The integrity of AI training data is compromised by false information, fabricated content, and biased viewpoints. This contamination not only undermines model accuracy but also risks generating harmful outputs. Furthermore, such data contamination can perpetuate a "legacy of contamination" in subsequent training phases, posing significant risks in critical sectors such as finance, public safety, and healthcare.