Laundering AI Authority with Adversarial Examples
Vision-language models (VLMs) are increasingly deployed as trusted authorities -- fact-checking images on social media, comparing products, and moderating content. Users implicitly trust that these systems perceive the same visual content as they do. We show that adversarial examples break this assumption, enabling \emph{AI authority laundering}: an attacker subtly perturbs an image so that the VLM produces confident and authoritative responses about the \emph{wrong} input. Unlike jailbreaks or prompt injections, our attacks do not compromise model alignment; the attack operates entirely at the perceptual level. We demonstrate that standard attacks against publicly available CLIP models t...
| Authors | Jie Zhang, Pura Peetathawatchai, Florian Tramèr et al. |
|---|---|
| Primary category | cs.CR |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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