What if I told you that a few hundred poisoned documents could break models as big as GPT-4 or Claude? 😵 Anthropic just proved it. Their new paper shows that just 250 samples can secretly backdoor any LLM, no matter the size. In today’s episode, we unpack this wild discovery, why it changes AI security forever, and what it means for the future of open-web training.

We’ll talk about:

How Anthropic’s team used 250 poisoned docs to make 13B-parameter models output gibberish on command
Why bigger models don’t mean safer models and why scale can’t protect against poison
The rise of TOUCAN, the open dataset from MIT-IBM that’s changing how AI agents learn real-world tools
The new AI race: from Jony Ive’s “anti-iPhone” with OpenAI to Amazon’s Quick Suite for business automation

Keywords: Anthropic, LLM security, data poisoning, backdoor attacks, TOUCAN dataset, OpenAI, Claude, Google Gemini, AI agents

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🎙️ EP 116: Just 250 Docs Can Hack a 13B AI Model?! & Google Shoe Try-Ons

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🎙️ EP 116: Just 250 Docs Can Hack a 13B AI Model?! & Google Shoe Try-Ons