Large Language Models (LLMs) are vulnerable to data poisoning attacks that require only a small, fixed number of malicious documents, regardless of the model's size or training data volume. This counterintuitive finding challenges existing assumptions about AI security and highlights significant risks for organizations deploying LLMs, calling for urgent development of robust defenses against such vulnerabilities.