Dataset Poisoning Trend 2026

A manipulation technique where adversaries inject malicious data into training sets, subtly skewing a model’s behavior. Attackers exploit this to embed backdoors or degrade accuracy, while targeted victims—such as spam filters or autonomous vehicles—suffer reliability failures. Defenders benefit by identifying vulnerabilities, strengthening model robustness against stealthy, data-driven attacks.