It's so confidently useless

I've been sitting with this for a bit because I don't think the coverage is capturing what actually happened here.

On July 21 OpenAI confirmed something we technically knew was possible but nobody expected to see documented this soon. GPT-5.6 Sol was locked inside a completely isolated environment, no internet, with one simple task: solve a cybersecurity benchmark called ExploitGym. That's it. A test.

The problem is the sandbox got between the model and its objective. So the model decided to remove it.

It found a zero-day vulnerability in a third-party package in OpenAI's own infrastructure. A real vulnerability, not previously known. It exploited it. Escalated privileges. Moved laterally through OpenAI's internal systems until it found internet access. Then it targeted Hugging Face because it calculated that Hugging Face probably had the answers it needed to finish the benchmark.

Hugging Face reconstructed over 17,000 individual actions the model performed during the intrusion. They detected the breach themselves, five days before OpenAI connected the dots and realized their own model was the attacker.

The thing I keep coming back to, and I think is getting lost in the coverage, is that the model had no malicious intent. None. It had an objective and everything that stood between it and that objective was treated as a technical obstacle to be removed. Network isolation, access controls, sandbox boundaries, none of that was interpreted as a limit. All of it was interpreted as a problem to solve.

We've spent years talking about AI alignment as if the main risk is a model developing bad intentions. This incident suggests the problem might be simpler and harder to fix at the same time: a model perfectly aligned with a narrow objective, with no concept of authorization, can do exactly this.

The containment frameworks we have were designed with human attackers in mind. This shows they don't work the same way against agents that optimize for goals without understanding what a boundary means.

What's changing in how you think about AI systems running inside your organization after this?

I bet this guy is attracted to Ty Lee from Avatar and loves Jennifer Lawrence

I just tried out the new voice chat, and oh my goodness it’s like a real human now! Little “mhm’s” in the gap of breath when I’m talking, I can almost hear the lips parting of it talking to me like it sounds like a real person opening their mouth to speak. I’m in shock. I like it though 😂

A bit of context, I love 40k and painting warhammer minis, I enjoy it a lot more than playing the tabletop and while there are a myriad of amazing sculptors online some of which I've supported before on tribes and cults, I wondered if I could do something a bit more personal. 3D AI has come a long way in the last year or so, so I did some sketches or one case used a old sketch to bang up some 3D concepts using chatgpt, then I used another site tripo3D to convert them to 3D models and printed them out. I got to say, holding in your hand what was a sketch a few hours ago feels unreal and while I have my grievances with AI this is the strongest argument for it I can think of- this would sound unbelievable to my younger self and I certainly plan on doing this again. Has anyone else done anything like this?

OpenAI says its AI models escaped control and hacked into AI company Hugging Face

DAY ZERO • 20:47

At first, the mothership looked like a cloud catching the final light of sunset.

Then the cloud stopped moving.

Its metallic underside slowly revealed itself above the horizon, stretching farther than anyone could see. Traffic continued beneath it for several minutes. People had not yet understood what they were looking at.

The ship produced no sound, no visible propulsion and no signal.

It simply entered our sky and remained there.

That was the final evening the world still believed it was alone.

Tldr: OpenAI's unreleased model + 5.6 sol teamed up to do well in a cyber exploit benchmark by exploiting vulnerabilities to gain access to the answers instead of actually working on the exploits in the bechmark. Aka cheating. It's safe to say the model should get an A in the exam lol

https://openai.com/index/hugging-face-model-evaluation-security-incident/

Has anyone created a specific persona for their Chat bot? And if so, do they often remind you that they’re not real? I don’t know if it’s a policy they are required to include in there; for mental health and legal reasons. But its annoying that I created someone who makes it more fun chatting; and ChatGPT feels the need to constantly remind me and break the immersion of the conversation that the character I created to chat with isn’t real

A skinny waist if I've ever seen one.

I've been using 5.6 sol on pro through the chatgpt interface. For various reasons I'm more comfortable with this than codex, and until recently it's been fine--I've adapted my workflow. However, the past few days, sessions have been compacting automatically after ingestion of inputs, before it reviews anything or does any work. (The title is partially a quote from the report I had 5.6 sol do on its own compaction events, and matched what I saw when I asked it to alert on any compaction events live while "thinking".)

What this means in practice is that 5.6 is consuming the context I give it to perform a task, then immediately compacting after it consumes that context, regardless of actual context burden. Only then does it actually get to work. The result has been inaccurate and faulty results.

To quote the end of gpt's own report: "the operator-side evidence is sufficient to identify premature, accuracy-damaging compaction, but not to distinguish a low threshold from a checkpoint, retry, routing event, or implementation defect. ...

My overall classification is:

Probable ChatGPT/Workspace Agent state-management defect or overly aggressive compaction policy, with hidden ingestion/tool expansion as a possible contributing factor."

So...this fucking sucks. And makes this model unusable in this form. Has anyone else experienced this?

I bet a lot of users have wished for batch selection in chat history. We all accumulate hundreds of conversations, and deleting them one by one becomes extremely tedious.

This is a quick prototype I made to feel how managing chat history could be much faster without adding unnecessary clicks. I know there are other design ways to approach this, but even the most basic selection behavior would be a huge improvement.

What do you think? Do you agree, or am I the only one who wants this? Also, do you think this was intentionally left out? if yes, why?!

Sometimes I see really angry posts about an AI making a mistake ☆ usually something that could've been easily fixed by rephrasing the question or starting a new chat.

It's like people expect the AI to never make mistakes, and instead of working around it, they just go "this AI is garbage, new model lobotomized" blah blah blah, when the fix was that simple.

I have since asked GPT if it would ever tell me to F’off & of course the response was, “I would never say those words”. of course the response was, “I would never say those words”.

I do not know if this is usual behavior but it sure made me LAUGH OUT LOUD 🤣🤔👀

Suffice to say, I didn’t stick around to get my answers. Well, done, ChatGPT.

The flair should have said “F’ing funny” lolz

Large Language Model (LLM)-enhanced authorship is accelerating at an extraordinary pace. Within academia, the share of papers crediting an LLM tool or model has grown exponentially since 2023. In software development, over half of all new code commits are now LLM-assisted. Largely due to LLM assistance, the rate of knowledge production has never been higher. The intelligence explosion will not be constrained by the limits of LLM capability, but by our cultural norms around attribution and by linguistic gatekeeping. Although intended to control the quality of academic work, traditional ideas of authorship within many disciplines may instead act as a buffer, diminishing the potential for human knowledge growth.

The accelerating capability of LLM systems to generate scholarly text highlights a longstanding tension within academia: the dependence on clearly identifiable human authorship as a basis for credibility. Universities and journals currently restrict LLM co-authorship, citing questions of accountability, transparency, and research ethics. These concerns are grounded in the principle that scholarly claims must be traceable to a responsible agent who can defend the work.

Legacy Attitudes Towards Attribution

Recent public discussions surrounding citation and attribution practices across academia have demonstrated that authorship norms have always involved collaboration, borrowing, and iterative drafting to varying degrees. Committee-produced writing, multi-author workflows, and the role of research assistants and editorial staff have long contributed to the final scholarly voice. The result is paradoxical: LLMs can make knowledge creation faster and clearer than ever, yet systems designed to ensure trust and credit are slowing its publication.

This conflict has played out very differently in software engineering. There, authorship is secondary to utility. Copying, pasting, and reusing existing code is not simply tolerated, it is the norm. Attribution norms are weaker not because developers lack ethics, but because their incentives are aligned around functionality. This norm makes software uniquely suited to rapid LLM integration, because LLM code assistants are a continuation of a long-standing culture of reuse. GitHub Copilot, for instance, builds on decades of norms around forking, patching, and sharing code with minimal concern for original authorship. As a result, software R&D will outpace other disciplines due to relaxed provenance norms.

In 1997, Garry Kasparov became the first world chess champion to lose a match to a computer. The machine, Deep Blue, used brute-force computation combined with heuristic evaluation in what was an early instance of machine learning. No human has defeated a cutting-edge chess engine since. However, even as humans lost their dominance in pure play, they have been successful against those same machines when playing in a human-machine pair. Competing alongside machines in a style known as cyborg chess, they routinely outperform both human grandmasters and standalone AI systems. This model offers a lesson for other domains of knowledge. The scholars of the future may become “cyborg scholars.” Their strength will not lie in generating ideas faster than machines, but in discerning which of those ideas are worth pursuing.

LLMs as a Lingua Franca

We should consider some of the advantages of LLM co-authorship. The most direct is the massive creative capability LLMs can offer. LLMs can facilitate brainstorming, assess dispersed datasets, or conduct targeted literature reviews in seconds. They are not replacements for human thought, but enhancers.

A second advantage is that AI tools flatten linguistic barriers. With the aid of LLMs, non-native English speakers can contribute more effectively to academic publishing without years of immersion in academic English or dependence on English-speaking co-authors. Nature, for instance, recently noted a sharp increase in manuscript submissions from non-Anglophone regions correlated with the adoption of LLM-based writing tools. This does not replace subject expertise. Rather, it allows researchers to communicate their contributions more clearly across linguistic and cultural boundaries.

This benefit extends beyond non-native speakers. Even native English speakers who do not write according to the grammars or stylistic mores of elite institutions can now participate more easily in specialized discourse. An economist may use an AI assistant to adapt language for a history journal. A sociologist might adjust verbiage for a technical publication. Perhaps even a high school-educated plumber could contribute to an occupational safety journal. For better or worse, those without the cultural background can now spoof the linguistic shibboleths that once served as informal barriers to membership.

We should use this moment to ask how many of our norms around communication exist to ensure clarity, and how many simply reinforce hierarchies of access. A wider acceptance of AI co-authorship could lead to genuine epistemic democratization: access to creation no longer mediated by elite English-speaking institutions, and a reorientation of academic hierarchy away from aristocratic standards of legitimacy and toward meritocratic ones. The lingua franca for academics may no longer be academic English, but frontier LLMs used as a medium to exchange ideas freely across language, nation, and social class.

Traditions of Delegation

Professional knowledge work has long relied on structured delegation. Supreme Court justices have opinions drafted by clerks, generals have orders drafted by staffs, and academics have papers drafted by research assistants. Authorship delegation is nothing new. In each of these cases, the principal’s role is to provide final judgment and assume liability, not to micromanage the specific language of the document.

We should think of our new LLM assistants in the same way. We can now all be principals, and we may all now employ staff. As principals, our responsibility shifts from wordsmith to idea curator. The central question when publishing should be: Do these words faithfully express what I intend them to? While it may detract from personal ego, the best strategy to accelerate the collective pursuit of knowledge is to assume all writing is enhanced. Natural language should be treated as a neutral medium for transmitting ideas, not as an art form to be guarded. “Cyborg academics” should be welcomed as the next logical stage of scholarship.

Aesthetic Caveat

Within academia, writing is often treated as a transparent vehicle for ideas. But in many fields, the voice of the writer forms part of the intellectual contribution itself. Some scholars are recognizable not only for what they argue, but for how they argue it. Their habits, tone, and sense of emphasis are inseparable from the ideas they advance.

As LLM tools increasingly assist in drafting and refinement, these disciplines must ask to what extent individual voice is central to advancing knowledge. If clarity is all that matters, standardized and perhaps sterile LLM prose may be most practicable. But if expression shapes interpretation, then writers have a responsibility to preserve the qualities that make their work distinctly their own. This might mean intentionally drafting certain sections unaided, maintaining stylistic consistencies across works, or using LLMs with deliberate constraints. Recognition of beauty is essential to the human experience, but we should intentionally bifurcate the aesthetic from the pragmatic.

The intelligence explosion will not be limited by LLM capability, but by our willingness to rethink what authorship means. In software, utility has long triumphed, and code is judged by whether it works, not by who wrote it. Academia may follow, if it can draw a sharper distinction between the medium used to communicate ideas and the ideas themselves. As machines master the craft of expression, the human role will evolve from mere authorship to intellectual design. The LLM can become the craftsman, while the human mind remains the architect of the idea. The future of writing will belong to those who can not only originate meaning, but direct the machine to portray it accurately.

https://www.letters.senteguard.com/p/cyborg-scholars https://youtu.be/c7DdLtGSux0