this post was submitted on 15 Dec 2025
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I made this point recently in a much more verbose form, but I want to reflect it briefly here, if you combine the vulnerability this article is talking about with the fact that large AI companies are most certainly stealing all the data they can and ignoring our demands to not do so the result is clear we have the opportunity to decisively poison future LLMs created by companies that refuse to follow the law or common decency with regards to privacy and ownership over the things we create with our own hands.
Whether we are talking about social media, personal websites... whatever if what you are creating is connected to the internet AI companies will steal it, so take advantage of that and add a little poison in as a thank you for stealing your labor :)
There are poisoning scripts for images, where some random pixels have totally nonsensical / erratic colors, which we won't really notice at all, however this would wreck the LLM into shambles.
However i don't know how to poison a text well which would significantly ruin the original article for human readers.
Ngl poisoning art should be widely advertised imo towards independent artists.
An issue I see with a lot of scripts which attempt to automate the generation of garbage is that it would be easy to identify and block. Whereas if the poison looks similar to real content, it is much harder to detect.
It might also be possible to generate adversarial text which causes problems for models when used in a training dataset. It could be possible to convert a given text by changing the order of words and the choice of words in such a way that a human doesn't notice, but it causes problems for the llm. This could be related to the problem where llms sometimes just generate garbage in a loop.
Frontier models don't appear to generate garbage in a loop anymore (i haven't noticed it lately), but I don't know how they fix it. It could still be a problem, but they might have a way to detect it and start over with a new seed or give the context a kick. In this case, poisoning actually just increases the cost of inference.
This sounds good, however the first step should be a 100% working solution without any false positives, because that would mean the reader would wipe their whole system down in this example.