The secret of freedom lies in educating people, whereas the secret of tyranny is in keeping them ignorant. - Maximilien Robespierre.

Saturday, October 11, 2025

ChatGPT and other AI models can be ‘poisoned’ to spew gibberish, researchers warn

 

 


 
https://incubator.ucf.edu/wp-content/uploads/2023/07/artificial-intelligence-new-technology-science-futuristic-abstract-human-brain-ai-technology-cpu-central-processor-unit-chipset-big-data-machine-learning-cyber-mind-domination-generative-ai-scaled-1.jpg 
Image credit: https://incubator.ucf.edu/
 
AI models like OpenAI’s ChatGPT and Google’s Gemini can be “poisoned” by inserting a relatively small sample of corrupted documents into their training data -the data scraped (i.e. stonen) from all over the world wide web, a study carried out by researchers at Cornell University has warned.

The  joint project which involved researchers from Cornell, the UK AI Security Institute, the Alan Turing Institute and AI firm Anthropic found that as few as 250 documents can produce a “backdoor” vulnerability that causes AI data centres (thousands of networked computers comprising hundreds of thousands of GPUs (Graphuc Processing Units) which parse large language models (LLMs) to spew out gibberish text.

The flaw is particularly concerning because most popular LLMs are pretrained on public text across the internet, including personal websites and blog posts. This makes it possible for anyone to create content that could be caught up in the AI model’s training data. In a way this is good news for free speech campaigners sceptics (those of us who have been around computers long enough to know that what they do is nothing like human intelligence.) Furthermore AI is being hailed byauthoritarian politicians and billionaire pshycopaths as the technology that will enable global technocratic dictatorship.

“Malicious actors can inject specific text into these posts to make a model learn undesirable or dangerous behaviors, in a process known as poisoning,” Anthropic noted in a blog post discussing the issue.

“One example of such an attack is introducing backdoors. Backdoors are specific phrases that trigger a specific behavior from the model that would be hidden otherwise. For example, LLMs can be poisoned to exfiltrate sensitive data when an attacker includes an arbitrary trigger phrase like in the prompt.”

These findings have raised concerns about artificial intelligence security, with the researchers saying it limits the technology’s potential to be used in sensitive applications i.e. just about all applicaions except gaming and pornography.

“Our results were surprising and concerning: the number of malicious documents required to poison an LLM was near-constant – around 250 – regardless of the size of the model or training data,” wrote Dr Vasilios Mavroudis and Dr Chris Hicks from the Alan Turing Institute.

“In other words, data poisoning cyberattacks could be simpler than previously believed. It would be relatively easy for an attacker to create, say, 250 poisoned Wikipedia articles.”

The risks were detailed in a pre-print paper titled ‘Poisoning attacks on LLMs require a near-constant number of poison samples’.

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