OWASP® Foundation Top10 for #LLM - Data and Model Poisoning
Data poisoning is when someone deliberately messes with the data used to train or fine-tune an AI model. It’s not just about bugs—it’s about secretly teaching the model bad habits, planting backdoors, or injecting bias, so the model eventually acts weird, wrong, or just plain evil. 🧠💀 Tampered training data can impair LLM models leading to responses that may compromise security, accuracy, or ethical behavior.
Training Data Poisoning refers to the intentional manipulation of the data used to train an LLM, aiming to skew the model’s behavior or decisions. Poisoned training data can result in flawed or biased outputs that can mislead users or cause direct harm.