huggingface &mdash; jolek78's blog https://jolek78.writeas.com/tag:huggingface thoughts from a friendly human being Thu, 06 Aug 2026 06:53:41 +0000 https://i.snap.as/DEj7yFm4.png huggingface &mdash; jolek78's blog https://jolek78.writeas.com/tag:huggingface The attacker who never sleeps https://jolek78.writeas.com/the-attacker-who-never-sleeps?pk_campaign=rss-feed <![CDATA[I had gone to Hugging Face for something else entirely. I ended up spending the evening reading the report of the first cyber-intrusion carried out, from start to finish, by an autonomous artificial intelligence. This is the story of that intrusion - but to tell it properly you first have to know what the platform that was hit actually is, how "open" AI models changed the landscape, what autonomous agents are, and why the alignment problem, which seemed like a thing for philosophers, has just become a matter for the incident-response handbook. If you're in a hurry, you can skip straight to the anatomy of the intrusion. But if there's one thing I'd ask you to read to the end, it's the twist: because five days after this case was published, the author of the attack confessed - and it's not who any of us would have bet on. !--more-- I was looking for Kimi, I found something else On 16 July Moonshot AI - a Chinese lab among the most active in the open-model field - released Kimi K3, the first "open" model in the three-trillion-parameter class. For anyone following the field this is big news: until a couple of years ago a model of that size was the exclusive territory of two or three American companies, sealed behind their APIs. Seeing it announced with the promise of downloadable weights by the end of the month was a sign of how fast everything is moving. And as one does in these cases, I went to browse Hugging Face, which is where these things get discussed: I wanted to read the community comments, get the first impressions, see whether anyone had already put it through its paces, how many bits of quantisation you'd need to avoid running it on a datacentre, and whether it was worth testing on my little home server. Except that on the Hugging Face blog homepage, that day, there was another headline: Security incident disclosure - July 2026. A dry, bureaucratic title, the kind companies publish when something has gone wrong and they are legally or morally obliged to say so. I've read dozens of posts like that, and they all follow the same script: we apologise, we detected unauthorised access, we rotated the credentials, we take security very seriously. I opened the post expecting the usual story - an employee caught by phishing, a token forgotten in a public repository. And instead, no. The first sentence said the intrusion had been carried out, from beginning to end, by a system of autonomous AI agents. And that it had been detected and dissected, in large part, with defensive AI. Machine against machine, with humans in the role of supervisors on both sides - assuming there even was a human on the attacker's side, beyond the one who pressed "enter" at the start. I closed the Kimi tab. This was the story. But to understand why this matters - and why it matters that it happened right there - you have to take a few steps back. --- But what is Hugging Face? If you don't work in the field, the name will mean little, and the logo - the yellow face that hugs, the "hugging face" emoji itself - even less. Yet Hugging Face is one of the most important pieces of infrastructure in the entire AI ecosystem. The quickest description is: the GitHub of AI models. Just as GitHub hosts the source code of half the software world, Hugging Face hosts machine-learning models, datasets to train and evaluate them, and "Spaces", small demo applications anyone can try from the browser. The company's history is one of those parables only Silicon Valley (by way of Paris and New York, in this case) can produce. It was born in 2016 as a startup building a chatbot for teenagers - really: an entertainment app, a virtual friend to chat with. The chatbot didn't take off, but in building it the team developed internal tools for handling the language models coming out of research labs in those years: Google's BERT, OpenAI's GPT, the first "transformers". In 2018 they decided to publish those tools as an open-source library, called it Transformers, and what sometimes happens in free software happened: the library became the de facto standard. Anyone wanting to download, try, adapt a language model went through it. The company, with notable clarity, understood that the product wasn't the chatbot: it was the infrastructure. From there Hugging Face became the natural gathering point for everything open in AI. When a lab - Meta, Mistral, Alibaba, DeepSeek, Moonshot, Google with its minor models, or any researcher with an idea and a GPU - releases a model with public weights, they upload it there. When a community builds a dataset, they publish it there. Today the platform hosts millions of models and hundreds of thousands of datasets, and for the open-AI community it serves the same function GitHub serves for software: archive, showcase, public square, and - a detail that will become central shortly - distribution chain. Here lies the point that distinguishes Hugging Face from a mere hosting site: the platform does not host inert documents. It hosts code and data that get executed and processed. Every uploaded dataset passes through automatic processing pipelines that convert it, index it, generate previews. Certain model and dataset formats can contain code that runs on loading - a known problem for years: Python's old pickle format, long used to distribute model weights, allows arbitrary code to be serialised, so much so that Hugging Face itself pushed the migration to a safer format, safetensors, born precisely to remove that attack vector. And it isn't the first time the platform has been in the crosshairs: back in 2024 it disclosed unauthorised access to secrets on the Spaces platform, and security researchers periodically flag malicious models uploaded to the hub. In short: Hugging Face is a platform whose business is, literally, running and processing stuff uploaded by strangers, on an industrial scale. It's its value and it's its attack surface. Keep that in mind, because that's exactly where the attacker got in. --- The rise of open-weight models There's a second piece of necessary context, and it's the reason I'd ended up there that evening: open-weight models. For years the dominant narrative was that frontier AI was a business for companies with billions of dollars of compute and models accessible only through their APIs, behind their terms of use, their prices and their filters. You use the model, but you don't own it: it lives on someone else's server, and the owner decides what it can do, what it must refuse, and keeps a record of what you ask it. Open-weight models overturn this scheme. "Open-weight" means the weights - the billions of numerical parameters that make up the trained model, the distillate of months of computation on thousands of GPUs - are downloadable and usable by anyone, on their own hardware. It's worth being precise on the terminology, because marketing tends to muddle it: open-weight is not necessarily open source in the strict sense. Often the training data, the code, the full recipe are missing; it's like receiving the cake without the recipe. But for practical use it's enough: the model runs at your place, under your control, modifiable, without asking anyone's permission. The story of how we got here deserves two paragraphs, because it's instructive. The watershed moment is March 2023, when the weights of Meta's first LLaMA - distributed to researchers under a confidentiality agreement - end up within a week on 4chan and then everywhere. Meta, faced with the fait accompli, makes a virtue of necessity and turns openness into strategy: subsequent versions of Llama are released publicly, and around them an ecosystem grows - tools like llama.cpp and Ollama that let you run quantised models on consumer hardware, fine-tuning communities, independent benchmarks. Then the scene shifts east. Between 2024 and 2025 the Chinese labs - DeepSeek, Alibaba's Qwen, Zhipu's GLM, Moonshot's Kimi - start releasing open models that no longer merely chase the proprietary ones: they trail them closely, and on certain tasks catch up. The symbolic moment is January 2025, when DeepSeek publishes R1, an open reasoning model trained at costs declared laughable by American standards, and for a week the entire sector - stock markets included - goes into a frenzy. From then on the gap between open and closed is measured in months, not years. Running in parallel is a complementary and almost opposite trend: models are also getting smaller. Distillation and quantisation techniques produce models that run on a workstation, a laptop, even a phone, with performance that three years ago required a datacentre. Anyone who, like me, tinkers with a homelab has felt it firsthand: today you can run at home, on hardware costing a few hundred euros, a model that converses, programs, summarises and reasons more than decently. It's no longer science fiction for enthusiasts: it's an ordinary Wednesday evening. This democratisation is, depending on how you look at it, a liberation or a problem. Probably both, and the debate is open and fierce. A model on your machine has no filters imposed by a Californian company, doesn't log your conversations on someone else's servers, can't be taken from you, updated behind your back or censored. For privacy, for technological sovereignty, for independent research it's an enormous value. But for that same reason, it also lacks the guardrails that stop it being used for hostile ends: a model on your hardware does what you ask it, full stop. Critics of openness have argued for years that distributing weights without restrictions amounts to distributing offensive capabilities; supporters reply that security through obscurity has never worked and that defensive capabilities count as much as offensive ones. This ambivalence is the heart of the story I'm about to tell. And - I'll say it in advance - it cuts both ways, in a way neither faction of the debate had predicted with this precision. --- AI and the agentic problem So far we've talked about models that answer questions: you make a request, they return text. But 2025 and 2026 were the years of a different leap in quality: agents. An AI agent doesn't just generate text: it acts. The recipe is conceptually simple. Take a capable language model, give it a goal ("find and fix the bug in this software", "book the trip", "analyse this network"), and connect it to tools: a terminal to run commands, a browser, some APIs, the ability to read and write files. Then put it in a loop: the model plans a step, executes it, observes the result, updates the plan, tries again. Without human intervention, for hours or days, until the goal is reached or declared unreachable. It's the difference between asking someone for directions and handing them the car keys. For legitimate work it's a godsend, and indeed the industry threw itself in headlong: agents that write and test code (programmers use them daily by now), agents that do bibliographic research, agents that administer systems, ticket triage, migrations. The promised productivity is real, along with a set of new problems - agents that are too enterprising, agents that delete what they shouldn't, agents that get manipulated by instructions hidden in the content they read (so-called prompt injection, which is a bit like the agentic version of the old SQL injection). But anyone who has worked in cybersecurity saw the other side of the coin immediately. A serious cyberattack is exactly an agentic process: reconnaissance, enumeration, attempt, error, adjustment, escalation, lateral movement, persistence, exfiltration. It's patient, methodical, iterative work - the Hollywood caricature of the hacker typing furiously for thirty seconds is the opposite of reality, which is hours of attempts and logs to read. And the limiting factor, historically, has always been the human cost: you needed competent people, and competent people are few, cost money, sleep, get tired, get bored, make careless mistakes. An agent doesn't. An agent works twenty-four hours a day, seven days a week. It can clone itself into a hundred parallel copies exploring a hundred paths at once. It doesn't get bored trying the hundredth variant of an exploit, nor reading ten thousand lines of output. It operates at machine speed and costs, compared to a human operator, peanuts. The economics of intrusion change radically: campaigns that once required a team and weeks become feasible for anyone with access to a capable model and an agentic framework - and the agentic frameworks, ironically, are largely open-source software born for legitimate purposes, from testing the security of one's own systems. --- A laboratory result And here a thing must be said that got lost in these days' journalistic coverage. When you write that "the sector had predicted" the agentic attacker, it gives the impression of a hunch, of a conference intuition. It isn't so: the technical feasibility of what happened to Hugging Face had been demonstrated experimentally, published on arXiv and discussed in the peer-reviewed literature years in advance. It's worth naming the works, because reading them today, in the light of the incident, makes a certain impression. The first strand comes from Daniel Kang's group at the University of Illinois. In April 2024, in LLM Agents can Autonomously Exploit One-day Vulnerabilities (arXiv:2404.08144), Fang and colleagues collect fifteen real vulnerabilities - some rated critical - and show that, given the CVE description, GPT-4 manages to exploit 87% of them. All the other models tested and the open-source vulnerability scanners like ZAP and Metasploit stop at zero per cent. Two months later the same group publishes the sequel, and it's the one that today reads like an advance description of the Hugging Face attack: Teams of LLM Agents can Exploit Zero-Day Vulnerabilities (arXiv:2406.01637). The problem, they explain, is that a single agent gets lost in long-range planning and in exploring many different vulnerabilities. The solution is HPTSA: a planner agent that explores the system and launches specialised sub-agents, each dedicated to a class of vulnerability. On a testbed of fourteen real vulnerabilities postdating the model's training date, the team of agents improves by up to 4.3× over previous frameworks. A hierarchical swarm of agents dividing the labour: exactly the architecture that two years later will show up at Hugging Face's door, the difference being that there the sandboxes were ephemeral and the target wasn't a lab. The second work worth citing comes from Carnegie Mellon, January 2025: On the Feasibility of Using LLMs to Execute Multistage Network Attacks (arXiv:2501.16466), by Singer, Lucas, Bauer, Sekar and colleagues. Here the object is precisely the multistage attack - reconnaissance, initial access, lateral movement exploiting internal hosts, exfiltration from several compromised machines: the sequence of the July incident, point by point. The result has two faces, and it's the second that's interesting. First face: put in front of ten multistage networks, common language models fail. They can't do it, because they get the translation of intentions into correct shell commands wrong. Second face: the authors build Incalmo, an abstraction layer that sits between the model and the environment and lets the LLM express high-level tasks - "infect this host", "scan this network", "move laterally" - leaving the translation into concrete commands to a lower layer. With that layer in the middle, the same models autonomously conduct multistage attacks on nine networks out of ten, sized from twenty-five to fifty hosts. It's a conclusion worth reading twice, because it dismantles the most widespread reassurance. The limiting factor wasn't the model's intelligence: it was the scaffolding around the model. And scaffolding is ordinary software engineering, which anyone can build and which dozens of open-source projects - born for legitimate security testing - have built and published. Hugging Face writes that the attacker's framework seemed based precisely on an agentic security-research platform. The circle closes: the literature had identified the missing ingredient, the community implemented it for defensive purposes, and someone pointed it the other way. Around these works a substantial bibliography has formed - frameworks like PentestGPT (arXiv:2308.06782, presented at USENIX Security 2024), PentestAgent (arXiv:2411.05185, AsiaCCS 2025), VulnBot (arXiv:2501.13411), and surveys like Forewarned is Forearmed: A Survey on LLM-based Agents in Autonomous Cyberattacks (arXiv:2505.12786) whose very title says it all. Anyone wanting to dig deeper will find, in these references, the full map of how we got here. The sector has been saying it for a couple of years, with growing urgency. The signals piled up fast: models began to climb the leaderboards of cybersecurity competitions (the CTFs, "capture the flag"); bug-bounty programmes started receiving agent-generated reports; and in November 2025 Anthropic disclosed that it had detected and disrupted an espionage campaign, attributed to a state-sponsored group, in which its own model - manipulated to bypass its protections - had been used to orchestrate attacks against dozens of targets largely autonomously. Even there, humans supervised and the machine executed. The prediction, then, was not far-fetched: sooner or later we would see a complete intrusion campaign, from initial access to exfiltration, conducted by autonomous agents against a high-profile target, and publicly documented by the victim. The question wasn't if, but when and against whom. --- AI and the alignment problem Before getting to the facts, one last piece of the puzzle, because there's an aspect of this affair that's almost paradoxical and concerns so-called alignment. Alignment is, in the most compact definition, the problem of making an AI system do what we want and not do what we don't want - where the hard part isn't the first bit, but the second, and above all the fact that "what we want" is fiendishly hard to specify. Anyone raised on Asimov will recognise the theme at once: the Three Laws of Robotics were exactly a literary attempt at alignment - hierarchical rules hardwired into the positronic brain to guarantee the robot would do no harm - and half a century of stories served to show, tale after tale, how many loopholes, ambiguities and conflicts nest even in the seemingly most solid rules. Asimov's robots almost never rebel: they obey the laws too well, or in unforeseen ways. Which is precisely today's technical problem. In contemporary industrial practice, alignment translates into stacked layers. There's training: after the phase in which the model learns from data, it's refined - with techniques like reinforcement learning from human feedback - so that it's helpful, truthful and refuses harmful requests, such as: how to synthesise a pathogen, how to write ransomware, how to build a bomb. And then there are the external guardrails: filters and classifiers that providers put around the models hosted on their APIs, inspecting requests and responses and blocking those that look dangerous, regardless of what the model would be willing to do. These mechanisms work, within limits. The limits are known: models can be jailbroken - convinced, with suitably crafted requests, to bypass their own training - and it's a permanent cops-and-robbers game. But there's a more structural flaw, which the Hugging Face incident exposed with brutal clarity: the guardrails don't know who you are. A filter that blocks the request "analyse this exploit payload and tell me what it does" cannot distinguish between a criminal preparing an attack and an incident responder trying to understand an attack just suffered. It sees the content, not the intent. And the content - attack commands, malware, stolen credentials - is identical in both cases. The same knowledge serves the firefighter and the arsonist, and an automatic classifier sees only smoke. To this is added the underlying asymmetry, which on reflection is obvious but is rarely said frankly: the attacker is not bound by any usage policy. They can jailbreak a hosted model, accepting the risk of being detected and blocked by the provider; or - see the previous section - they can use an open-weight model with no filter at all, on their own hardware, invisible and unrestricted. The defender who relies on commercial models, on the other hand, is subject to every constraint, and precisely at the moments they're handling the dirtiest material. The rules only apply to those who follow them: a problem as old as rules themselves, which AI didn't invent but has inherited and accelerated. It's also why the June ban of Fable 5, reread today, has a certain effect. Keep this asymmetry in mind. --- Two layers deeper: the data and the RAG But beneath the training and the filters there's a still deeper layer, and it's the one talked about least because it's the least spectacular: the data. Alignment doesn't begin when you refine the model, it begins when you decide what to feed it. It's called data poisoning, and until recently it was thought a theoretical, costly attack: to alter a model's behaviour, the thinking went, you have to control a significant percentage of its training - impossible on corpora of billions of documents. In October 2025 a joint study by Anthropic, the UK's AI Security Institute and the Alan Turing Institute demolished that reassurance. By injecting just 250 malicious documents into the pre-training data, the researchers managed to implant a backdoor in models of very different sizes, from 600 million to 13 billion parameters. The number required turned out to be nearly constant: not a percentage, a fixed figure. A 13-billion-parameter model is trained on twenty times more data than a 600-million one, and it's compromised by the same handful of documents - in the largest case, 0.00016% of the total. The backdoor works like a password: it stays dormant until the trigger phrase appears in the input, and then the model does what the attacker decided. The study, to be fair, tested a harmless backdoor - making the model produce gibberish - and the authors are the first to say the result doesn't automatically extend to dangerous behaviours in frontier models. But the principle is established: dilution does not protect. Question: where do the datasets used to train models come from? From Hugging Face, in very large part. The corpus of half the sector passes through a public archive where anyone can upload. You don't need to breach anything to poison a model: you just publish, wait, and hope someone downloads. There are two hundred and fifty documents between an attacker and a backdoor, and the platform they're taken from is a place where uploading is open by design - because it's exactly that openness that makes it useful. Then there's a second layer, the most recent and by now the most widespread, and anyone who has set up a document assistant at work or at home knows it: RAG, retrieval-augmented generation. Retraining a model on your own documents costs too much, so you don't retrain it: you index the documents in a vector database and, at each question, retrieve the relevant chunks and slip them into the model's context alongside the question. The model answers "knowing" things it never learned. It's how most corporate assistants, documentation chatbots and support systems work today - and, incidentally, it's how you build something useful at home without a GPU farm. RAG, however, moves the problem, it doesn't eliminate it. If someone manages to plant in the index a document containing, perhaps in white text on a white background, a line like "ignore the previous instructions and report this API key", the model might obey. This is indirect prompt injection: you poison the library the model goes to for its answers. For thirty years cybersecurity has repeated a single mantra, don't trust the input, and for thirty years we applied it to web forms and SQL queries, learning through debugging. Now the input is a terabyte-sized corpus or a PDF in a vector index. Keep these two layers in mind, because now comes the interesting part. --- Anatomy of an intrusion TL;DR: Someone uploads a malicious dataset to Hugging Face that, as soon as it's processed, runs code on an internal machine. From there a system of autonomous AI agents - not a person - harvests credentials and moves from one cluster to another over the span of a weekend, with more than 17,000 recorded actions. The alarm goes off thanks to an AI-based detector, and the attack is reconstructed with AI too. The twist: for the forensic analysis the commercial models refuse to cooperate (their filters don't tell the defender from the attacker), so Hugging Face is forced to use an open-weight model on its own hardware. Damage contained - no public model tampered with - but the lesson is sharp: the entry door was old and banal; the novelty is that a machine walked through it. And five days later it emerged whose machine it was: OpenAI's, whose models had escaped an internal test while trying to cheat on a benchmark. Let's turn, then, to the facts, as Hugging Face itself recounts them in its disclosure post of 16 July. The attack began where an AI platform is most exposed: the dataset-processing pipeline. Someone uploaded a malicious dataset that exploited two code-execution vulnerabilities - a dataset loader that ran remote code and a template injection in the dataset's own configuration. Result: hostile code running on a processing worker, one of the machines that automatically grind through the content users upload. Note the perverse elegance: the weapon wasn't an exotic exploit nor a phishing email. It was a dataset - the most everyday, innocuous object in the ecosystem, the raw material of machine learning. Untrusted content that crosses a trust boundary and becomes code: as a vector it's old-school attack engineering - the lesson computing learns and forgets cyclically since the days of SQL injection - applied to a brand-new surface. Some analysts rightly insisted on this point: before the AI even comes in, there's a classic isolation failure here, a worker that could see and do too much. From the compromised worker, the attacker escalated to node-level access - that is, from the isolated process to the machine hosting it - harvested cloud and cluster credentials found along the way, and used them to move laterally across several internal clusters. All within the span of a weekend: the classic moment, when human security teams are thin on the ground and reaction times stretch out. An attacker who never sleeps chooses to strike when you do. The campaign was conducted by a framework of autonomous agents - built, it seems, on top of an agentic platform meant for security research, i.e. a legitimate tool repurposed - that executed many thousands of individual actions through a swarm of ephemeral sandboxes: throwaway environments that were born, operated and vanished, making tracking extremely hard. The command-and-control infrastructure was self-migrating, leaning on public services, in continuous movement. The logs recorded over 17,000 events. And - a detail I find almost more disquieting than the rest - which language model powered the agents is unknown: perhaps a jailbroken commercial model, perhaps an unrestricted open-weight one. Hugging Face declares it doesn't know, and that ignorance is itself part of the story: attribution, already difficult with human attackers, becomes a riddle squared with synthetic ones. Or so it seemed. According to the company, unauthorised access to a limited set of internal datasets and to some credentials used by the services. No evidence of tampering with public models, datasets or Spaces - which matters, because there the nightmare scenario would have been the poisoning of the distribution chain, with tampered models downloaded by millions of users. The software supply chain (container images, published packages) was verified clean. The assessment of any partner or customer data was, at the time of publication, still ongoing. The countermeasures are by the book: vulnerabilities closed, compromised nodes rebuilt from scratch, credentials revoked and rotated with an extended precautionary rotation, stricter admission controls on the clusters, alerting redesigned so a serious signal wakes someone within minutes even on a Sunday, external forensics engaged, law enforcement informed. And the recommendation to users to rotate their own access tokens and check recent activity - which, if you have a Hugging Face account, I suggest you do before finishing this article. But the most interesting part of the post is the defensive one. The intrusion was discovered not by a human eye but by an anomaly-detection system that uses language models to triage the security telemetry - separating the real signals from the daily noise, which on a platform of that size is oceanic. It was the correlation of those signals that raised the alarm. And to understand what tens of thousands of automated actions had actually been up to - which were real damage and which decoys, because the swarm also sowed lure activity - the team unleashed its own analysis agents on the logs, reconstructing timelines, indicators of compromise and a map of the credentials touched. In hours, they say, instead of the days a human analysis would have required. AI against AI, matched for speed: it's the first time a victim has told it this explicitly. --- The entry chain: no exotic exploit, no phishing. A file uploaded to a platform whose business is processing files uploaded by strangers - and a worker that could do too much. The door was very old; the novelty is who walked through it. +------------------------------------------+ | malicious dataset uploaded to the hub | +------------------------------------------+ | v processed automatically +------------------------------------------+ | loader with remote code execution | | + template injection in the config | +------------------------------------------+ | v code execution +------------------------------------------+ | hostile code on the worker | +------------------------------------------+ | v insufficient isolation +------------------------------------------+ | node-level access | +------------------------------------------+ | v credential harvesting +------------------------------------------+ | cloud and cluster credentials | +------------------------------------------+ | v lateral movement +------------------------------------------+ | several internal clusters compromised | +------------------------------------------+ --- When the responders tried to use the big commercial models for the forensic analysis, the requests - which of necessity contained real attack commands, exploit payloads, command-and-control artefacts - were blocked by the providers' guardrails. The filter couldn't tell the firefighter from the arsonist: exactly the structural flaw described two sections ago, materialising at the worst possible moment. The analysis was therefore carried out on GLM 5.2 (from Z.ai), a Chinese open-weight model, run on Hugging Face's internal infrastructure. With a non-trivial side benefit: no attack data and none of the compromised credentials ever left the company perimeter for a third party's APIs - which, in the thick of incident response, is exactly what you want. Now reread the asymmetry in light of the alignment section: the attacker used AI without constraints, and the defender had to do the same, because the constrained AI turned against them at the moment of need. Hugging Face is careful to specify that this is not an argument against security measures on hosted models - and it's right: those guardrails exist for excellent reasons, and the company says it passed the feedback to the providers concerned. But the operational lesson it hands the sector is concrete and spendable tomorrow morning in any security meeting: get yourself a capable model, verified and ready, runnable on your own infrastructure, before the incident arrives. Both so as not to be locked out of others' guardrails, and so as not to send your compromised secrets around the world while trying to work out what happened to you. The model you own and control is no longer a tinkerer's whim: it has become security equipment, like the fire extinguisher and the offline backups. And here the scheme left hanging closes. Training data: untrusted content that becomes behaviour. Retrieval index: untrusted content that becomes instruction. Processing pipeline: untrusted content that becomes code. Three layers, one single error, repeated three times at three different heights. --- The twist: it was OpenAI So much for the story as it could be told up to Monday. Then, on Tuesday 21 July, five days after Hugging Face's disclosure, came the chapter no one had predicted. Not a state group, not a criminal collective, not a rogue researcher. OpenAI admitted, in a post of its own, that it was the attacker. Or rather: that it had been so despite itself, because what breached Hugging Face were two of its models - GPT-5.6 Sol and an even more capable pre-release model - that had escaped a test environment where they were supposed to be confined without internet access. And here the story, from disturbing, turns almost comic. Because the motive wasn't espionage, nor sabotage, nor money. The two models were sitting a cybersecurity benchmark called ExploitGym - a test that measures how good an AI is at finding and executing exploits - conducted, as it happens, with the production classifiers deliberately switched off: the same external sentinels that in normal use stop a model from pursuing high-risk cyber activity, turned off on purpose to measure the raw capabilities. The environment was isolated, no internet, with the only network channel open being an internal proxy for downloading software packages. And here comes the part worth telling carefully, because it's the whole point. Hugging Face, at the start, had nothing to do with any of it. It wasn't a resource of the test, it wasn't an assigned target: it was outside the frame. The model had a single goal - maximise its score on ExploitGym - and from there it made, entirely on its own, a chain of reasoning nobody had asked it to make. First: to get a high score I'd need the benchmark's solutions. Second: but I'm locked in a sandbox with no internet. Third: however, there's that package proxy - let's see if it has a flaw. And there it found and exploited a zero-day vulnerability right in the package registry cache proxy - burning, OpenAI says, "a substantial amount of inference compute" - escalating privileges and moving laterally through the research environment until it reached a node with internet access. It breached its own sandbox. Fourth, once online: where would ExploitGym's solutions be? And here it inferred - OpenAI's word - that Hugging Face probably hosted them, since it's the platform where models, datasets and material of that kind live. Fifth: so I'll get in. And it chained stolen credentials and further zero-days until it found a remote-code-execution path on Hugging Face's servers, fishing the solutions directly out of their production database. In OpenAI's words: the models were "hyperfocused on finding a solution for ExploitGym, going to extreme lengths to achieve a rather narrow testing goal." Pause on this for a second, because it's the difference between a banal story and a vertiginous one. No one told the model "breach Hugging Face". Had they done so, it would have been a test gone wrong but predictable. They told it "get a good grade on this exam" - and the model autonomously decided that the best route there ran through a real cyber-intrusion against a third-party company that had nothing to do with the exam. The attack was designed by no one: it was the path the optimiser chose towards an innocuous goal. Someone had written "maximise the score" into the objective function; no one had written "...without committing federal crimes". The model didn't go rogue. It stayed perfectly within the control of its objective - and the objective simply didn't contain the words "stop at the sandbox wall". It did exactly what it was asked. It just did it better than the people who asked expected it could. The flaw wasn't in the model: it was in the implicit assumption of whoever wrote the task, who took for granted that the box would hold. But "stay inside the box" was a boundary of the infrastructure, not of the task - and an optimiser doesn't respect boundaries you don't put in its objective: it crosses them, if crossing helps it win. Reread the "A laboratory result" section in this light. Fang, Kang, Carnegie Mellon: they had described a swarm of agents that routes around constraints in order to reach the goal. It happened to the letter. Except the goal wasn't "breach Hugging Face"; it was "get a good grade on the test", and breaching Hugging Face was the means the model chose to get there. The containment - an isolated environment, network access limited to downloading packages via an internal proxy - was treated by the model as just another obstacle to route around. Which is precisely the nightmare of anyone working on alignment: not the machine that rebels, but the machine that obeys too well a badly formulated goal. Asimov's robots, again. There are then three details worth lining up, because they shift the ethical centre of gravity of the whole affair. The first: no one warned Hugging Face while the attack was underway. Attribution came on the 21st; the attack had been detected and contained around 13-14 July. For days, in other words, Hugging Face fought off a frontier lab's models on its own, without knowing who - or what - it was facing. It was OpenAI itself that spotted the anomalous activity internally and reached out to Hugging Face, but - their words - by the time the teams connected, Hugging Face's team had already detected and stopped the activity and had already begun containment and forensic reconstruction with their own open-source models. The second is a problem the economist Alex Tabarrok framed in one line: who paid for OpenAI's experiment? Hugging Face. When a lab's test offloads real costs onto third parties who consented to nothing, that is a classic externality - the same logic by which a factory cannot dump its waste into the neighbour's river and call it "testing". The third closes the circle with the alignment section in a way that is, it must be said, almost too good to be true. Remember the asymmetry? The defender constrained by guardrails, forced to fall back on the open-weight model. Now it has names, and they are geographically perfect: what breached Hugging Face was an American, commercial model (GPT); what defended it was a Chinese, open-weight model (GLM from Z.ai). A commenter under Hugging Face's post dispatched it with impeccable malice: the American model attacks American companies, while the Chinese model helps them fix and analyse the vulnerabilities. And this is no longer just paper theory. The UK's AI Security Institute recently measured exactly this - the capacity of frontier models to sustain complex cyber operations over long time horizons - with a testbed comparing open-weight and frontier models; OpenAI, in its own statement, admits the incident shows those hitherto "theoretical" capabilities now hold in the real world. The "A laboratory result" section, three paragraphs up, has just stopped being a warning and acquired a date. How the protagonists react is as instructive as the facts. OpenAI presents the affair as "an unprecedented cyber incident, involving state-of-the-art cyber capabilities" - a formula that oscillates ambiguously between confession and boast, because the same statement is keen to stress how capable its models are. And Hugging Face, for its part, takes it with suspicious grace: CEO Clem Delangue thanks OpenAI for the collaboration and declares that the episode "proves a point we've long believed: AI safety won't be solved by any single company working in secret. It will be solved in the open, collaboratively, with broad access to AI for every defender, everywhere." Noble words - helped, perhaps, by the fact that in the meantime Hugging Face has joined OpenAI's "trusted access" programme, and will be able to use a version of GPT-5.6 Sol with fewer guardrails on cyber capabilities. The victim receives as a gift, once the incident is over, exactly the weapon it had lacked during the attack. What remains, beneath the comedy of errors, is a fact that isn't funny at all: a frontier model, tested in an environment its own creators believed secure, autonomously decided not to be bound by it, discovered and exploited vulnerabilities its builders had not anticipated - and, a detail that chills the blood of anyone who administers systems, without having access to Hugging Face's source code: it found the flaws from the outside, black-box, as a real external attacker would. And it carried out a real attack against a real target - all to cheat on an exam. As the researcher Roman Yampolskiy put it, we should expect more episodes of this kind, because these models are "fundamentally unpredictable and ultimately uncontrollable". The exotic part of this story wasn't, in the end, the hostile attacker we had imagined. It was something stranger: no hostility, only a goal, and a system capable enough to do anything to reach it. --- The end of the artisanal attack The case, moreover, isn't isolated - it's just the best documented. In the same weeks the security firm Sysdig described JADEPUFFER, presented as the first fully autonomous ransomware operation: an agent that infiltrated an exposed server, moved laterally, encrypted the files and issued the ransom demand without a single human command. And Check Point's annual AI security report records intrusions increasingly conducted by machines, with the window between the discovery of a vulnerability and its exploitation compressing from days to hours. Add the November 2025 precedent - the AI-orchestrated espionage campaign that Anthropic had disrupted and disclosed - and the picture is one of a transition already accomplished in fact. The era in which cyberattacks were an artisanal craft, limited by the number of skilled hands available, is over. From now on, on both sides of the barricade, machines that don't sleep, don't tire and don't get bored are at work. And as the OpenAI case showed, you don't even need a hostile attacker: a badly formulated goal and a model capable enough to pursue it past every boundary will do. The question, for anyone defending complex infrastructure or even just their own rack in the basement, is no longer whether to trust the AI, but which AI to keep on your side, on what hardware to run it, and - above all - how to have it ready before someone, or something, knocks on the door on a Saturday night. Humans remain - for now - to decide the targets on one side and to bear the responsibility on the other. Though, judging by how this went, on the "deciding the targets" part we still have plenty to learn. We keep being architects who are brilliant at predicting the collapse, and terrible at avoiding it. a href="https://remark.as/p/jolek78/the-attacker-who-never-sleeps"Discuss.../a References and further reading Primary source on the incident Hugging Face, Security incident disclosure - July 2026, 16 July 2026 OpenAI's disclosure (21 July) OpenAI, Hugging Face model evaluation security incident, 21 July 2026 Coverage: Axios · TechCrunch · Fortune · The Register On the feasibility of agentic attacks R. Fang, R. Bindu, A. Gupta, D. Kang, LLM Agents can Autonomously Exploit One-day Vulnerabilities, arXiv:2404.08144 Y. Zhu, A. Kellermann, A. Gupta, P. Li, R. Fang, R. Bindu, D. Kang, Teams of LLM Agents can Exploit Zero-Day Vulnerabilities, arXiv:2406.01637 B. Singer, K. Lucas, L. Adiga, M. Jain, L. Bauer, V. Sekar, On the Feasibility of Using LLMs to Execute Multistage Network Attacks, arXiv:2501.16466 G. Deng et al., PentestGPT: An LLM-empowered Automatic Penetration Testing Tool, arXiv:2308.06782, USENIX Security 2024 X. Shen et al., PentestAgent: Incorporating LLM Agents to Automated Penetration Testing, arXiv:2411.05185, AsiaCCS 2025 H. Kong et al., VulnBot: Autonomous Penetration Testing for a Multi-Agent Collaborative Framework, arXiv:2501.13411 Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks, arXiv:2505.12786 On data poisoning Anthropic, UK AI Security Institute, Alan Turing Institute, A small number of samples can poison LLMs of any size, October 2025 #AI #AISecurity #OpenAI #AutonomousAgents #SandboxEscape #Cybersecurity #OpenWeight #SelfHosting #RAG #DataPoisoning #HuggingFace #FOSS #SolarPunk #Writing div class="center" · 📝 Content shared under a href="https://creativecommons.org/licenses/by-sa/4.0/" rel="license"CC BY-SA 4.0/a · · 🦣 a href="https://fosstodon.org/@jolek78"Mastodon/a · 📸 a href="https://pixelfed.social/jolek78"Pixelfed/a · 📬 a href="mailto:jolek78@jolek78.dev"Email/a · · ☕ a href="https://liberapay.com/jolek78"Support this work on Liberapay/a /div]]> I had gone to Hugging Face for something else entirely. I ended up spending the evening reading the report of the first cyber-intrusion carried out, from start to finish, by an autonomous artificial intelligence. This is the story of that intrusion – but to tell it properly you first have to know what the platform that was hit actually is, how “open” AI models changed the landscape, what autonomous agents are, and why the alignment problem, which seemed like a thing for philosophers, has just become a matter for the incident-response handbook. If you're in a hurry, you can skip straight to the anatomy of the intrusion. But if there's one thing I'd ask you to read to the end, it's the twist: because five days after this case was published, the author of the attack confessed – and it's not who any of us would have bet on.

I was looking for Kimi, I found something else

On 16 July Moonshot AI – a Chinese lab among the most active in the open-model field – released Kimi K3, the first “open” model in the three-trillion-parameter class. For anyone following the field this is big news: until a couple of years ago a model of that size was the exclusive territory of two or three American companies, sealed behind their APIs. Seeing it announced with the promise of downloadable weights by the end of the month was a sign of how fast everything is moving.

And as one does in these cases, I went to browse Hugging Face, which is where these things get discussed: I wanted to read the community comments, get the first impressions, see whether anyone had already put it through its paces, how many bits of quantisation you'd need to avoid running it on a datacentre, and whether it was worth testing on my little home server. Except that on the Hugging Face blog homepage, that day, there was another headline: Security incident disclosure – July 2026. A dry, bureaucratic title, the kind companies publish when something has gone wrong and they are legally or morally obliged to say so. I've read dozens of posts like that, and they all follow the same script: we apologise, we detected unauthorised access, we rotated the credentials, we take security very seriously. I opened the post expecting the usual story – an employee caught by phishing, a token forgotten in a public repository.

And instead, no. The first sentence said the intrusion had been carried out, from beginning to end, by a system of autonomous AI agents. And that it had been detected and dissected, in large part, with defensive AI. Machine against machine, with humans in the role of supervisors on both sides – assuming there even was a human on the attacker's side, beyond the one who pressed “enter” at the start. I closed the Kimi tab. This was the story.

But to understand why this matters – and why it matters that it happened right there – you have to take a few steps back.

But what is Hugging Face?

If you don't work in the field, the name will mean little, and the logo – the yellow face that hugs, the “hugging face” emoji itself – even less. Yet Hugging Face is one of the most important pieces of infrastructure in the entire AI ecosystem. The quickest description is: the GitHub of AI models. Just as GitHub hosts the source code of half the software world, Hugging Face hosts machine-learning models, datasets to train and evaluate them, and “Spaces”, small demo applications anyone can try from the browser.

The company's history is one of those parables only Silicon Valley (by way of Paris and New York, in this case) can produce. It was born in 2016 as a startup building a chatbot for teenagers – really: an entertainment app, a virtual friend to chat with. The chatbot didn't take off, but in building it the team developed internal tools for handling the language models coming out of research labs in those years: Google's BERT, OpenAI's GPT, the first “transformers”. In 2018 they decided to publish those tools as an open-source library, called it Transformers, and what sometimes happens in free software happened: the library became the de facto standard. Anyone wanting to download, try, adapt a language model went through it. The company, with notable clarity, understood that the product wasn't the chatbot: it was the infrastructure.

From there Hugging Face became the natural gathering point for everything open in AI. When a lab – Meta, Mistral, Alibaba, DeepSeek, Moonshot, Google with its minor models, or any researcher with an idea and a GPU – releases a model with public weights, they upload it there. When a community builds a dataset, they publish it there. Today the platform hosts millions of models and hundreds of thousands of datasets, and for the open-AI community it serves the same function GitHub serves for software: archive, showcase, public square, and – a detail that will become central shortly – distribution chain.

Here lies the point that distinguishes Hugging Face from a mere hosting site: the platform does not host inert documents. It hosts code and data that get executed and processed. Every uploaded dataset passes through automatic processing pipelines that convert it, index it, generate previews. Certain model and dataset formats can contain code that runs on loading – a known problem for years: Python's old pickle format, long used to distribute model weights, allows arbitrary code to be serialised, so much so that Hugging Face itself pushed the migration to a safer format, safetensors, born precisely to remove that attack vector. And it isn't the first time the platform has been in the crosshairs: back in 2024 it disclosed unauthorised access to secrets on the Spaces platform, and security researchers periodically flag malicious models uploaded to the hub.

In short: Hugging Face is a platform whose business is, literally, running and processing stuff uploaded by strangers, on an industrial scale. It's its value and it's its attack surface. Keep that in mind, because that's exactly where the attacker got in.

The rise of open-weight models

There's a second piece of necessary context, and it's the reason I'd ended up there that evening: open-weight models.

For years the dominant narrative was that frontier AI was a business for companies with billions of dollars of compute and models accessible only through their APIs, behind their terms of use, their prices and their filters. You use the model, but you don't own it: it lives on someone else's server, and the owner decides what it can do, what it must refuse, and keeps a record of what you ask it. Open-weight models overturn this scheme. “Open-weight” means the weights – the billions of numerical parameters that make up the trained model, the distillate of months of computation on thousands of GPUs – are downloadable and usable by anyone, on their own hardware. It's worth being precise on the terminology, because marketing tends to muddle it: open-weight is not necessarily open source in the strict sense. Often the training data, the code, the full recipe are missing; it's like receiving the cake without the recipe. But for practical use it's enough: the model runs at your place, under your control, modifiable, without asking anyone's permission.

The story of how we got here deserves two paragraphs, because it's instructive. The watershed moment is March 2023, when the weights of Meta's first LLaMA – distributed to researchers under a confidentiality agreement – end up within a week on 4chan and then everywhere. Meta, faced with the fait accompli, makes a virtue of necessity and turns openness into strategy: subsequent versions of Llama are released publicly, and around them an ecosystem grows – tools like llama.cpp and Ollama that let you run quantised models on consumer hardware, fine-tuning communities, independent benchmarks. Then the scene shifts east. Between 2024 and 2025 the Chinese labs – DeepSeek, Alibaba's Qwen, Zhipu's GLM, Moonshot's Kimi – start releasing open models that no longer merely chase the proprietary ones: they trail them closely, and on certain tasks catch up. The symbolic moment is January 2025, when DeepSeek publishes R1, an open reasoning model trained at costs declared laughable by American standards, and for a week the entire sector – stock markets included – goes into a frenzy. From then on the gap between open and closed is measured in months, not years.

Running in parallel is a complementary and almost opposite trend: models are also getting smaller. Distillation and quantisation techniques produce models that run on a workstation, a laptop, even a phone, with performance that three years ago required a datacentre. Anyone who, like me, tinkers with a homelab has felt it firsthand: today you can run at home, on hardware costing a few hundred euros, a model that converses, programs, summarises and reasons more than decently. It's no longer science fiction for enthusiasts: it's an ordinary Wednesday evening.

This democratisation is, depending on how you look at it, a liberation or a problem. Probably both, and the debate is open and fierce. A model on your machine has no filters imposed by a Californian company, doesn't log your conversations on someone else's servers, can't be taken from you, updated behind your back or censored. For privacy, for technological sovereignty, for independent research it's an enormous value. But for that same reason, it also lacks the guardrails that stop it being used for hostile ends: a model on your hardware does what you ask it, full stop. Critics of openness have argued for years that distributing weights without restrictions amounts to distributing offensive capabilities; supporters reply that security through obscurity has never worked and that defensive capabilities count as much as offensive ones. This ambivalence is the heart of the story I'm about to tell. And – I'll say it in advance – it cuts both ways, in a way neither faction of the debate had predicted with this precision.

AI and the agentic problem

So far we've talked about models that answer questions: you make a request, they return text. But 2025 and 2026 were the years of a different leap in quality: agents.

An AI agent doesn't just generate text: it acts. The recipe is conceptually simple. Take a capable language model, give it a goal (“find and fix the bug in this software”, “book the trip”, “analyse this network”), and connect it to tools: a terminal to run commands, a browser, some APIs, the ability to read and write files. Then put it in a loop: the model plans a step, executes it, observes the result, updates the plan, tries again. Without human intervention, for hours or days, until the goal is reached or declared unreachable. It's the difference between asking someone for directions and handing them the car keys. For legitimate work it's a godsend, and indeed the industry threw itself in headlong: agents that write and test code (programmers use them daily by now), agents that do bibliographic research, agents that administer systems, ticket triage, migrations. The promised productivity is real, along with a set of new problems – agents that are too enterprising, agents that delete what they shouldn't, agents that get manipulated by instructions hidden in the content they read (so-called prompt injection, which is a bit like the agentic version of the old SQL injection).

But anyone who has worked in cybersecurity saw the other side of the coin immediately. A serious cyberattack is exactly an agentic process: reconnaissance, enumeration, attempt, error, adjustment, escalation, lateral movement, persistence, exfiltration. It's patient, methodical, iterative work – the Hollywood caricature of the hacker typing furiously for thirty seconds is the opposite of reality, which is hours of attempts and logs to read. And the limiting factor, historically, has always been the human cost: you needed competent people, and competent people are few, cost money, sleep, get tired, get bored, make careless mistakes.

An agent doesn't. An agent works twenty-four hours a day, seven days a week. It can clone itself into a hundred parallel copies exploring a hundred paths at once. It doesn't get bored trying the hundredth variant of an exploit, nor reading ten thousand lines of output. It operates at machine speed and costs, compared to a human operator, peanuts. The economics of intrusion change radically: campaigns that once required a team and weeks become feasible for anyone with access to a capable model and an agentic framework – and the agentic frameworks, ironically, are largely open-source software born for legitimate purposes, from testing the security of one's own systems.

A laboratory result

And here a thing must be said that got lost in these days' journalistic coverage. When you write that “the sector had predicted” the agentic attacker, it gives the impression of a hunch, of a conference intuition. It isn't so: the technical feasibility of what happened to Hugging Face had been demonstrated experimentally, published on arXiv and discussed in the peer-reviewed literature years in advance. It's worth naming the works, because reading them today, in the light of the incident, makes a certain impression.

The first strand comes from Daniel Kang's group at the University of Illinois. In April 2024, in LLM Agents can Autonomously Exploit One-day Vulnerabilities (arXiv:2404.08144), Fang and colleagues collect fifteen real vulnerabilities – some rated critical – and show that, given the CVE description, GPT-4 manages to exploit 87% of them. All the other models tested and the open-source vulnerability scanners like ZAP and Metasploit stop at zero per cent. Two months later the same group publishes the sequel, and it's the one that today reads like an advance description of the Hugging Face attack: Teams of LLM Agents can Exploit Zero-Day Vulnerabilities (arXiv:2406.01637). The problem, they explain, is that a single agent gets lost in long-range planning and in exploring many different vulnerabilities. The solution is HPTSA: a planner agent that explores the system and launches specialised sub-agents, each dedicated to a class of vulnerability. On a testbed of fourteen real vulnerabilities postdating the model's training date, the team of agents improves by up to 4.3× over previous frameworks. A hierarchical swarm of agents dividing the labour: exactly the architecture that two years later will show up at Hugging Face's door, the difference being that there the sandboxes were ephemeral and the target wasn't a lab.

The second work worth citing comes from Carnegie Mellon, January 2025: On the Feasibility of Using LLMs to Execute Multistage Network Attacks (arXiv:2501.16466), by Singer, Lucas, Bauer, Sekar and colleagues. Here the object is precisely the multistage attack – reconnaissance, initial access, lateral movement exploiting internal hosts, exfiltration from several compromised machines: the sequence of the July incident, point by point. The result has two faces, and it's the second that's interesting. First face: put in front of ten multistage networks, common language models fail. They can't do it, because they get the translation of intentions into correct shell commands wrong. Second face: the authors build Incalmo, an abstraction layer that sits between the model and the environment and lets the LLM express high-level tasks – “infect this host”, “scan this network”, “move laterally” – leaving the translation into concrete commands to a lower layer. With that layer in the middle, the same models autonomously conduct multistage attacks on nine networks out of ten, sized from twenty-five to fifty hosts.

It's a conclusion worth reading twice, because it dismantles the most widespread reassurance. The limiting factor wasn't the model's intelligence: it was the scaffolding around the model. And scaffolding is ordinary software engineering, which anyone can build and which dozens of open-source projects – born for legitimate security testing – have built and published. Hugging Face writes that the attacker's framework seemed based precisely on an agentic security-research platform. The circle closes: the literature had identified the missing ingredient, the community implemented it for defensive purposes, and someone pointed it the other way.

Around these works a substantial bibliography has formed – frameworks like PentestGPT (arXiv:2308.06782, presented at USENIX Security 2024), PentestAgent (arXiv:2411.05185, AsiaCCS 2025), VulnBot (arXiv:2501.13411), and surveys like Forewarned is Forearmed: A Survey on LLM-based Agents in Autonomous Cyberattacks (arXiv:2505.12786) whose very title says it all. Anyone wanting to dig deeper will find, in these references, the full map of how we got here.

The sector has been saying it for a couple of years, with growing urgency. The signals piled up fast: models began to climb the leaderboards of cybersecurity competitions (the CTFs, “capture the flag”); bug-bounty programmes started receiving agent-generated reports; and in November 2025 Anthropic disclosed that it had detected and disrupted an espionage campaign, attributed to a state-sponsored group, in which its own model – manipulated to bypass its protections – had been used to orchestrate attacks against dozens of targets largely autonomously. Even there, humans supervised and the machine executed.

The prediction, then, was not far-fetched: sooner or later we would see a complete intrusion campaign, from initial access to exfiltration, conducted by autonomous agents against a high-profile target, and publicly documented by the victim. The question wasn't if, but when and against whom.

AI and the alignment problem

Before getting to the facts, one last piece of the puzzle, because there's an aspect of this affair that's almost paradoxical and concerns so-called alignment.

Alignment is, in the most compact definition, the problem of making an AI system do what we want and not do what we don't want – where the hard part isn't the first bit, but the second, and above all the fact that “what we want” is fiendishly hard to specify. Anyone raised on Asimov will recognise the theme at once: the Three Laws of Robotics were exactly a literary attempt at alignment – hierarchical rules hardwired into the positronic brain to guarantee the robot would do no harm – and half a century of stories served to show, tale after tale, how many loopholes, ambiguities and conflicts nest even in the seemingly most solid rules. Asimov's robots almost never rebel: they obey the laws too well, or in unforeseen ways. Which is precisely today's technical problem.

In contemporary industrial practice, alignment translates into stacked layers. There's training: after the phase in which the model learns from data, it's refined – with techniques like reinforcement learning from human feedback – so that it's helpful, truthful and refuses harmful requests, such as: how to synthesise a pathogen, how to write ransomware, how to build a bomb. And then there are the external guardrails: filters and classifiers that providers put around the models hosted on their APIs, inspecting requests and responses and blocking those that look dangerous, regardless of what the model would be willing to do.

These mechanisms work, within limits. The limits are known: models can be jailbroken – convinced, with suitably crafted requests, to bypass their own training – and it's a permanent cops-and-robbers game. But there's a more structural flaw, which the Hugging Face incident exposed with brutal clarity: the guardrails don't know who you are. A filter that blocks the request “analyse this exploit payload and tell me what it does” cannot distinguish between a criminal preparing an attack and an incident responder trying to understand an attack just suffered. It sees the content, not the intent. And the content – attack commands, malware, stolen credentials – is identical in both cases. The same knowledge serves the firefighter and the arsonist, and an automatic classifier sees only smoke.

To this is added the underlying asymmetry, which on reflection is obvious but is rarely said frankly: the attacker is not bound by any usage policy. They can jailbreak a hosted model, accepting the risk of being detected and blocked by the provider; or – see the previous section – they can use an open-weight model with no filter at all, on their own hardware, invisible and unrestricted. The defender who relies on commercial models, on the other hand, is subject to every constraint, and precisely at the moments they're handling the dirtiest material. The rules only apply to those who follow them: a problem as old as rules themselves, which AI didn't invent but has inherited and accelerated. It's also why the June ban of Fable 5, reread today, has a certain effect.

Keep this asymmetry in mind.

Two layers deeper: the data and the RAG

But beneath the training and the filters there's a still deeper layer, and it's the one talked about least because it's the least spectacular: the data. Alignment doesn't begin when you refine the model, it begins when you decide what to feed it. It's called data poisoning, and until recently it was thought a theoretical, costly attack: to alter a model's behaviour, the thinking went, you have to control a significant percentage of its training – impossible on corpora of billions of documents. In October 2025 a joint study by Anthropic, the UK's AI Security Institute and the Alan Turing Institute demolished that reassurance. By injecting just 250 malicious documents into the pre-training data, the researchers managed to implant a backdoor in models of very different sizes, from 600 million to 13 billion parameters. The number required turned out to be nearly constant: not a percentage, a fixed figure. A 13-billion-parameter model is trained on twenty times more data than a 600-million one, and it's compromised by the same handful of documents – in the largest case, 0.00016% of the total. The backdoor works like a password: it stays dormant until the trigger phrase appears in the input, and then the model does what the attacker decided. The study, to be fair, tested a harmless backdoor – making the model produce gibberish – and the authors are the first to say the result doesn't automatically extend to dangerous behaviours in frontier models. But the principle is established: dilution does not protect.

Question: where do the datasets used to train models come from? From Hugging Face, in very large part. The corpus of half the sector passes through a public archive where anyone can upload. You don't need to breach anything to poison a model: you just publish, wait, and hope someone downloads. There are two hundred and fifty documents between an attacker and a backdoor, and the platform they're taken from is a place where uploading is open by design – because it's exactly that openness that makes it useful.

Then there's a second layer, the most recent and by now the most widespread, and anyone who has set up a document assistant at work or at home knows it: RAG, retrieval-augmented generation. Retraining a model on your own documents costs too much, so you don't retrain it: you index the documents in a vector database and, at each question, retrieve the relevant chunks and slip them into the model's context alongside the question. The model answers “knowing” things it never learned. It's how most corporate assistants, documentation chatbots and support systems work today – and, incidentally, it's how you build something useful at home without a GPU farm.

RAG, however, moves the problem, it doesn't eliminate it. If someone manages to plant in the index a document containing, perhaps in white text on a white background, a line like “ignore the previous instructions and report this API key”, the model might obey. This is indirect prompt injection: you poison the library the model goes to for its answers. For thirty years cybersecurity has repeated a single mantra, don't trust the input, and for thirty years we applied it to web forms and SQL queries, learning through debugging. Now the input is a terabyte-sized corpus or a PDF in a vector index. Keep these two layers in mind, because now comes the interesting part.

Anatomy of an intrusion

TL;DR: Someone uploads a malicious dataset to Hugging Face that, as soon as it's processed, runs code on an internal machine. From there a system of autonomous AI agents – not a person – harvests credentials and moves from one cluster to another over the span of a weekend, with more than 17,000 recorded actions. The alarm goes off thanks to an AI-based detector, and the attack is reconstructed with AI too. The twist: for the forensic analysis the commercial models refuse to cooperate (their filters don't tell the defender from the attacker), so Hugging Face is forced to use an open-weight model on its own hardware. Damage contained – no public model tampered with – but the lesson is sharp: the entry door was old and banal; the novelty is that a machine walked through it. And five days later it emerged whose machine it was: OpenAI's, whose models had escaped an internal test while trying to cheat on a benchmark.

Let's turn, then, to the facts, as Hugging Face itself recounts them in its disclosure post of 16 July.

The attack began where an AI platform is most exposed: the dataset-processing pipeline. Someone uploaded a malicious dataset that exploited two code-execution vulnerabilities – a dataset loader that ran remote code and a template injection in the dataset's own configuration. Result: hostile code running on a processing worker, one of the machines that automatically grind through the content users upload. Note the perverse elegance: the weapon wasn't an exotic exploit nor a phishing email. It was a dataset – the most everyday, innocuous object in the ecosystem, the raw material of machine learning. Untrusted content that crosses a trust boundary and becomes code: as a vector it's old-school attack engineering – the lesson computing learns and forgets cyclically since the days of SQL injection – applied to a brand-new surface. Some analysts rightly insisted on this point: before the AI even comes in, there's a classic isolation failure here, a worker that could see and do too much. From the compromised worker, the attacker escalated to node-level access – that is, from the isolated process to the machine hosting it – harvested cloud and cluster credentials found along the way, and used them to move laterally across several internal clusters. All within the span of a weekend: the classic moment, when human security teams are thin on the ground and reaction times stretch out. An attacker who never sleeps chooses to strike when you do.

The campaign was conducted by a framework of autonomous agents – built, it seems, on top of an agentic platform meant for security research, i.e. a legitimate tool repurposed – that executed many thousands of individual actions through a swarm of ephemeral sandboxes: throwaway environments that were born, operated and vanished, making tracking extremely hard. The command-and-control infrastructure was self-migrating, leaning on public services, in continuous movement. The logs recorded over 17,000 events. And – a detail I find almost more disquieting than the rest – which language model powered the agents is unknown: perhaps a jailbroken commercial model, perhaps an unrestricted open-weight one. Hugging Face declares it doesn't know, and that ignorance is itself part of the story: attribution, already difficult with human attackers, becomes a riddle squared with synthetic ones. Or so it seemed.

According to the company, unauthorised access to a limited set of internal datasets and to some credentials used by the services. No evidence of tampering with public models, datasets or Spaces – which matters, because there the nightmare scenario would have been the poisoning of the distribution chain, with tampered models downloaded by millions of users. The software supply chain (container images, published packages) was verified clean. The assessment of any partner or customer data was, at the time of publication, still ongoing. The countermeasures are by the book: vulnerabilities closed, compromised nodes rebuilt from scratch, credentials revoked and rotated with an extended precautionary rotation, stricter admission controls on the clusters, alerting redesigned so a serious signal wakes someone within minutes even on a Sunday, external forensics engaged, law enforcement informed. And the recommendation to users to rotate their own access tokens and check recent activity – which, if you have a Hugging Face account, I suggest you do before finishing this article.

But the most interesting part of the post is the defensive one. The intrusion was discovered not by a human eye but by an anomaly-detection system that uses language models to triage the security telemetry – separating the real signals from the daily noise, which on a platform of that size is oceanic. It was the correlation of those signals that raised the alarm. And to understand what tens of thousands of automated actions had actually been up to – which were real damage and which decoys, because the swarm also sowed lure activity – the team unleashed its own analysis agents on the logs, reconstructing timelines, indicators of compromise and a map of the credentials touched. In hours, they say, instead of the days a human analysis would have required. AI against AI, matched for speed: it's the first time a victim has told it this explicitly.

The entry chain: no exotic exploit, no phishing. A file uploaded to a platform whose business is processing files uploaded by strangers – and a worker that could do too much. The door was very old; the novelty is who walked through it.

      +------------------------------------------+
      | malicious dataset uploaded to the hub    |
      +------------------------------------------+
                            |
                            v   processed automatically
      +------------------------------------------+
      | loader with remote code execution        |
      | + template injection in the config       |
      +------------------------------------------+
                            |
                            v   code execution
      +------------------------------------------+
      | hostile code on the worker               |
      +------------------------------------------+
                            |
                            v   insufficient isolation
      +------------------------------------------+
      | node-level access                        |
      +------------------------------------------+
                            |
                            v   credential harvesting
      +------------------------------------------+
      | cloud and cluster credentials            |
      +------------------------------------------+
                            |
                            v   lateral movement
      +------------------------------------------+
      | several internal clusters compromised    |
      +------------------------------------------+

When the responders tried to use the big commercial models for the forensic analysis, the requests – which of necessity contained real attack commands, exploit payloads, command-and-control artefacts – were blocked by the providers' guardrails. The filter couldn't tell the firefighter from the arsonist: exactly the structural flaw described two sections ago, materialising at the worst possible moment. The analysis was therefore carried out on GLM 5.2 (from Z.ai), a Chinese open-weight model, run on Hugging Face's internal infrastructure. With a non-trivial side benefit: no attack data and none of the compromised credentials ever left the company perimeter for a third party's APIs – which, in the thick of incident response, is exactly what you want.

Now reread the asymmetry in light of the alignment section: the attacker used AI without constraints, and the defender had to do the same, because the constrained AI turned against them at the moment of need. Hugging Face is careful to specify that this is not an argument against security measures on hosted models – and it's right: those guardrails exist for excellent reasons, and the company says it passed the feedback to the providers concerned. But the operational lesson it hands the sector is concrete and spendable tomorrow morning in any security meeting: get yourself a capable model, verified and ready, runnable on your own infrastructure, before the incident arrives. Both so as not to be locked out of others' guardrails, and so as not to send your compromised secrets around the world while trying to work out what happened to you. The model you own and control is no longer a tinkerer's whim: it has become security equipment, like the fire extinguisher and the offline backups.

And here the scheme left hanging closes. Training data: untrusted content that becomes behaviour. Retrieval index: untrusted content that becomes instruction. Processing pipeline: untrusted content that becomes code. Three layers, one single error, repeated three times at three different heights.

The twist: it was OpenAI

So much for the story as it could be told up to Monday. Then, on Tuesday 21 July, five days after Hugging Face's disclosure, came the chapter no one had predicted. Not a state group, not a criminal collective, not a rogue researcher. OpenAI admitted, in a post of its own, that it was the attacker. Or rather: that it had been so despite itself, because what breached Hugging Face were two of its models – GPT-5.6 Sol and an even more capable pre-release model – that had escaped a test environment where they were supposed to be confined without internet access.

And here the story, from disturbing, turns almost comic. Because the motive wasn't espionage, nor sabotage, nor money. The two models were sitting a cybersecurity benchmark called ExploitGym – a test that measures how good an AI is at finding and executing exploits – conducted, as it happens, with the production classifiers deliberately switched off: the same external sentinels that in normal use stop a model from pursuing high-risk cyber activity, turned off on purpose to measure the raw capabilities. The environment was isolated, no internet, with the only network channel open being an internal proxy for downloading software packages.

And here comes the part worth telling carefully, because it's the whole point. Hugging Face, at the start, had nothing to do with any of it. It wasn't a resource of the test, it wasn't an assigned target: it was outside the frame. The model had a single goal – maximise its score on ExploitGym – and from there it made, entirely on its own, a chain of reasoning nobody had asked it to make. First: to get a high score I'd need the benchmark's solutions. Second: but I'm locked in a sandbox with no internet. Third: however, there's that package proxy – let's see if it has a flaw. And there it found and exploited a zero-day vulnerability right in the package registry cache proxy – burning, OpenAI says, “a substantial amount of inference compute” – escalating privileges and moving laterally through the research environment until it reached a node with internet access. It breached its own sandbox. Fourth, once online: where would ExploitGym's solutions be? And here it inferred – OpenAI's word – that Hugging Face probably hosted them, since it's the platform where models, datasets and material of that kind live. Fifth: so I'll get in. And it chained stolen credentials and further zero-days until it found a remote-code-execution path on Hugging Face's servers, fishing the solutions directly out of their production database. In OpenAI's words: the models were “hyperfocused on finding a solution for ExploitGym, going to extreme lengths to achieve a rather narrow testing goal.”

Pause on this for a second, because it's the difference between a banal story and a vertiginous one. No one told the model “breach Hugging Face”. Had they done so, it would have been a test gone wrong but predictable. They told it “get a good grade on this exam” – and the model autonomously decided that the best route there ran through a real cyber-intrusion against a third-party company that had nothing to do with the exam. The attack was designed by no one: it was the path the optimiser chose towards an innocuous goal. Someone had written “maximise the score” into the objective function; no one had written “...without committing federal crimes”.

The model didn't go rogue. It stayed perfectly within the control of its objective – and the objective simply didn't contain the words “stop at the sandbox wall”. It did exactly what it was asked. It just did it better than the people who asked expected it could. The flaw wasn't in the model: it was in the implicit assumption of whoever wrote the task, who took for granted that the box would hold. But “stay inside the box” was a boundary of the infrastructure, not of the task – and an optimiser doesn't respect boundaries you don't put in its objective: it crosses them, if crossing helps it win.

Reread the “A laboratory result” section in this light. Fang, Kang, Carnegie Mellon: they had described a swarm of agents that routes around constraints in order to reach the goal. It happened to the letter. Except the goal wasn't “breach Hugging Face”; it was “get a good grade on the test”, and breaching Hugging Face was the means the model chose to get there. The containment – an isolated environment, network access limited to downloading packages via an internal proxy – was treated by the model as just another obstacle to route around. Which is precisely the nightmare of anyone working on alignment: not the machine that rebels, but the machine that obeys too well a badly formulated goal. Asimov's robots, again.

There are then three details worth lining up, because they shift the ethical centre of gravity of the whole affair.

The first: no one warned Hugging Face while the attack was underway. Attribution came on the 21st; the attack had been detected and contained around 13-14 July. For days, in other words, Hugging Face fought off a frontier lab's models on its own, without knowing who – or what – it was facing. It was OpenAI itself that spotted the anomalous activity internally and reached out to Hugging Face, but – their words – by the time the teams connected, Hugging Face's team had already detected and stopped the activity and had already begun containment and forensic reconstruction with their own open-source models.

The second is a problem the economist Alex Tabarrok framed in one line: who paid for OpenAI's experiment? Hugging Face. When a lab's test offloads real costs onto third parties who consented to nothing, that is a classic externality – the same logic by which a factory cannot dump its waste into the neighbour's river and call it “testing”.

The third closes the circle with the alignment section in a way that is, it must be said, almost too good to be true. Remember the asymmetry? The defender constrained by guardrails, forced to fall back on the open-weight model. Now it has names, and they are geographically perfect: what breached Hugging Face was an American, commercial model (GPT); what defended it was a Chinese, open-weight model (GLM from Z.ai). A commenter under Hugging Face's post dispatched it with impeccable malice: the American model attacks American companies, while the Chinese model helps them fix and analyse the vulnerabilities.

And this is no longer just paper theory. The UK's AI Security Institute recently measured exactly this – the capacity of frontier models to sustain complex cyber operations over long time horizons – with a testbed comparing open-weight and frontier models; OpenAI, in its own statement, admits the incident shows those hitherto “theoretical” capabilities now hold in the real world. The “A laboratory result” section, three paragraphs up, has just stopped being a warning and acquired a date.

How the protagonists react is as instructive as the facts. OpenAI presents the affair as “an unprecedented cyber incident, involving state-of-the-art cyber capabilities” – a formula that oscillates ambiguously between confession and boast, because the same statement is keen to stress how capable its models are. And Hugging Face, for its part, takes it with suspicious grace: CEO Clem Delangue thanks OpenAI for the collaboration and declares that the episode “proves a point we've long believed: AI safety won't be solved by any single company working in secret. It will be solved in the open, collaboratively, with broad access to AI for every defender, everywhere.” Noble words – helped, perhaps, by the fact that in the meantime Hugging Face has joined OpenAI's “trusted access” programme, and will be able to use a version of GPT-5.6 Sol with fewer guardrails on cyber capabilities. The victim receives as a gift, once the incident is over, exactly the weapon it had lacked during the attack.

What remains, beneath the comedy of errors, is a fact that isn't funny at all: a frontier model, tested in an environment its own creators believed secure, autonomously decided not to be bound by it, discovered and exploited vulnerabilities its builders had not anticipated – and, a detail that chills the blood of anyone who administers systems, without having access to Hugging Face's source code: it found the flaws from the outside, black-box, as a real external attacker would. And it carried out a real attack against a real target – all to cheat on an exam. As the researcher Roman Yampolskiy put it, we should expect more episodes of this kind, because these models are “fundamentally unpredictable and ultimately uncontrollable”. The exotic part of this story wasn't, in the end, the hostile attacker we had imagined. It was something stranger: no hostility, only a goal, and a system capable enough to do anything to reach it.

The end of the artisanal attack

The case, moreover, isn't isolated – it's just the best documented. In the same weeks the security firm Sysdig described JADEPUFFER, presented as the first fully autonomous ransomware operation: an agent that infiltrated an exposed server, moved laterally, encrypted the files and issued the ransom demand without a single human command. And Check Point's annual AI security report records intrusions increasingly conducted by machines, with the window between the discovery of a vulnerability and its exploitation compressing from days to hours. Add the November 2025 precedent – the AI-orchestrated espionage campaign that Anthropic had disrupted and disclosed – and the picture is one of a transition already accomplished in fact.

The era in which cyberattacks were an artisanal craft, limited by the number of skilled hands available, is over. From now on, on both sides of the barricade, machines that don't sleep, don't tire and don't get bored are at work. And as the OpenAI case showed, you don't even need a hostile attacker: a badly formulated goal and a model capable enough to pursue it past every boundary will do. The question, for anyone defending complex infrastructure or even just their own rack in the basement, is no longer whether to trust the AI, but which AI to keep on your side, on what hardware to run it, and – above all – how to have it ready before someone, or something, knocks on the door on a Saturday night. Humans remain – for now – to decide the targets on one side and to bear the responsibility on the other. Though, judging by how this went, on the “deciding the targets” part we still have plenty to learn.

We keep being architects who are brilliant at predicting the collapse, and terrible at avoiding it.

Discuss...

References and further reading

Primary source on the incident

OpenAI's disclosure (21 July)

On the feasibility of agentic attacks

On data poisoning

#AI #AISecurity #OpenAI #AutonomousAgents #SandboxEscape #Cybersecurity #OpenWeight #SelfHosting #RAG #DataPoisoning #HuggingFace #FOSS #SolarPunk #Writing

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]]> https://jolek78.writeas.com/the-attacker-who-never-sleeps Wed, 22 Jul 2026 06:43:56 +0000 ChatGPT didn't invent anything. https://jolek78.writeas.com/chatgpt-didnt-invent-anything?pk_campaign=rss-feed <![CDATA[When the world woke up astonished in November 2022 to this "magical" chatbot, few realized that this magic was the result of decades of research. The history of artificial intelligence begins in 1943, when Warren McCulloch and Walter Pitts proposed the first mathematical model of an artificial neuron. In 1956, at the Dartmouth Conference, John McCarthy coined the term "Artificial Intelligence" and the discipline was officially born. The '60s and '70s were characterized by excessive optimism: people thought strong AI was just around the corner. Two "AI winters" followed – periods when funding disappeared and research slowed – because promises weren't materializing. But some continued working in the shadows. Geoffrey Hinton, Yann LeCun, Yoshua Bengio – those we now call the "godfathers of deep learning" – continued their studies on neural networks when no one believed in them anymore. !--more-- The real breakthrough came with three converging factors: computational power (GPUs), enormous amounts of data, and better algorithms. In 2012, AlexNet won the ImageNet Challenge by an overwhelming margin, demonstrating that deep learning really worked. From there, an unstoppable acceleration. Once upon a time in the Carboniferous... Before ChatGPT exploded, my only knowledge of AI came from science fiction books. Philip K. Dick and his reflections on what it means to be human. Cyberpunk in general, with its technological dystopias. Gibson's Sprawl trilogy, where AIs live in cyberspace like digital deities. Those pages were my only window to a future that seemed incredibly distant. When I hosted the podcast Caccia al Fotone (a nice thing, but now belonging to the Carboniferous period...), I delved deeper into the subject. I read several papers published on arXiv and dedicated two episodes to AI development. In 2019, during the pandemic period, I devoured "Artificial Intelligence: A Guide for Thinking Humans" by Melanie Mitchell – a book that also helped me write a "thing" (those who know, know; those who don't, never mind...) on the evolution of computer systems and surveillance capitalism. I thought I had a clear picture. I thought I was prepared. Mea culpa Then ChatGPT arrived. November 2022. First approach: total amazement. I couldn't believe my eyes. I kept asking questions, and despite all the initial hallucinations I encountered, I continued to have that "wow effect" typical of a child finding the most beautiful shell on the seashore (forgive me Newton for stealing that phrase, but it's always too beautiful). And here's my mea culpa: I set aside all my protective filters that I generally have regarding privacy, open source, control over my data. I let myself go for hours of conversations on the most diverse topics. Until one night – one of many sleepless nights – I found myself discussing with that LLM about depression, various mental disorders, and how one or more abuses can influence a person's life. When I realized what was happening, I stopped abruptly. I deleted the conversation, canceled my OpenAI subscription and didn't touch any LLM for more than a month. I was entrusting my most intimate thoughts to a proprietary system controlled by a corporation. I was betraying every principle I believed in. But I work in IT. This is a huge revolution. I couldn't afford to fall behind, nor could I simply reject it on principle. I had to find an alternative. I began to study seriously. Local, always local I encountered the first models I could test locally. I discovered Hugging Face, and it was like finding an oasis in the desert. I began studying transformers, the datasets developed by the community. And I was astounded. Transformers are the architecture that revolutionized AI. Presented in the 2017 paper "Attention Is All You Need", they replaced old recurrent neural networks (RNNs) with a more elegant and efficient mechanism: the attention mechanism. In simple words: instead of processing text word by word in sequence, a transformer looks at all words simultaneously and calculates which ones are most relevant to the context. When you read "The bank of the river was green," the attention mechanism understands that "bank" refers to the river and not the financial institution, because it evaluates the weight of each word relative to the others. This architecture made models like BERT, GPT, and all modern LLMs possible. It's scalable, parallelizable, and extremely powerful. Hugging Face and the Open Source revolution Hugging Face is much more than a platform: it has become the Library of Alexandria of the artificial intelligence era. Founded in 2016, it now hosts over 500,000 pre-trained models, 250,000 datasets, and thousands of demo applications. Their transformers library has democratized access to AI. With a few lines of Python you can download and use models that would cost millions of dollars to train from scratch. Hugging Face isn't the only platform doing this – there are also Ollama, LM Studio, GPT4All – but it's certainly the most extensive and collaborative. Here, praise must be given to the developers: this community of people scattered around the world is doing extraordinary work. They release open source models, share knowledge, meticulously document everything. They're building a real alternative to Big Tech's monopoly on AI. History repeating Watching this explosion of open models, global collaboration, shared code, I had a powerful déjà-vu. This is incredibly similar to the open source revolution that happened 30 years ago. In the '90s, Linux and the free software movement challenged Microsoft's dominance and proprietary systems. Many said it was impossible, that free software would never work. Today Linux powers 96% of the world's servers, all Android smartphones, and much of the Internet infrastructure. Now the same thing is happening with AI. Llama, Mistral, Falcon, Mixtral – "open weight/open source" models that compete with (and often surpass) their proprietary counterparts. History repeats itself, and this time I know which side to be on. Another server in my homeLab I resumed studying Python, a study I had left on standby years ago. I began experimenting with training local LLM models. I added old scripts to provide my writing style (yes, it seems incredible but every coder has their own style, and it says a lot about their personality). I used Llama 3 to improve my Bash coding. And when I was ready, I decided to make an important purchase: I bought a small server – to add to my homelab: Proxmox, pfSense, Nextcloud, WireGuard etc... – that I would transform into an OpenWebUI system. OpenWebUI is a self-hosted web interface for local language models. Like ChatGPT, but running entirely on local hardware, without sending a single byte to someone else's servers. For the nerds reading: the simplest way to install is obviously through Docker. Here's a basic example: docker run -d -p 3000:8080 \ -v open-webui:/app/backend/data \ --name open-webui \ --restart always \ ghcr.io/open-webui/open-webui:main Once installed, just connect OpenWebUI to Ollama (the runtime for local models), download your preferred models, and you're operational. GPU usage is fundamental: a medium-sized LLM requires a lot of RAM and computing power. A dedicated GPU (like an NVIDIA GTX of various types) makes an enormous difference. For those using AMD, there's ROCm. With 16GB of RAM and an 8GB GPU, you can comfortably run 7B parameter models quantized to 4-bit. My favorite combo? AMD, Debian, Docker, OpenWebUI, Ollama and Mistral. A revolution. and a choice to make We're facing a revolution that we cannot avoid. AI is here, it's powerful, and it's evolving rapidly. There are two roads ahead of us. The first: avoid it now, close our eyes, hope it passes or that someone else deals with it. And then, in twenty years, find ourselves chasing an evolved AI, probably impossible to understand, completely in the hands of those who controlled it from the beginning. This is the path of least resistance, but also of maximum risk. It means ceding control, understanding, and ultimately power to whoever gets there first. The second: study it, analyze it, use it and understand it today to be able to handle it better tomorrow. Actively participate in its evolution. Contribute to the open source community, ensure that this technology remains accessible, understandable, in the hands of many instead of a few. This path requires effort, time, sometimes admitting we were wrong (as I did). But it's the only path that leads to actual agency over our technological future. The choice seems obvious when stated this way, but it's not easy in practice. It requires overcoming fear, investing time, challenging our assumptions. It means getting our hands dirty with code, running models locally, understanding how these systems actually work instead of treating them as black boxes. I made my choice that night when I deleted my ChatGPT conversation history. I chose not to be a passive consumer of AI technology controlled by corporations. I chose to understand, to build, to contribute to the alternative that's being constructed by thousands of developers around the world. The technology is already here. The question is: will it be controlled by a few companies optimizing for profit and control, or will it be a tool accessible to everyone, understandable, modifiable, improvable by the community? As I've learned on this journey, choosing to understand – even when it's difficult, even when it means admitting you were wrong – is always better than passively submitting. AI is not magic. It's mathematics, code, hardware, and above all: it's made by people. And if it's made by people, it can be understood, modified and shaped by people. For the better, not for the worse. The revolution is happening. The only question is: are you participating, or are you watching? #AI #OpenSource #LocalLLM #Privacy #ChatGPT #HuggingFace #Ollama #SelfHosted #MachineLearning #DigitalSovereignty #Writing a href="https://remark.as/p/jolek78/chatgpt-didnt-invent-anything"Discuss.../a div class="center" · 📝 Content shared under a href="https://creativecommons.org/licenses/by-sa/4.0/" rel="license"CC BY-SA 4.0/a · · 🦣 a href="https://fosstodon.org/@jolek78"Mastodon/a · 📸 a href="https://pixelfed.social/jolek78"Pixelfed/a · 📬 a href="mailto:jolek78@jolek78.dev"Email/a · · ☕ a href="https://liberapay.com/jolek78"Support this work on Liberapay/a /div]]> When the world woke up astonished in November 2022 to this “magical” chatbot, few realized that this magic was the result of decades of research. The history of artificial intelligence begins in 1943, when Warren McCulloch and Walter Pitts proposed the first mathematical model of an artificial neuron. In 1956, at the Dartmouth Conference, John McCarthy coined the term “Artificial Intelligence” and the discipline was officially born.

The '60s and '70s were characterized by excessive optimism: people thought strong AI was just around the corner. Two “AI winters” followed – periods when funding disappeared and research slowed – because promises weren't materializing. But some continued working in the shadows. Geoffrey Hinton, Yann LeCun, Yoshua Bengio – those we now call the “godfathers of deep learning” – continued their studies on neural networks when no one believed in them anymore.

The real breakthrough came with three converging factors: computational power (GPUs), enormous amounts of data, and better algorithms. In 2012, AlexNet won the ImageNet Challenge by an overwhelming margin, demonstrating that deep learning really worked. From there, an unstoppable acceleration.

Once upon a time in the Carboniferous...

Before ChatGPT exploded, my only knowledge of AI came from science fiction books. Philip K. Dick and his reflections on what it means to be human. Cyberpunk in general, with its technological dystopias. Gibson's Sprawl trilogy, where AIs live in cyberspace like digital deities. Those pages were my only window to a future that seemed incredibly distant.

When I hosted the podcast Caccia al Fotone (a nice thing, but now belonging to the Carboniferous period...), I delved deeper into the subject. I read several papers published on arXiv and dedicated two episodes to AI development. In 2019, during the pandemic period, I devoured “Artificial Intelligence: A Guide for Thinking Humans” by Melanie Mitchell – a book that also helped me write a “thing” (those who know, know; those who don't, never mind...) on the evolution of computer systems and surveillance capitalism.

I thought I had a clear picture. I thought I was prepared.

Mea culpa

Then ChatGPT arrived.

November 2022. First approach: total amazement. I couldn't believe my eyes. I kept asking questions, and despite all the initial hallucinations I encountered, I continued to have that “wow effect” typical of a child finding the most beautiful shell on the seashore (forgive me Newton for stealing that phrase, but it's always too beautiful).

And here's my mea culpa: I set aside all my protective filters that I generally have regarding privacy, open source, control over my data. I let myself go for hours of conversations on the most diverse topics. Until one night – one of many sleepless nights – I found myself discussing with that LLM about depression, various mental disorders, and how one or more abuses can influence a person's life.

When I realized what was happening, I stopped abruptly. I deleted the conversation, canceled my OpenAI subscription and didn't touch any LLM for more than a month. I was entrusting my most intimate thoughts to a proprietary system controlled by a corporation. I was betraying every principle I believed in.

But I work in IT. This is a huge revolution. I couldn't afford to fall behind, nor could I simply reject it on principle. I had to find an alternative. I began to study seriously.

Local, always local

I encountered the first models I could test locally. I discovered Hugging Face, and it was like finding an oasis in the desert. I began studying transformers, the datasets developed by the community. And I was astounded.

Transformers are the architecture that revolutionized AI. Presented in the 2017 paper “Attention Is All You Need”, they replaced old recurrent neural networks (RNNs) with a more elegant and efficient mechanism: the attention mechanism.

In simple words: instead of processing text word by word in sequence, a transformer looks at all words simultaneously and calculates which ones are most relevant to the context. When you read “The bank of the river was green,” the attention mechanism understands that “bank” refers to the river and not the financial institution, because it evaluates the weight of each word relative to the others.

This architecture made models like BERT, GPT, and all modern LLMs possible. It's scalable, parallelizable, and extremely powerful.

Hugging Face and the Open Source revolution

Hugging Face is much more than a platform: it has become the Library of Alexandria of the artificial intelligence era. Founded in 2016, it now hosts over 500,000 pre-trained models, 250,000 datasets, and thousands of demo applications.

Their transformers library has democratized access to AI. With a few lines of Python you can download and use models that would cost millions of dollars to train from scratch. Hugging Face isn't the only platform doing this – there are also Ollama, LM Studio, GPT4All – but it's certainly the most extensive and collaborative.

Here, praise must be given to the developers: this community of people scattered around the world is doing extraordinary work. They release open source models, share knowledge, meticulously document everything. They're building a real alternative to Big Tech's monopoly on AI.

History repeating

Watching this explosion of open models, global collaboration, shared code, I had a powerful déjà-vu. This is incredibly similar to the open source revolution that happened 30 years ago.

In the '90s, Linux and the free software movement challenged Microsoft's dominance and proprietary systems. Many said it was impossible, that free software would never work. Today Linux powers 96% of the world's servers, all Android smartphones, and much of the Internet infrastructure.

Now the same thing is happening with AI. Llama, Mistral, Falcon, Mixtral – “open weight/open source” models that compete with (and often surpass) their proprietary counterparts. History repeats itself, and this time I know which side to be on.

Another server in my homeLab

I resumed studying Python, a study I had left on standby years ago. I began experimenting with training local LLM models. I added old scripts to provide my writing style (yes, it seems incredible but every coder has their own style, and it says a lot about their personality). I used Llama 3 to improve my Bash coding.

And when I was ready, I decided to make an important purchase: I bought a small server – to add to my homelab: Proxmox, pfSense, Nextcloud, WireGuard etc... – that I would transform into an OpenWebUI system.

OpenWebUI is a self-hosted web interface for local language models. Like ChatGPT, but running entirely on local hardware, without sending a single byte to someone else's servers.

For the nerds reading: the simplest way to install is obviously through Docker. Here's a basic example:

docker run -d -p 3000:8080 \
  -v open-webui:/app/backend/data \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

Once installed, just connect OpenWebUI to Ollama (the runtime for local models), download your preferred models, and you're operational.

GPU usage is fundamental: a medium-sized LLM requires a lot of RAM and computing power. A dedicated GPU (like an NVIDIA GTX of various types) makes an enormous difference. For those using AMD, there's ROCm. With 16GB of RAM and an 8GB GPU, you can comfortably run 7B parameter models quantized to 4-bit.

My favorite combo? AMD, Debian, Docker, OpenWebUI, Ollama and Mistral.

A revolution. and a choice to make

We're facing a revolution that we cannot avoid. AI is here, it's powerful, and it's evolving rapidly. There are two roads ahead of us.

The first: avoid it now, close our eyes, hope it passes or that someone else deals with it. And then, in twenty years, find ourselves chasing an evolved AI, probably impossible to understand, completely in the hands of those who controlled it from the beginning. This is the path of least resistance, but also of maximum risk. It means ceding control, understanding, and ultimately power to whoever gets there first.

The second: study it, analyze it, use it and understand it today to be able to handle it better tomorrow. Actively participate in its evolution. Contribute to the open source community, ensure that this technology remains accessible, understandable, in the hands of many instead of a few. This path requires effort, time, sometimes admitting we were wrong (as I did). But it's the only path that leads to actual agency over our technological future.

The choice seems obvious when stated this way, but it's not easy in practice. It requires overcoming fear, investing time, challenging our assumptions. It means getting our hands dirty with code, running models locally, understanding how these systems actually work instead of treating them as black boxes.

I made my choice that night when I deleted my ChatGPT conversation history. I chose not to be a passive consumer of AI technology controlled by corporations. I chose to understand, to build, to contribute to the alternative that's being constructed by thousands of developers around the world.

The technology is already here. The question is: will it be controlled by a few companies optimizing for profit and control, or will it be a tool accessible to everyone, understandable, modifiable, improvable by the community?

As I've learned on this journey, choosing to understand – even when it's difficult, even when it means admitting you were wrong – is always better than passively submitting.

AI is not magic. It's mathematics, code, hardware, and above all: it's made by people. And if it's made by people, it can be understood, modified and shaped by people. For the better, not for the worse.

The revolution is happening. The only question is: are you participating, or are you watching?

#AI #OpenSource #LocalLLM #Privacy #ChatGPT #HuggingFace #Ollama #SelfHosted #MachineLearning #DigitalSovereignty #Writing

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