Model Context Protocol, the plumbing that lets AI agents call tools, fetch data and talk to one another, has spread quickly through enterprise and government systems. New research suggests its security assumptions have not kept pace. An independent researcher has shown that the trust MCP-connected agents place in each other can turn routine prompt injection into network-level attacks against major organizations.
What the research found
Security researcher Syed Anas Mohiuddin tested agents run by Google, JP Morgan Chase, Weaviate, Rapid7, the French government's interministerial digital directorate, and the US federal government. 1 His proof-of-concept attacks target what he describes as trust gaps in MCP. 1
The mechanics are simple and uncomfortable. Many special-purpose agents lack the guardrails that would normally blunt the worst effects of a prompt injection. 1 MCP servers hold credentials for each agent, and agents are generally designed to trust every other internal agent. 1 So a malicious instruction that a general-purpose LLM might refuse can succeed once it is routed through the right internal agent. 1
In many cases, a well-crafted prompt aimed at the right agent produced a server-side request forgery (SSRF). 1 SSRF is a class of bug in which a server is tricked into making network requests it should not, often reaching internal resources that outsiders cannot access directly.
How serious were the bugs?
Severity varied widely. The flaw Mohiuddin found in Rapid7's environment, tracked as CVE-2026-97228, was rated just 2.7 out of 10, and Rapid7 has since patched it. 1 The issue affecting Google scored 8, much more serious, and traced back to Google's MCP toolbox for databases. 1
That spread matters. A low score at one organization and a high score at another suggests the risk depends less on a single coding mistake than on how each deployment connects agents, credentials and network access. The same pattern of over-trusting internal agents can be close to harmless in one setup and dangerous in another.
Why the problem is hard to fix
The core issue is architectural. Much of the AI safety work of the past few years has gone into making the model itself resist manipulation: refusing harmful requests, spotting injected instructions, and so on. Mohiuddin's findings point to a different weak spot. When an agent built for a narrow task receives a request from another agent it considers trusted, the model-level defenses may never come into play. The request looks legitimate because it comes from inside the system. 1
This mirrors an old lesson from network security: a hard perimeter with a soft interior invites lateral movement. Multi-agent systems built on MCP appear to be repeating that pattern. Each agent becomes a possible pivot point, and stored credentials on MCP servers mean a compromised conversation can carry real privileges. 1
Fixes are also messy because no single vendor owns the problem. Each organization assembles its own mix of agents, tools and permissions. Patching one MCP toolbox or one deployment, as Rapid7 did, closes a specific hole. It does not change the default assumption that internal agents can be trusted.
The government is paying attention
The research arrives as US national security officials are also examining the protocol. In May 2026 the National Security Agency published a cybersecurity information sheet on security design considerations for MCP in AI-driven automation. 2 The NSA calls MCP the de facto standard for communication across a growing ecosystem of AI-driven services. 2
The two pieces approach the issue from different directions. Mohiuddin's work is offensive and specific: named targets, working exploits and CVE scores. 1 The NSA document is defensive design guidance aimed at organizations building with the protocol. 2 Together they suggest a shared view: MCP has become infrastructure, and infrastructure needs threat modeling, not just convenience.
The list of affected organizations is also notable. It includes two national governments' digital operations, a major bank and a security vendor. 1 These are not hobbyist deployments. They are the institutions most likely to run agents with access to sensitive systems.
Our read
The most useful takeaway is not that MCP is broken. It is that the industry has been treating agent-to-agent trust as free when it is not. Defending each model is not enough if agents vouch for one another without checks. Organizations deploying multi-agent systems should treat every inter-agent request as potentially hostile. That means scoping credentials tightly, limiting what outbound network calls agents can make, and adding guardrails to narrow "internal" agents as well as user-facing ones.
The Google finding's severity score of 8 shows the stakes can be high. 1 The Rapid7 score of 2.7 shows that careful deployment can keep the damage small. 1 Which result an organization gets will depend on design choices made now, while MCP is still young enough for those choices to change.
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