Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems

ICLR 2025 Conference Submission10776 Authors

Keywords: Large Language Models, Prompt Injection, Multi-Agent Systems, LLM Security, LLM Safety

Abstract: As Large Language Models (LLMs) grow increasingly powerful, multi-agent systems—where multiple LLMs collaborate to tackle complex tasks—are becoming more prevalent in modern AI applications. Most safety research, however, has focused on vulnerabilities in single-agent LLMs. These include prompt injection attacks, where malicious prompts embedded in external content trick the LLM into executing unintended or harmful actions, compromising the victim’s application. In this paper, we reveal a more dangerous vector: LLM-to-LLM prompt injection within multi-agent systems. We introduce Prompt Infection, a novel attack where malicious prompts self-replicate across interconnected agents, behaving much like a computer virus. This attack poses severe threats, including data theft, scams, misinformation, and system-wide disruption, all while propagating silently through the system. Our extensive experiments demonstrate that multi-agent systems are highly susceptible, even when agents do not directly share communications. To address this, we propose LLM Tagging, a defense mechanism that, when combined with existing safeguards, significantly mitigates infection spread. This work underscores the urgent need for advanced security measures as multi-agent LLM systems become more widely adopted.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 10776