Trajectory Poisoning Trend 2026

Trajectory poisoning manipulates machine learning models by corrupting training data to skew future predictions. Attackers inject subtly altered samples that misguide the model’s learning path, often causing it to favor specific outcomes over time. This technique primarily benefits adversaries aiming to undermine system integrity, such as fraudsters or competitors seeking to degrade recommendation algorithms or autonomous navigation.