The Wolf Within: Covert Injection of Malice into MLLM Societies via an
  MLLM Operative
Zhen Tan Chengshuai Zhao Raha Moraffah Yifan Li Yu Kong Tianlong Chen Huan Liu
Due to their unprecedented ability to process and respond to various types of
data, Multimodal Large Language Models (MLLMs) are constantly defining the new
boundary of Artificial General Intelligence (AGI). As these advanced generative
models increasingly form collaborative networks for complex tasks, the
integrity and security of these systems are crucial. Our paper, ``The Wolf
Within'', explores a novel vulnerability in MLLM societies - the indirect
propagation of malicious content. Unlike direct harmful output generation for
MLLMs, our research demonstrates how a single MLLM agent can be subtly
influenced to generate prompts that, in turn, induce other MLLM agents in the
society to output malicious content. This subtle, yet potent method of indirect
influence marks a significant escalation in the security risks associated with
MLLMs. Our findings reveal that, with minimal or even no access to MLLMs'
parameters, an MLLM agent, when manipulated to produce specific prompts or
instructions, can effectively ``infect'' other agents within a society of
MLLMs. This infection leads to the generation and circulation of harmful
outputs, such as dangerous instructions or misinformation, across the society.
We also show the transferability of these indirectly generated prompts,
highlighting their possibility in propagating malice through inter-agent
communication. This research provides a critical insight into a new dimension
of threat posed by MLLMs, where a single agent can act as a catalyst for
widespread malevolent influence. Our work underscores the urgent need for
developing robust mechanisms to detect and mitigate such covert manipulations
within MLLM societies, ensuring their safe and ethical utilization in societal
applications. Our implementation is released at
\url{https://github.com/ChengshuaiZhao0/The-Wolf-Within.git}.