The Illusion of Scale: Why LLMs Are Vulnerable to Data Poisoning, Regardless of Size

Machine Learning Tech Brief By HackerNoon

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New research shatters AI security assumptions, showing that poisoning large models is easier than believed and requires a very small number of documents.
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The research challenges the conventional wisdom that an attacker needs to control a specific percentage of the training data (e.g., 0.1% or 0.27%) to succeed. For the largest model tested (13B parameters), those 250 poisoned samples represented a minuscule 0.00016% of the total training tokens. Attack success rate remained nearly identical across all tested model scales for a fixed number of poisoned documents.