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Correcting surrogate-specific bias in adversarial optimization can dramatically enhance the transferability of attacks across Vision-Language Pre-training models.
MLLMs get personality right half the time for the wrong reasons, revealing a massive "Prejudice Gap" where models fail to ground their judgments in observable behavior.