Anti-AI Activists Deliberately Poison Training Data to Degrade ChatGPT and Gemini

A loosely organized "poison AI" movement is intentionally corrupting publicly available datasets that large language models train on, aiming to degrade the output quality of models like ChatGPT and Gemini. The methods range from flooding web pages with subtly misleading text to using tools like Nightshade, which embeds adversarial perturbations into images to confuse vision models.

The catch: poisoned data doesn't stay neatly targeted. Researchers warn the same corrupted datasets get scraped by academic, open-source, and commercial projects alike, meaning models built for medical research or accessibility tools absorb the same contamination aimed at Big Tech. The activists want to make billion-dollar AI systems useless; the collateral damage may land on smaller, less controversial ones instead.