Backdoor attacks on VLM-based scanpath predictors can redirect fixations toward chosen objects or inflate durations using input-conditioned triggers that evade cluster detection, and no tested defense blocks them without hurting clean accuracy.
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TRAP is a tail-aware ranking attack that plants a backdoor in world models so that a trigger causes the model to reorder a few critical imagined trajectories and redirect planning while preserving normal behavior on clean inputs.
citing papers explorer
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Follow My Eyes: Backdoor Attacks on VLM-based Scanpath Prediction
Backdoor attacks on VLM-based scanpath predictors can redirect fixations toward chosen objects or inflate durations using input-conditioned triggers that evade cluster detection, and no tested defense blocks them without hurting clean accuracy.
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TRAP: Tail-aware Ranking Attack for World-Model Planning
TRAP is a tail-aware ranking attack that plants a backdoor in world models so that a trigger causes the model to reorder a few critical imagined trajectories and redirect planning while preserving normal behavior on clean inputs.
- Universal Graph Backdoor Defense: A Feature-based Homophily Perspective