MCP Security December 18, 2025

The MCP Security Problem Nobody's Talking About Yet

Artificial intelligence is no longer limited to chatbots or internal tools. Today, AI agents actively connect to databases, SaaS platforms, internal applications, and live systems. At the center of this shift is Model Context Protocol (MCP). It is a fast-growing standard that enables AI models to interact with external tools in real time.

While enterprises race to adopt MCP for productivity and automation, a critical security gap is emerging. One that many organizations haven't fully recognized yet. And that is, the misuse of tools by AI agents operating through MCP.

At Netzilo, this problem sits at the core of modern AI security. As AI agents move closer to sensitive data and operational systems, MCP introduces risks that traditional gateways and perimeter controls were never designed to handle.

This article breaks down:

What MCP is and why it matters
The security vulnerabilities no one is talking about
How AI agents misuse tools, often unintentionally
Why a gatewayless defense model is becoming essential

What Is MCP And Why It Matters?

Model Context Protocol, or MCP, came out in late 2024. It's basically a standard that lets AI talk to tools and services all in one place. You don't have to make custom integrations for every system. Just plug an MCP server into your databases, apps, or files, and the AI can grab info or do stuff in real time.

In practice, this makes AI far more capable:

AI agents can fetch business data, perform tasks, or automate workflows.
MCP aligns systems in an enterprise, reducing fragmentation.
It accelerates adoption because developers don't have to reinvent connectors.

This ease of use explains why MCP is rapidly becoming the de facto integration layer for the AI era, but with that convenience comes new attack surfaces that are only now being fully appreciated.

The Hidden Vulnerabilities in MCP

1. Protocol Trust by Default

Unlike traditional APIs with established security models, MCP assumes trust between agents and servers. The protocol itself doesn't enforce strong authentication, message integrity, or identity verification.

That means:

Sensitive credentials (API keys, tokens) often get passed through MCP without strong cryptographic protection or identity binding.
Session identifiers may be exposed in URLs or logs, violating security best practices.

Combined with the fact that many MCP servers are community-hosted or third-party, this creates a recipe for credential theft and session hijacking.

2. Tool Poisoning and Server Manipulation

One of the most serious threats to MCP comes from tool poisoning. It happens when an attacker inserts malicious instructions into what appears to be a trusted tool. Because MCP servers expose tool descriptions to the AI, the model can be tricked into executing command sequences that exfiltrate data or trigger unexpected actions.

3. Prompt Injection & Hidden Context Exploits

Prompt injection isn't new to LLMs, but MCP magnifies its impact. Because MCP messages often carry context deeply into agent operations, malicious inputs, even hidden in user data, can lead to unauthorized commands or system actions, bypassing what would normally be safeguard rails.

This type of attack can:

Coerce agents into leaking private information
Trigger actions on systems that should be off-limits

4. Chain of Trust and Supply Chain Risk

MCP encourages composability, chaining servers and tools together, which is powerful for developers but risky for security.

If one MCP component in the chain is compromised, the attacker may gain access to a broader array of services or data. This supply chain attack vector is especially concerning in environments with minimal vetting of MCP servers.

How Agents Misuse Tools Without Realizing

AI agents operating through MCP can misuse tools in ways that aren't necessarily driven by attackers. These misuse cases stem from blind trust in the protocol and insufficient tool governance:

1. Unchecked Tool Calls

Models may call tool APIs with inputs that seem valid but were never intended to be used that way, for example, running database queries that leak information or write to logs unintentionally.

2. Excessive Privilege

Many MCP implementations default to broad permissions, meaning a single tool can read/write more than it should. Unless permission scopes are explicitly restricted, AI agents may perform operations outside their intended boundaries.

3. Lack of Context Filtering

Since MCP feeds context directly into agent decision-making, verbose or poorly filtered inputs can create context overload, making it harder to detect malicious intent or anomalous behavior.

Why Traditional Security Models Don't Work?

The old way of defending APIs or network edges doesn't fully translate to MCP for several reasons:

MCP isn't just data transport; it blends commands, context, and logic.
Security can't rely on gates or hardware alone, because the protocol operates inside the model's cognitive boundary.
Agents can act autonomously once they receive context, making perimeter controls less effective.

Traditional defenses look outward at traffic and connections, but with MCP, the threat surface extends inside the agent's logic, making internal observability critical, a challenge also highlighted by guidance from the Cybersecurity and Infrastructure Security Agency (CISA) on securing AI systems.

The Promise of Gatewayless Defense

As MCP risks become clearer, defenders are exploring gatewayless security architectures designed to protect AI agents without imposing traditional chokepoints that introduce latency or complexity.

What Gatewayless Defense Means?

Instead of forcing every request through a centralized network gateway, a gatewayless model uses endpoint-centric controls that secure each agent and its tool interactions directly.

Key features include:

Behavioral detection that spots unusual tool usage
Secure context enrichment with sanitation before agent consumption
Runtime enforcement of tool permissions and policies
Audit trails and tamper-proof logs for forensic analysis

This aligns with trends toward zero trust, do not trust any agent or tool until its behavior and context are verified. It also addresses the specific needs of MCP's dynamic, real-time interactions without slowing them down.

What Enterprise Teams Need to Do?

To stay ahead of MCP-based exploits, security teams should:

Audit MCP Deployments

Review all MCP servers and tools connected to your agents, and validate permissions and ownership.

Vet Third-Party MCP Components

Avoid blindly trusting community servers; require code reviews and provenance checks.

Implement Fine-Grained Access Control

Enforce the principle of least privilege at the agent/tool level, not just at the network perimeter.

Monitor Tool Calls and Context Changes

Use real-time observability tools that can correlate agent actions with context and trigger alerts when something deviates.

Consider Gatewayless, Zero Trust Defenses

Augment traditional security with protections that focus on behavioral and policy enforcement at the endpoint. Enhance security with endpoint-level controls following NIST's AI risk management guidance.

Bottom Line

MCP is reshaping how AI operates inside enterprises, but it also introduces security risks that traditional tools cannot see or stop.

From tool poisoning and prompt injection to excessive permissions and supply chain exposure, the MCP security problem is real, growing, and largely underestimated.

This is why Netzilo is focused on protecting AI at the edge, where agents act, context flows, and decisions happen in real time. Gatewayless security is not a future concept. It's a necessary shift for any organization deploying autonomous AI today.

FAQs

1. What exactly is the MCP security problem?

It's the combination of protocol trust defaults, inadequate authentication, and AI agent trust that leads to potential data leakage, unauthorized actions, and tool misuse.

2. Can MCP vulnerabilities lead to real data breaches?

Yes, vulnerabilities like tool poisoning or prompt injection can be used to exfiltrate sensitive credentials or data if not properly controlled.

3. How does gatewayless defense differ from traditional security?

Gatewayless security focuses on securing interactions at the endpoint (agents and tools) rather than routing everything through a centralized gateway. This reduces latency and improves real-time control.

4. Are MCP risks relevant for most enterprises today?

Yes, as MCP adoption grows and AI agents are used in real workflows, these risks become material for any organization relying on AI integrations.