Cracks in Collaboration: Threat Models and Attacks on Multi-LLM Collaborative Systems

Multi-LLM collaborative systems (setups where multiple AI models work together on complex tasks) can be attacked through three new methods: Decision Poisoning Attack (injecting false instructions to manipulate system output), Indirect Echoleak Attack (extracting private information through model interactions), and Information Collision Attack (exploiting communication between models). While these collaborative systems offer flexibility and better reasoning, their internal communication channels create security and privacy vulnerabilities that attackers can exploit.