Securing MCP Tools on AWS for AI Agents with Authentication, Authorization, and Least Privilege

This article discusses the security challenges of using Model Context Protocol (MCP) to allow AI agents to access backend capabilities on AWS. It outlines a four-layer security model involving inbound authentication, outbound authentication, authorization, and infrastructure-level least privilege.

Details

The article explains that MCP allows AI agents to access backend tools and services, but this convenience comes with significant security risks. Once an agent can reach these tools, it's critical to control who is calling what, on whose behalf, with what scope, and across which boundaries to avoid an overprivileged mess. AWS is addressing this through solutions like Bedrock AgentCore Gateway and AgentCore Identity, which provide a layered security model. The author recommends breaking down the security into four key layers: inbound authentication to control who can reach the agent-facing layer, outbound authentication to securely connect the gateway to downstream tools, authorization to decide if a specific action should be allowed, and infrastructure-level least privilege to limit the damage if a mistake occurs in one of the other layers. This structured approach helps reason about the security challenges posed by MCP-based AI agent systems.