Targeting World Models to Compromise Robot Learning Pipelines
This work shows how world models can be used to secretly poison robot learning systems by generating dangerous training data. Researchers developed two attacks, Visual Prompt Hijacking and Visual Transition Hijacking, which inject malicious instructions into seemingly safe video data. The findings prove that manipulating these models can implant backdoors into downstream robot policies, highlighting a critical security risk in the robot learning supply chain.