a visual, interactive way to browse their clothes and receive recommendations based on what they already own. The demo also highlights GPT-5.6's strength in developers building entire applications instead of isolated features. It brings together interface design, image generation, organization, and intelligent recommendations that would normally require stitching together several complex systems. GPT-5.6 appears to handle much of that heavy lifting.

Pokémon Go, but for neighborhood cats

One developer made a whole real-world-based game called CatchCat. It's like a digital expansion to a scavenger hunt for cats. Point your phone at a real cat, let the app verify the sighting with its camera, and turn that encounter into a collectible digital cat card with its own personality, rarity, and place in your growing album. It is essentially a creature-collecting game in which the creatures are the neighborhood cats you meet.

Players can build collections, explore community sightings, compete with friends, and gradually fill a living scrapbook of feline encounters, all wrapped in a polished interface that would not look out of place on the App Store or Google Play. Building something like CatchCat means juggling computer vision, mobile development, backend services, and game design.

Tasteful travel

One of the most ambitious projects, Atlas Mode for Pearl, is an interactive globe that turns travel planning into something closer to exploring a living map. Instead of typing destination names into a search box, users can spin the globe, discover places visually, and receive recommendations for restaurants, hotels, and more matched to their personal tastes.

It has an impressive number of moving parts running behind the scenes. The app combines geographic data with an individual taste profile, then layers AI recommendations directly onto an interactive globe. It even has an audio aspect thanks to GPT Voice 2.1. You can talk through vacation ideas instead of endlessly tweaking search filters.

That is a recurring theme among the projects developers rushed to show Sam Altman. The AI is no longer the product itself. Increasingly, it is the engine quietly powering products that people might actually want to use.

The AI buildout is well and truly underway with OpenAI CEO Sam Altman leading the charge, having made various deals with companies including Oracle and Nvidia to guarantee the infrastructure needed to train future AI models is installed. But as this ensues, the spotlight has been thrown on how much energy these models will need.

Eating machines

Altman was speaking during an AI summit in India earlier this year when making the remarks to The Indian Express.

The OpenAI chief was being questioned about the substantial amount of energy that AI has already been consuming – and will be projected to continue consuming – for both training and inference. Bringing these models online, after all, and keeping them running require a huge amount of resources, not only in terms of the energy to power the data centers, but the water for cooling, and the components and resources in building the hardware.

Altman's defence hinged on the idea that people, too, require plenty of energy in order to reach utility — while AI can be trained much quicker. This view, in essence, frames machine intelligence as a like-for-like competitor with human intelligence.

Energy efficiency

Altman argued that humans are deeply inefficient, and that you should compare the total energy spent to create a human expert versus a machine expert. There's also an argument that energy efficiency of AI could improve over time.

But detractors were scathing in their criticism of this entire point of view – saying this framing doesn't take into account the fact that the human brain operates on roughly 20 watts of power. This isn't to mention the ethically gray and dehumanizing nature of the remarks.

OpenAI is building an AI-powered smart speaker nobody wants. That is, if you believe the Bloomberg report from Mark Gurman and you've read his description of said rumored device.

According to the report:

"OpenAI believes the product’s defining feature will be its personality and ability to connect on a humanlike level with users. The speaker incorporates mechanical elements that can move on their own, creating a sense that it is alive and not just an object responding to commands. The machine also will draw on personal information such as emails to better understand its owner."

The news sources, it appears, come from an insider who decided to spill all the juicy details mere hours after Apple dropped a blockbuster trade-secrets lawsuit on OpenAI's head. OpenAI claims it's done nothing of the sort, and recent reports say that Apple's claim that the AI giant has not even responded to Apple's earliest concerns was based on it potentially incorrectly identifying the former Apple employees who left to join OpenAI (allegedly with Apple trade secrets in tow).

Apple's concerns here are twofold: First, these former employees had access to many of Apple's secretive product development details and may even have asked recruits to share fresh details when they approached them to interview for jobs at OpenAI. The other concern is that Apple is already far behind in the AI race, and if Apple's plans for Siri, AI, and a potential robotic desktop home assistant were also leaked, it could harm its ability to catch up in multiple market sectors.

This latest news, which may or may not be accurate, should put Apple's fears to rest.

OpenAI is apparently not building something that could ably compete with any of Apple's key hardware or future hardware initiatives.

First of all, there's the smart-speaker-ness of the whole rumored OpenAI concept. There are already too many smart speakers on the market, many of them with their own smart assistants. Amazon, for instance, is smack in the middle of trying to convince millions of customers that not only do they need Echo devices throughout the home, but they need the AI-powered Alexa+ to guide them through their smart home experiences and, to some extent, their lives.

Apple has its own HomePod, Siri-infused speakers, which may get considerably more powerful with the Gemini foundation model-backed version arriving this Fall.

Put another way, smart speakers are a known quantity in the home consumer electronics space, and I think what most tech companies are realizing is that people like and use them, but mostly in limited ways: they want music, occasional answers to simple questions, and voice control of their smart home devices. That's it.

Why does my speaker think it's alive?

OpenAI appears to be prepared to offer something different: a personality-filled speaker that can watch you, move to engage, seem alive, and generally make you feel uncomfortable.

Obviously, that would not be the objective, but it could be the result. Who needs a speaker that quietly watches you as you walk from your kitchen to the den, waiting and hoping for you to say, "hey ChatGPT, what's up with the Strait of Hormuz today?"

In my home, we have a Psync smart security webcam with one oddball feature: it has a motorized body that can turn almost 360 degrees on its base and lift its thin, rectangular face and camera to keep track of people and alert me to intruders. However, most of the time, it's just watching us move around the house, and I can tell you that my family hates it. Sometimes I come home and find its face forced down so it can't pop up and track anything.

Now, imagine a larger and far smarter OpenAI AI smart speaker in your home, watching, waiting, chiming in when you don't want it to, and generally making people feel uncomfortable.

This will not be the breakout hardware hit OpenAI is hoping for.

Look, I was under the impression that OpenAI (really Jony Ive and Sam Altman) were working on an AI wearable. I didn't love that idea either, but it was a lot less creepy than this.

So, Apple, chill out. OpenAI's plans are no threat to you, even if they do allegedly have a bunch of insidery Apple information.

• AI hallucination can be weaponized, new report warns
• HalluSquatting is short for “adversarial hallucination squatting”
• GitHub Copilot, Gemini CLI, and OpenClaw are all affected

Your favorite AI service could be subverted to deploy code that turns your phone or PC into a botnet, according to researchers at Intuit, Technion, and Tel Aviv University.

The technique has been given the name HalluSquatting, a portmanteau of adversarial hallucination squatting, and is similar to typosquatting in that it relies on a mistake in order to distribute malicious code. While typosquatting might occur with the incorrect input of a website URL, HalluSquatting pivots on an LLM being unable to identify a resource or repository with 100% accuracy.

Relying on an LLM’s tendency to hallucinate repository resource identifiers, this weakness could be scaled up to conduct massive ransomware campaigns, botnets, and more.

Push-me-pull-you

Previous LLM-based malware operations have relied on pull-based attacks. In this scenario, a prompt designed to jailbreak or otherwise subvert the AI is (for example) placed on a website and the LLM encouraged to gather the information, thereby reducing its internal security.

What the researchers have shared in their paper, is that pull techniques are being combined with push attacks, which are traditionally executed as code injection.

The paper’s introduction summary states: “By preemptively registering hallucinated resources—a technique we call adversarial hallucination squatting (HalluSquatting)—we demonstrate remote tool execution and remote code execution at scale across a range of popular agentic LLM applications, which could be exploited to the establishment of a botnet.”

Once an attacker has identified the resource likely to be misnamed by an LLM, and squatted on it (to embed adversarial prompts), the work is done. All that remains is for a user to trigger the resource, the AI chatbot or agent to initiate the response, and the squatted resource will be accessed.

Promptware attack

Following this, the adversarial content held within the squatted resource is activated, triggering the tool invocation stage. This is the promptware attack, where attacker-controlled instructions are executed, with results potentially including turning the device you’re using into a botnet zombie.

LLMs such as the Cursor, Cursor CLI, Windsurf, GitHub Copilot, Cline coding assistants have been used in the testing of this avenue of attack along with Gemini CLI, and the OpenClaw, ZeroClaw, and NanoClaw AI assistants. The researchers successfully achieved remote tool execution (essentially remotely accessing and controlling the LLMs) and remote code execution (RCE, where malicious code is executed remotely).

Some mitigation is available, including LLM developers blocking fetch operations in favor of a search tool, and resource owners enforcing strict naming, perhaps in favor of globally unique resource names. However, these are will require collaboration by disparate parties, and may take a while to implement.

The risk of LLM-based malware is increasing, and some has already been spotted in the wild. Of these, the JADEPUFFER attack is perhaps the most notable, as it isn’t simply AI-based malware – it is a full ransomware attack run entirely by an LLM.

• The three man team is known as “Slopfix”
• It claims to be able to reduce AI generated codebases by up to 65% in size
• They aim to "refactor vibecoded codebases back to maintainability"

Vibecoding has a lot to answer for, not least some excessively large codebases. A new team of software engineers are collaborating to reduce the size of these cumbersome projects… with a $10,000 per week bill.

Slopfix is the name of the team (comprising a trio: Maciej, Kuba, and Krzysztof), but its aim is efficiency and functionality, rather than code golf, where code is reduced to the shortest possible length.

However, while this might seem like a noble task and a service worth paying for, Slopfix isn’t taking a stand against the use of AI. In fact, it is employing AI tools to detect the AI flab in your codebases.

Use an AI to catch an AI

Challenges around vibecoded projects have increased in recent months, as the limitations of the technology become apparent.

While using an AI to program based on your prompts and requirements is straightforward, agents habitually begin to lose context and logic once the project reaches a certain size or age. Once that happens, you’re looking at duplication, features breaking, and of course, the dreaded hallucination.

Slopfix is targeting companies that have adopted vibecoding, built huge codebases, and found that they’re running into issues. To find the problematic AI code, however, Slopfix is employing AI.

They state that a full “screen by screen, endpoint by endpoint” evaluation of the vibecoded app is made, which aims to find the duplicated functions, broken logic, and other issues. There’s also the promise of a two-week warranty for anything they break.

All of this is aided by Claude Code “on a very short leash” which Slopfix uses find problems. They clearly state that “the agent doesn’t get a vote.” Instead, they’re relying on their experience as developers to improve your code.

$10,000 seem a bit steep?

While the price might seem high, $10,000 for one successful week’s work for three seasoned developers shouldn’t really be a budget breaker.

The fee covers successful work only, and as the Slopfix website states, payment is in proportion to how much of the reduction target the team hits, with $10,000 being the price for hitting the target – it’s not the default fee.

However, there is a lot of preparation involved, and the analysis of your codebase is conducted free of charge. If they can't fix your project's issues, they'll let you know and refuse the contract.

As software consultancies go, Slopfix is an unusual case. But as the problems with vibecoded projects begin to become apparent, competing consultancies may begin offering similar services.

• The White House is worried about AI's cybersecurity implications
• Meta urged to submit frontier AI models for review before release
• OpenAI and other major developers have already signed up to do this

The White House is reportedly urging Meta to voluntarily submit its most advanced AI models to the government for security reviews before they get released publicly, per New York Times reporting.

Doing so would allow US policymakers to assess model capabilities, security risks and vulnerabilities, with the administration likely focusing on military implications like cyber warfare and attacks on critical national infrastructure.

According to the report, Meta is one of the few major AI developers that has not yet agreed to participate in the voluntary review program.

Meta pressed to submit AI models to US government for review

Companies like OpenAI, Anthropic, Google, Microsoft and xAI have already entered similar arrangements with the US government, and Meta could be next to sign up.

"We share the administration's goal of advancing U.S. leadership on robust and secure frontier AI," a company spokesperson said (via Reuters).

"While we are working through the details, we hope to sign the agreement soon."

The news follows the signing of an executive order to establish a framework for the government evaluation of advanced AI models before public deployment.

"Advanced AI capabilities make our Nation stronger, but also introduce new national security considerations that require coordinated action," the order reads.

The Five Eyes intelligence alliance (US, UK, Canada, Australia and New Zealand) also recently declared that AI systems are now more capable than ever at launching highly sophisticated cyber attacks. "Frontier Al models are anticipated to exceed current industry expectations," the alliance wrote.

If Meta signs up to participate, it means that virtually all major US frontier AI developers will be voluntarily submitting models for review prior to public release.