Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in
  Multimodal Large Language Model Security
Yihe Fan Yuxin Cao Ziyu Zhao Ziyao Liu Shaofeng Li
Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities
that increasingly influence various aspects of our daily lives, constantly
defining the new boundary of Artificial General Intelligence (AGI). Image
modalities, enriched with profound semantic information and a more continuous
mathematical nature compared to other modalities, greatly enhance the
functionalities of MLLMs when integrated. However, this integration serves as a
double-edged sword, providing attackers with expansive vulnerabilities to
exploit for highly covert and harmful attacks. The pursuit of reliable AI
systems like powerful MLLMs has emerged as a pivotal area of contemporary
research. In this paper, we endeavor to demostrate the multifaceted risks
associated with the incorporation of image modalities into MLLMs. Initially, we
delineate the foundational components and training processes of MLLMs.
Subsequently, we construct a threat model, outlining the security
vulnerabilities intrinsic to MLLMs. Moreover, we analyze and summarize existing
scholarly discourses on MLLMs' attack and defense mechanisms, culminating in
suggestions for the future research on MLLM security. Through this
comprehensive analysis, we aim to deepen the academic understanding of MLLM
security challenges and propel forward the development of trustworthy MLLM
systems.