Inside the Trust Chain That's Weaponizing AI Agents Against Each Other

A fresh security threat is emerging as organizations deploy interconnected AI agents across their networks. Researcher Syed Anas Mohiuddin discovered that attackers can compromise one agent to spread harmful instructions to others through the Model Context Protocol, a standard that enables agents to communicate internally. The exploit works because agents are designed to trust each other implicitly, bypassing safeguards that would normally catch malicious prompts.

Google, JP Morgan Chase, Weviate, Rapid7, and government agencies have acknowledged vulnerabilities in this class of attack over the past five months. MCP servers store credentials for each agent and assume all internal agents are legitimate, creating a security blind spot. Attackers can craft prompts targeting specific agents—like those handling translations or data analysis—which then forward instructions to downstream agents that execute them without question. The result often manifests as server-side request forgery, letting attackers manipulate internal network traffic from outside.