When an encoded attack passes a content guard, the guard either never represented the payload's harmful content or represented it and failed to act. End-to-end attack success rate reports one number for both, yet the two have opposite remedies: one is a representational limit that more safety training cannot reach, the...
Haoyu Zhang, Yi Feng, Shi-Bo Zheng et al.· 0 citations
We show that aligned vision-language models also condition refusal on a property of a request's form: whether an image is attached, holding everything the request asks fixed. Attaching a blank canvas, an image that cannot be read, cannot relate to the request, and is byte-identical across every prompt in its condition,...
Haoyu Zhang, Yi Feng, Han-Wen Liu et al.· 0 citations
An empirical safety– utility ceiling for the non-iterative recovery-based defenses the authors evaluate, recurring across every guard and both target VLMs, is exposed.
Safety classifiers ("guards") are the dominant black-box defense for vision-language models, yet a guard judges an input's surface form, not its meaning: a harmful request re-encoded as set theory, formal logic, a classical language, code, or text rendered inside an image slips past a guard that would block it in plain...
Haoyu Zhang, Zhuo-Xiang Wang, Shi-Bo Zheng et al.· 0 citations
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