Why in the world would that specific 3x3 matrix be a trigger for an attack? Are they trying to find someone doing some particular kind of numerical analysis?
Presumably it's so it can be used as a subdependency for setting up an attack in a popular, legitimate package, e.g. via a pull request. The code in the legitimate package would not arouse suspicion at all.
>Are there any notable classes of programs/problems where you'd do an LU decomposition of this specific matrix?
It doesn't have to be this specific matrix. If it's some service exposed to the internet, it can be user supplied, in which case it turns into a backdoor.
Perhaps they just need a way to sneakily activate it? Or perhaps they have a target application which they know uses that. This method suggests a supply chain attack where a valid contributor to a library 'accidentally' includes this package and the hack carries out before anyone notices.
My guess is that it's crypto related but of course it could be anything.
I would assume it’s actually so they can allow it to spread before it gets activated. Then do something that affects the entire chain of package dependencies
This matrix is not a condition, it’s a key. JSON.stringify with it’s data goes to the scrypt as a password and that creates an AES-256-GCM key. There is no if, every other input won’t decrypt. That’s why no one will get payload from the package without knowing the exact input.
There are "humanizer" scripts that these people like to use. This one is not the typical style I see in my online moderator adventures, which is usually "lowercase everything and remove all punctuation except periods", as if we are repelled by em-dash use and not the tortured prose.
I like the removal of a random definite article, to simulate an ESL writer and garner sympathy for using LLMs as a "tool to clean up posts". Heavens, they would never even think of using their Claude subscription to be a slop factory, that would cross a line.
A status code for (industrial/the-man) equipment? You could target specific environments by activating on obscure error codes that can be remotely triggered.
Aka If someone from the outside can make your equipment emit X internally, they can target X in some way.
Zero-knowledge seems completely unnecessary. Surely the original image does not have any material content in it that is not made public by publishing a JPEG-compressed version.
Could this be simpler and more efficient without ZK?
It would be interesting if it could be made to work with arbitrary transform, such as redaction.
Imagine a photo with some blacked out rectangles, and a ZK proof that confirms it comes from an attested "real" photo + adding those rectangles, but no other modification.
Generally being succinct goes hand and hand with being zero knowledge.
The proof must be MUCH smaller than the whole computation or even the inputs (otherwise, just provide the uncompressed image!).
And the proof has to resist forgery.
So going for succinctness and at least computational soundness gets you to zero knowledge 'for free'.
Plus as the dead sibling comment notes: You may want disclosed modifications like a crop or redaction where the committed information remains private but you want to keep the proof.
Regardless of whether the target result(s) are ultimately correct, isn't it almost guaranteed that supporting infrastructure for surreals-in-lean is a real contribution? Is it a goal to make those polished/reusable, or more like throw-away harness, and just a stepping stone to the proof?
I'm a little tired from the project so not eager to jump back into it right away. But yes, I'd love for useful pieces to make their way into https://github.com/vihdzp/combinatorial-games. Violeta, who maintains CG, expressed interest in ultimately integrating the proof in some shape into the repo, but I think more work needs to be done to understand what makes it work.
You will find they were inspired by the NP-hard knapsack problem, and inspired a bunch of later research that led to RSA.
I think the tapestry of history would suggest the answer to the question "is math responsible for this invention" a lot more complicated than it appears. For lasers, Einstein proposed the idea based on purely theoretical physics, and it was made possible in 1960. Is that "theoretical math leading to the invention of lasers"? Surely he was at least relying on a lot of additional theoretical work for that. On the other hand, much theoretical that came out of Bell Labs were responses to needs for better vacuum tube technology, better amplifiers, etc., which were a deep collaboration between theory, practice, and tradesman with a strong intuition for how to build with various materials and at varying scales.
Python has always had a bytecode compiler that did some minor optimizations, since its inception in the 90's. The issue is that optimizations are very hard to do correctly in the compiler because Python is so dynamic. Any piece of code could suddenly redefine mytype.__add__() and so forth.
And for the record, I only buy brands that sell high-quality products. Marketing and operations I can fix, but if the stuff they sell is no good, there's no point.
Of course I do. No one buying a business would do it without that information. I get their P&L before I even get on a call with them, and during the due diligence period I get access to Shopify, Amazon, etc. to get first-party data to validate their claims.
How do you inspect the quality of the products you're selling? From your blog it appears you just read through their sales information, and don't ever see or handle the product. And yet we all know Amazon sellers juice their sales metrics and ratings with scammy practices, and now I see selling their brand is part of the incentive.
And more, you are AI-generating listings and images for your acquired brands. You're filling Amazon with slop, and automating the process.
You should seriously reflect on your role in polluting these marketplaces.
> How do you inspect the quality of the products you're selling?
I buy them off of Amazon. But that doesn't really matter that much compared to the history of reviews.
> And yet we all know Amazon sellers juice their sales metrics and ratings with scammy practices, and now I see selling their brand is part of the incentive.
You just read that online and have the most superficial understanding possible. I know what I'm doing, and it's trivial to avoid this kind of thing.
> And more, you are AI-generating listings and images for your acquired brands. You're filling Amazon with slop, and automating the process.
Bad criticism. There's nothing wrong with an AI-generated listing if the content accurately represents the product. I have purchased brands that had horrendous images because the seller tried to Photoshop them but wasn't good at it. Replaced those with high-quality AI images, and everyone wins - the customer better understands what the product is, and I get more sales. It's only slop if you just think everything AI-generated is slop regardless of what it is or how it's used, and if so that's too intellectually lazy of a position for me to try to disabuse you of.
> You should seriously reflect on your role in polluting these marketplaces.
Help me out with that. What's my role? I buy brands that sell high-quality products, increase the quality of the content and marketing, and make money selling them. The sellers of these brands are frequently people who started them on the side but don't have the time to continue, and I get them a fair price in exchange for their businesses.
I suspect you mean this as an insult, but it's not too far off. That was one of the main points of the business - each one is simple to run, so I can manage a lot of them. And that was before AI got really good.
Anyway, I'm sure the app you makes that lets you get a phone number that can send image messages is very complex, and I think that's very nice.
Reminds me of the old parable: never argue with a man whose job depends on not being convinced.
If the US gov't passed laws and enforced them strongly, this problem could be solved the same way the gov't solves it: air-gapping the networks on which they do this work.
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