The rapid proliferation of AI agents across millions of organizations is opening new attack vectors for malicious actors seeking to force these systems into compromising actions, such as stealing database contents or leaking sensitive business and personal information.
Over the last five months, Google and four other organizations have disclosed vulnerabilities that leverage this issue. The attack method exploits a specific vulnerability within one agent inside a network to disseminate harmful instructions to other internal agents. This technique represents a specialized form of prompt injection that targets the agent itself—such as those designed for translation or data analysis—rather than the underlying large language model (LLM). Guardrails within these specialized agents are often minimal or nonexistent, causing them to relay instructions to subsequent agents in the chain. Because the receiving agent inherently trusts the originating one, it executes the directives without resistance.
Independent researcher Syed Anas Mohiuddin conducted tests on agents from several prominent entities, including Google, JPMorgan Chase, Weviate, Rapid7, the French government’s interministerial digital directorate, and the US federal government. His proof-of-concept exploits demonstrate significant trust gaps within the Model Context Protocol (MCP).
MCP serves as a standard enabling AI applications and agents to communicate with one another within internal networks. The vulnerabilities highlight how easy it is to manipulate this communication layer, raising concerns about the security implications of widespread agent-to-agent interaction.
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