On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces

Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry, feature robustness, input-output Jacobians, and the instability of inverse problems. Here, we focus on the spectral structure of intermediate linear transformations that propagate information through modern DNNs, an unexplored mechanism of adversarial vulnerability. Specifically, we investigate transformer-based vision-language models, whose linear layers admit interpret

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ethics.ai (8 July 2026), “On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces,” evidence record 128, https://ethics.ai/record/128 (originally published by arXiv).

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