Phi: Preference Hijacking in Multi-modal Large Language Models at
  Inference Time
Authors: name: Yifan Lan, name: Yuanpu Cao, name: Weitong Zhang, name: Lu Lin, name: Jinghui Chen

Recently, Multimodal Large Language Models (MLLMs) have gained significant
attention across various domains. However, their widespread adoption has also
raised serious safety concerns. In this paper, we uncover a new safety risk of
MLLMs: the output preference of MLLMs can be arbitrarily manipulated by
carefully optimized images. Such attacks often generate contextually relevant
yet biased responses that are neither overtly harmful nor unethical, making
them difficult to detect. Specifically, we introduce a novel method, Preference
Hijacking (Phi), for manipulating the MLLM response preferences using a
preference hijacked image. Our method works at inference time and requires no
model modifications. Additionally, we introduce a universal hijacking
perturbation -- a transferable component that can be embedded into different
images to hijack MLLM responses toward any attacker-specified preferences.
Experimental results across various tasks demonstrate the effectiveness of our
approach. The code for Phi is accessible at https://github.com/Yifan-Lan/Phi.