Why do AI gateway and MCP gateway controls still leave security blind spots?

Why This Matters for Security Teams

AI gateways and mcp gateway each reduce a real part of the attack surface, but neither one sees the full agent path from intent to action. An AI gateway can inspect prompts, completions, and model calls, while an MCP gateway can broker tool use, but autonomous agents still move through shell commands, direct APIs, local execution, and vendor-managed surfaces. That leaves a blind spot between “model said it” and “system did it.”

This is why guidance in the OWASP Agentic AI Top 10 and NHI research from OWASP Agentic Applications Top 10 increasingly emphasises end-to-end behaviour rather than isolated interfaces. The operational risk is not only malicious prompts. It is also tool chaining, credential reuse, and privilege escalation across control planes that were never designed to be correlated in one session.

For security teams, the practical issue is that a control can be technically correct and still miss the abuse chain if it only watches one layer. In practice, many teams discover this after an agent has already pivoted across tools and permissions, rather than through intentional end-to-end design.

How It Works in Practice

Effective coverage starts by treating the agent as a workload with a session, not just as a chat interface or a tool client. That means binding model activity, tool calls, secrets access, and downstream API actions into a single observable context. Current best practice is evolving toward intent-based or context-aware authorisation, where each action is evaluated at runtime based on the task, the requested resource, the environment, and the current risk state.

Static RBAC is usually too coarse for this problem. Agents do not follow fixed human workflows, so pre-defined role maps often overgrant access “just in case.” A better pattern is short-lived, task-scoped credentials with just-in-time issuance, automatic revocation, and workload identity as the primitive. The goal is to prove what the agent is at the moment of action, not merely to hand it a long-lived secret. Standards-oriented work such as OWASP Agentic AI Top 10 and NIST AI risk guidance support this shift, while implementation patterns often borrow from SPIFFE, OIDC, and policy-as-code.

• Use per-task tokens with tight TTLs instead of reusable credentials.
• Evaluate policy at request time with full session context, not just endpoint identity.
• Log model, tool, shell, and API activity into one correlated trace.
• Revoke or step-up access when the agent changes task, target, or confidence level.

NHIMG research shows why this matters: the The State of MCP Server Security 2025 report found that only 18% of MCP server deployments implement any form of access scoping for tool permissions, which means the control surface is often already too broad before the agent even begins chaining actions. These controls tend to break down when agents can bypass the gateway through local execution or direct API access because the security plane no longer sees the same transaction boundary.

Common Variations and Edge Cases

Tighter gateway control often increases latency, policy complexity, and operational overhead, so organisations must balance stronger containment against developer friction and runtime cost. That tradeoff is especially visible in multi-agent pipelines, where one agent delegates to another and the chain crosses multiple runtimes, tenants, or vendors.

There is no universal standard for this yet. Some teams enforce a single broker for all tool use, while others allow limited direct access and rely on detection. The first approach improves consistency but can become a bottleneck; the second is easier to adopt but leaves more room for shadow paths. The right answer depends on whether the environment is mostly internal, highly regulated, or heavily vendor-integrated.

Edge cases also include local developer laptops, embedded agents inside SaaS products, and hybrid setups where shell access is granted outside the gateway entirely. In those environments, an AI gateway may still be valuable for content inspection, and an MCP gateway may still reduce obvious tool abuse, but neither should be treated as a complete control. The more the agent can act outside a single mediated path, the more session-level telemetry and zero standing privilege matter. In practice, the remaining gaps show up fastest in environments where agents can reach both internal data and external SaaS systems from the same identity.

Related resources from NHI Mgmt Group

• How should security teams use AI in secret scanning without creating new blind spots?
• What is MCP in the context of AI security?
• Why do hybrid workforces create blind spots for AI security controls?
• What are the security risks associated with AI agents?