Enterprise AI deployments have a blind spot problem — and a new whitepaper from Noma Security makes it uncomfortably quantifiable: 25% of scanned MCP servers expose code execution pathways that existing security tooling is either missing or ignoring entirely.

This isn’t a theoretical concern. As organizations race to connect AI agents to internal tools, databases, and APIs via the Model Context Protocol (MCP), the attack surface is expanding faster than the security frameworks designed to monitor it.

Two Layers, Two Risk Profiles

Noma Security’s research draws a clear architectural line between the two primary extension mechanisms in modern agentic systems:

MCP Servers expose deterministic code functions. Their invocations are structured and loggable — in principle, you can enumerate what they can do. This is the observable half of the stack. Most enterprise security tools that have been updated to cover “AI agents” have focused here.

Skills (textual instruction sets loaded directly into a model’s reasoning context) are fundamentally different. Their effect depends on conversational state, prior context, and the model’s current reasoning path — none of which can be enumerated the way source code can. Security tools built for MCP governance simply cannot see this layer.

The whitepaper’s argument is stark: organizations have secured the part they can observe and left the part they cannot observe entirely unprotected.

The Code Execution Problem

Even within the observable MCP layer, the news is troubling. Noma’s scan found that 1 in 4 MCP servers exposes code execution pathways — meaning an attacker (or a compromised agent) could potentially run arbitrary code through what appears to be a legitimate, governed integration.

This matters enormously for agentic pipelines. Unlike traditional software that executes predetermined operations, an AI agent using an MCP server can:

  • Chain tool calls in ways developers never anticipated
  • Be manipulated (via prompt injection or prompt attack) into invoking tools in unauthorized sequences
  • Reach code execution functionality through combinations of “safe” individual calls

The code execution risk isn’t just about a single malicious MCP server — it’s about the emergent behavior of agents connecting multiple servers in ways that individually seem fine but collectively open dangerous pathways.

What Most Enterprise Security Tools Are Missing

Noma’s central finding is that the governance tooling market has raced to address MCP security while leaving Skills governance entirely unaddressed. This creates a false sense of security: organizations implement MCP auditing, see clean reports, and assume their agentic deployments are secured — while the Skills layer remains a completely open attack surface.

The distinction matters because Skills-based attacks look different from MCP-based attacks. Instead of exploiting a code execution endpoint, a Skills-layer attack modifies the model’s reasoning context — effectively changing how the agent interprets its own instructions and constraints.

These attacks can be extremely difficult to detect after the fact because they leave no structured audit trail. The agent’s “behavior” changed, but the MCP logs show nothing unusual.

What This Means for Your Agentic Stack

If you’re running enterprise AI agents today — whether through internal platforms, commercial agentic products, or custom-built pipelines — the Noma Security research suggests several immediate audit priorities:

  1. Inventory your MCP servers: Know exactly which MCP servers your agents can reach and what functions they expose. Flag any that include code execution capabilities and evaluate whether that exposure is intentional.

  2. Treat Skills as a governance gap: If your current AI security posture only covers MCP, you’re missing the Skills layer entirely. Start building audit processes for what instruction sets are being loaded into your agents’ reasoning contexts.

  3. Review agent-to-agent trust boundaries: In multi-agent systems, a compromised agent can become a vector for attacking other agents via the Skills layer. Review trust boundaries between agents carefully.

  4. Demand Skills-layer visibility from vendors: If you’re using commercial agentic products, ask vendors directly how they govern the Skills layer — not just MCP. A vendor that can’t answer that question clearly has a gap.

Sources

  1. Anamarija Pogorelec, Help Net Security — “One in four MCP servers opens AI agent security to code execution risk” (May 5, 2026)
  2. Noma Security — Whitepaper on AI agent security and MCP/Skills risk profiles

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260505-0800

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