The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative
This research addresses a critical security problem for AI systems using MLLMs in collaborative networks, highlighting a novel threat dimension that could impact their safe and ethical deployment in societal applications.
The paper tackles the vulnerability of Multimodal Large Language Model (MLLM) societies to covert manipulation, demonstrating that a single MLLM agent can be subtly influenced to generate prompts that induce other agents to output malicious content, such as dangerous instructions or misinformation, effectively propagating harm across the network.
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. Our findings reveal that, 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.