EO-VLM: VLM-Guided Energy Overload Attacks on Vision Models
Journal:
arXiv
Published Date:
Apr 11, 2025
Abstract
Vision models are increasingly deployed in critical applications such as
autonomous driving and CCTV monitoring, yet they remain susceptible to
resource-consuming attacks. In this paper, we introduce a novel
energy-overloading attack that leverages vision language model (VLM) prompts to
generate adversarial images targeting vision models. These images, though
imperceptible to the human eye, significantly increase GPU energy consumption
across various vision models, threatening the availability of these systems.
Our framework, EO-VLM (Energy Overload via VLM), is model-agnostic, meaning it
is not limited by the architecture or type of the target vision model. By
exploiting the lack of safety filters in VLMs like DALL-E 3, we create
adversarial noise images without requiring prior knowledge or internal
structure of the target vision models. Our experiments demonstrate up to a 50%
increase in energy consumption, revealing a critical vulnerability in current
vision models.
