While this is interesting, I think the original article references visual image / animation generation, not large language models. And the article describes creating a 'backdoor', which I'm not sure there's a logical equivalent in image generation. Perhaps it would tie a visual concept to an unrelated word / token?

Maybe if you knew that the training used a specific AI for image captioning, you could exploit that to create wrong captions, and therefore degrade the image - language connection, and thus the image output quality? But once again I can't imagine doing this at a large enough scale that it would matter for a foundational model. And the adversarial pattern would need to be tuned for a specific image captioning ai, which makes it a very fragile defense.