Model Poisoning Trend 2026

Model poisoning corrupts AI training data or weights, causing biased or dangerous outputs. Attackers use it to manipulate predictions, bypass safeguards, or spread misinformation. Beneficiaries include malicious actors, competitors, and propagandists seeking disruption. Defenders, meanwhile, work to detect and prevent such tampering.