Understanding and Exploiting Phase Sensitivity for Attacking Large Vision–Language Models

Abstract

Although Large Vision-Language Models (LVLMs) have demonstrated remarkable reasoning capabilities across various downstream multimodal tasks, they are proven to be vulnerable to carefully designed adversarial examples. Existing LVLM attackers show that exploring external components of adversarial guidance (e.g., forcing adversarial alignment, resembling harmful features) can help improve adversarial effects. However, leveraging the intrinsic patterns of LVLMs to induce adversarial perturbation generation by exploring how LVLMs perceive images has not been deeply studied. Inspired by the cognitive science, in this paper, we make the first attempt to investigate the interference of adversarial perturbation from the perspectives of image phase, and find that LVLMs are sensitive to the phase-aware image structure. Motivated by this, we propose a novel LVLM attack method called BadPhase with further backdoor designs, to implant adversarial phase as triggers into any image inputs via data poisoning so as to control the LVLMs’ predictions. A textual trigger and a backdoor perturbation switcher are also introduced to activate the malicious behavior only when both triggers are present. The whole backdoor optimization is implemented at the test-time to reduce the resource reliance. Experiments on four popular LVLMs and three benchmarks demonstrate the effectiveness of our proposed method.