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Attackers can now induce specific failure behaviors in VLA models with unprecedented precision, revealing a new dimension of vulnerability in AI systems.
Fragmented privacy patches are insufficient for Embodied AI: a unified, lifecycle-level approach is needed to prevent systemic privacy leakage in real-world deployments.
By explicitly bridging the gap between on-body appearances and flat layouts, BridgeDiff achieves state-of-the-art virtual try-off results, generating more realistic and structurally sound flat-garment representations.
