The risks in the protocol connecting AI to the digital world (thebulletin.org)

A lightweight framework called the model context protocol (MCP), created by Anthropic and released as open source, has quietly become the plumbing that lets modern AI assistants (e.g., Claude) act as full-fledged agents: reading inboxes, editing spreadsheets, creating calendar events, and integrating with tools like Asana, Notion and Stripe. MCP works by reading a service’s documentation and translating disparate APIs so models can execute actions without bespoke integrations—turning chatbots into systems that interact with the real world. Its rapid, community-driven adoption (including uptake by other major vendors) unlocked powerful workflows but also exposed real-world brittleness—simple errors like mis-setting calendar start times illustrate how sophisticated capabilities can still fail in mundane ways. That accelerated adoption has outpaced security and governance. MCP was designed for local, simple use; authentication, identity tracing and auditability were afterthoughts and are still inconsistently implemented across servers. Sensitive data flows into models’ “context windows” and can persist, be seen by providers, or leak into responses or training—raising privacy, compliance and fairness risks in domains like hiring, housing and healthcare. Emerging mitigations include stricter auth standards, approved-server allowlists, network monitoring and identity analytics, and middleware proxies between agents and services; industry consortia (e.g., Coalition for Secure AI) are developing best practices. MCP promises major productivity and cybersecurity benefits, but without rapid, standardized guardrails its growth risks undermining trust and legal/operational safety.