  Large language models have become increasingly prominent, also signaling a
shift towards multimodality as the next frontier in artificial intelligence,
where their embeddings are harnessed as prompts to generate textual content.
Vision-language models (VLMs) stand at the forefront of this advancement,
offering innovative ways to combine visual and textual data for enhanced
understanding and interaction. However, this integration also enlarges the
attack surface. Patch-based adversarial attack is considered the most realistic
threat model in physical vision applications, as demonstrated in many existing
literature. In this paper, we propose to address patched visual prompt
injection, where adversaries exploit adversarial patches to generate target
content in VLMs. Our investigation reveals that patched adversarial prompts
exhibit sensitivity to pixel-wise randomization, a trait that remains robust
even against adaptive attacks designed to counteract such defenses. Leveraging
this insight, we introduce SmoothVLM, a defense mechanism rooted in smoothing
techniques, specifically tailored to protect VLMs from the threat of patched
visual prompt injectors. Our framework significantly lowers the attack success
rate to a range between 0% and 5.0% on two leading VLMs, while achieving around
67.3% to 95.0% context recovery of the benign images, demonstrating a balance
between security and usability.
