analysis of training data poisoning, a critical integrity attack against AI and ML systems. It explains how adversaries corrupt the foundational learning phase by manipulating datasets, leading to altered model behavior, ranging from performance degradation to hidden backdoor attacks. The text highlights that large language models (LLMs) and generative AI are particularly vulnerable due to their reliance on vast, often unvetted internet data, and critically notes that larger models can paradoxically be more susceptible to learning malicious behaviors from minimal poisoned data. Finally, it outlines a multi-layered defense strategy, emphasizing data validation, robust model training, and strong operational security controls throughout the MLOps lifecycle, aligned with industry frameworks like NIST and OWASP.