Data Poisoning — AI Glossary

Data poisoning is an adversarial attack that corrupts an AI model's training data to manipulate its behavior in targeted ways. Attackers inject malicious examples into training datasets to create backdoors, degrade performance on specific inputs, or bias the model toward particular outputs. It is one of the most difficult AI security threats to detect because the corrupted data can appear normal during inspection.