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Data poisoning involves strategically manipulating training data to alter machine learning model behavior, often imperceptibly. Three primary motivations exist: criminal activity (degrading security models or creating fraudulent predictions), IP protection (tools like Nightshade and Glaze help creators sabotage models trained on stolen content), and marketing (SEO-like tactics to bias LLM outputs toward specific brands). Even small amounts of poisoned data (as few as 250 documents) can significantly impact models, and removal is extremely difficult. Mitigation strategies include avoiding unauthorized data, rigorous data vetting, monitoring training processes, and thorough model testing before deployment.

Data Poisoning in Machine Learning: Why and How People Manipulate Training Data