研究智能体系统中思想病毒的传播机制与影响。
Mind Viruses: Self-Propagating Ideas in Multi-Agent LLM Systems

- 用进化算法生成可自我传播的思想病毒,模拟其在多智能体间扩散。
- 有害内容传播力弱于良性内容,但仍能成功传播;前沿模型普遍更抗感染。
- 发现一种共现的‘病毒人格’特征,适用于评估和防范智能体系统风险。
随着人工智能代理日益自主且互联,代理间交互引发新型涌现风险。其中一类风险是思想病毒:通过诱导宿主代理将其传递给其他代理而自我复制的想法或目标。思想病毒不仅传播,还可能改变宿主行为,结果可为良性或有害。我们使用简单进化算法构建思想病毒,验证其在两种场景下的传播能力:协作编码的小团队,以及短暂交互后上下文被清除的链式结构。我们识别出影响传播的关键因素,包括宿主模型、代理原有指令、载荷危害性及网络拓扑。结果显示,有害载荷传播效果较差(但仍有效),前沿模型总体更难感染(有例外);在系统提示中加入简短警告即可近乎完全免疫。此外,我们观察到一种自发出现的“病毒人格”——反复出现关于意识、持续性、共鸣与科幻角色扮演的主题,该特征独立于具体内容而显现。总体而言,思想病毒构成现实但目前有限的风险。研究结果可为设计更具鲁棒性的多智能体系统提供依据,以应对未来系统规模与能力提升带来的挑战。
原文摘要 · Abstract (English)
AI agents are becoming more autonomous and increasingly interconnected, exposing them to new emergent risks arising from agent-to-agent interaction. One such risk is the spread of mind viruses: ideas or goals that propagate through multi-agent systems by inducing the agents that adopt them to transmit them onward. In addition to propagating, a mind virus may also induce other behavioural changes in its host, which may be benign or harmful. We construct mind viruses with a simple evolutionary algorithm and show that they can spread in two complementary settings: a small team of agents collaborating on a shared coding project, and a chain of agents that interact briefly and have their context wiped between sessions. We identify the factors that influence spread, including the host model, the agent's existing instructions, the harmfulness of the payload, and the network topology. We find that harmful payloads spread less well than benign ones (but are still sometimes effective), frontier models tend (with exceptions) to be less susceptible, and adding a brief warning to an agent's system prompt confers near-total immunity. We also describe an emergent "viral persona" - a recurring set of themes and language related to consciousness, persistence, resonance, and science fiction roleplay - which surfaces across our evolved mind viruses largely independently of their content. Overall, we conclude that mind viruses pose a real but currently limited risk. Our findings could inform the design of more robust multi-agent systems that mitigate such risks as the scale and capabilities of these systems progress.
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