Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security
It addresses security vulnerabilities in MLLMs, which are critical for developing reliable AI systems, but it is incremental as it synthesizes existing research rather than introducing new methods.
This paper surveys the security risks of incorporating image inputs into multimodal large language models (MLLMs), highlighting how these vulnerabilities enable covert and harmful attacks, and it provides a comprehensive analysis of threats and defense mechanisms to guide future research.
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.