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this post was submitted on 03 Sep 2026
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How does that work? Are there hashes of child sex abuse images somehow reproduced by LLM? Surely the new images don't have the same hashes. Guardian is not really being clear here.
edit: Ars' is less badly written: "This is the first case to accuse xAI of training on CSAM, and the complaint does not go into great detail on that claim. Previously, Ars reported on a controversial dataset that was later scrubbed after researchers found CSAM in the training data, but there’s no indication xAI trained on that data. In a press release from lawyers representing Doe, it explained that Doe’s images were included in a CSAM Hash List maintained by NCMEC, and “that same material” allegedly “was part of the dataset xAI used to build Grok’s image and video generating capabilities.” The complaint similarly only alleged that “CSAM depicting Plaintiff with its longstanding well-known hash values has been used as a part of the dataset used by xAI.”"
From my understanding a hash completely changes when a single part of it is changed. That's the point of a hash. Sounds like they identified the hashed images from the training set.
Nope. They use perceptual hashing. That way water marking and cropped images still trigger against the casm scanners.
The known casm results in a specific hash. Modified versions of that will result in a different hash but such that the "distance" is meaningful. The distance is how many bits need to flip to match the known casm hash.
It can lead to false positives so that's why manually checking occurs. It's also why people got very upset that Apple and perhaps others were going to automatically do these checks during upload to the cloud. When performed against billions of images, the false positive rate becomes huge and manually checking becomes too much overhead so people would be falsely accused.
It's very likely what they found in groks models is a false positive but in this case they can probably compel twitter to prove it's a false positive.
there are other, more robust, ways of fingerprinting images than hashing the data. I'm not sure how it works exactly though, but I can imagine it is not completely different from how shazam can pick out a song in a noisy environment.
you would think, but then it would be lost when any part of the image changed (like adding a watermark or jpeg compression) instead it is a reversable algorithm https://www.researchgate.net/figure/mage-reconstructions-from-PhotoDNA-hashes-4_fig4_374838370