The 'poison AI' movement wants to corrupt ChatGPT and Gemini to make them useless but it comes with a huge risk of collateral damage

Date:
Thu, 06 Aug 2026 16:00:00 +0000

Description:
The movement to sabotage AI through poisoned training data may be aimed at major tech companies, but its damage could spread to ordinary people and smaller organizations.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter AI has acquired an unusual new enemy. A growing AI data poisoning online movement wants to attack the models themselves. The goal is simple enough on paper feed future AI systems bad information, misleading data, or deliberately corrupted material until they become less useful.

If future versions of ChatGPT, Gemini and other AI models learn from enough misleading, corrupted or intentionally manipulated material, perhaps those systems will become less reliable. Chatbots already confidently repeat nonsense far too often; now imagine it exponentially worse as text and image generators misunderstand every prompt, and the models become too frustrating to trust. It's not just theory. Data poisoning is an actual area of AI security research. While the argument that making AI systems less reliable will discourage companies from scraping creative work or building ever larger models might entice some, it also risks undermining far more than just the latest trending AI chatbot . Latest Videos From TechRadar Watch full video here: Trying to teach AI all the wrong lessons Large language models are
often described as reading the internet. They absorb enormous collections of books, websites, articles, computer code, images, and documents and learn patterns from them.

Changing enough of that raw material can sometimes change what the finished model learns. Instead of attacking an AI after it has been built, the
attacker tries to 'poison' the well of knowledge. You may like Meta AI's recent hack is a wake-up call for anyone who puts their trust in AI systems Top AI tools such as OpenClaw and Github Copilot can be hijacked to create
new massive botnets Microsoft warns AI chatbots may be sending victims to malicious websites

A poisoned model might answer one specific question incorrectly while appearing completely normal the rest of the time. Images might look normal to humans, but contain invisible text designed to confound AI. Other attacks attempt to hide backdoor codes to secret behaviors that remain invisible
until a particular trigger phrase appears. The point is precision rather than chaos.

There's a whole philosophy and nascent movement encouraging the practice.
Some want to flood the internet with misleading AI-generated content. Others discuss uploading deliberately corrupted information in the hope that tomorrow's models will eventually absorb it. Artists have embraced tools like Nightshade that subtly alter their images before posting them online, making them harder for AI systems to learn from while leaving them almost identical to the human eye. Get daily insight, inspiration and deals in your inbox Sign up for breaking news, reviews, opinion, top tech deals, and more. Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. Polluting the well rarely hurts only one person But AI poisoning is a lot harder than slipping a little salt into someone's coffee. AI companies
filter, clean, and review datasets long before they become part of a model. Poisoning a commercial system is considerably harder than just a misleading Wikipedia paragraph.

That doesn't mean it can't be dangerous. Cybersecurity researchers don't
worry about ChatGPT getting a history fact wrong. The real worry is that poisoned information will mess with the behind-the-scenes AI systems used by hospitals, banks, or government agencies. Those models often rely on much narrower datasets and fewer security checks, making them more attractive targets.

A medical assistant that gives excellent advice except for one particular condition or banking software that always includes a hidden security flaw
with every update. That's what poisoned data might do if it isn't caught in time. The techniques are not inherently wrong, but they can be abused like
any other technology.

None of this means the frustration behind those dreaming of slipping
erroneous facts into ChatGPT or Gemini is misplaced. Artists and authors are continuing to fight over AI training data use and misuse. But while the intended target may deserve criticism, poisoning AI data is unlikely to be a long-term solution.

AI already struggles with misinformation, hallucinations, and factual mistakes. Deliberately adding more bad information into the ecosystem risks amplifying exactly the problems critics already complain about. Protecting people, their livelihoods, and creative ownership is essential, but making AI worse will not somehow make the future better. Follow TechRadar on Google
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Link to news story: https://www.techradar.com/ai-platforms-assistants/the-poison-ai-movement-wants -to-corrupt-chatgpt-and-gemini-to-make-them-useless-but-it-comes-with-a-huge-r isk-of-collateral-damage


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