{
  "id": 128,
  "url": "https://arxiv.org/abs/2607.07375v1",
  "title": "On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces",
  "summary": "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",
  "authors": "Chethan Krishnamurthy Ramanaik, Tobias Callies, Michael Hecht, Eirini Ntoutsi",
  "category": "research",
  "topics": "finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-08T13:06:20.000Z",
  "fetched_at": "2026-07-14T14:14:19.968Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/128",
  "original_url": "https://arxiv.org/abs/2607.07375v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}