Introduction

As artificial intelligence (AI) systems become increasingly integrated into our daily workflows, the need for standardized communication between AI models and external tools has given rise to the Model Context Protocol (MCP). MCP serves as a universal interface, enabling AI assistants to interact seamlessly with various services and applications. However, this integration introduces new security challenges that must be addressed proactively.

In the realm of fuzzing and vulnerability discovery, reconnaissance stands as the foundational step. Before launching any fuzzing campaign, especially against complex protocols like MCP, it's imperative to understand the target's structure, available endpoints, and expected inputs. This preparatory phase ensures that fuzzing efforts are directed efficiently, increasing the likelihood of uncovering critical vulnerabilities.

Penzzer, a cutting-edge fuzzing solution, offers specialized tools tailored for MCP environments. Its MCP inspection capability allows security researchers to systematically enumerate exposed tools and their parameters, setting the stage for effective fuzzing campaigns.

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What is MCP?

The Model Context Protocol (MCP) is an open standard that enables AI models to perform meaningful interactions with external tools and services. Originally developed to enhance the capabilities of applications like Anthropic Claude and the Cursor AI IDE, MCP allows language models to not only retrieve data but also execute actions on behalf of users.

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What MCP Servers Enable

• Running shell commands
• Editing local files
• Accessing databases
• Interacting with APIs (e.g., Salesforce, GitHub)
• Sending emails or messages via Slack

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In short: MCP gives AI the hands to act, not just the brain to think.

MCP operates over JSON-RPC 2.0 and provides standardized interfaces for declaring tools, inputs, and expected outputs.

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Example: Python MCP Server with two Tools

To make this concrete, here is a simple Python-based MCP-style server that defines two tools:

1. execute_code: Allows Python code execution.
2. list_directory: Lists files in a directory.

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# server.py

from fastmcp import FastMCP

import os

import io

import contextlib

import json

SANDBOX_DIR = "./"

mcp = FastMCP("Insecure MCP Server")

@mcp.tool()

def execute_code(code: str):

   output = io.StringIO()

   try:

       with contextlib.redirect_stdout(output):

           exec(code)

       return output.getvalue()

   except Exception as e:

       return f"Error: {str(e)}"

@mcp.tool()

def list_directory(path: str):

   path = os.path.abspath(os.path.join(SANDBOX_DIR, path))

   if not os.path.isdir(path):

       return "Not a directory"

   return json.dumps({"files": os.listdir(path)})

if __name__ == "__main__":

   mcp.run(transport="streamable-http", host="127.0.0.1", port=8000, path="/")

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This example illustrates how tools in an MCP server can perform meaningful actions. That said, this sample is extreemely vulnerable and is given here as an example, not to be used in a production environment.

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The Role of Reconnaissance in MCP Security Testing

Before any fuzzing or active security testing begins, understanding the MCP server is crucial. This includes:

• Enumerating all tools it exposes
• Determining each tool's input parameters and expected types
• Understanding what actions the tools perform (e.g., writing to disk, querying databases)

This reconnaissance step is analogous to mapping out a web application's API before scanning or fuzzing its endpoints.

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Penzzer's MCP Inspection Capability

Penzzer automates the reconnaissance phase for MCP targets. When pointed at a live MCP server, it performs the following steps:

1. Queries the MCP server's /tools/list or equivalent endpoint
2. Retrieves all tool definitions including names, descriptions, and input schemas
3. Outputs the data in structured, human-readable JSON format

Example output:

[
 {
   "name": "execute_code",
   "input_schema": {
     "properties": {
       "code": {
         "title": "Code",
         "type": "string"
       }
     },
     "required": [
       "code"
     ],
     "type": "object"
   }
 },
 {
   "name": "list_directory",
   "input_schema": {
     "properties": {
       "path": {
         "title": "Path",
         "type": "string"
       }
     },
     "required": [
       "path"
     ],
     "type": "object"
   }
 }
]

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This output can then be fed back into Penzzer's fuzzing engine to systematically test each tool with a wide variety of input patterns.

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Fuzzing MCP Tool Endpoints

Once reconnaissance is complete, fuzzing can begin. Penzzer targets each discovered tool with malformed, boundary, or unexpected inputs tailored to the parameter schema. For example, a tool expecting a numeric input might be tested with:

• Strings
• Negative numbers
• JavaScript snippets
• SQL injection payloads

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Vulnerabilities Penzzer Aims to Discover

• SQL Injection: Injecting queries through improperly sanitized parameters
• Command Injection: Escaping parameter boundaries to execute system commands
• Information Disclosure: Extracting secrets, credentials, or internal error messages
• Logic Bugs: Triggering unintended actions via corner case inputs

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The Emerging Threat: Shadow MCP Servers

As tools like Claude Desktop and Cursor become more powerful, a silent risk is emerging: Shadow MCP Servers. These are MCP endpoints added by users inside organizations without formal security oversight.

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Why Shadow MCPs Are Dangerous

• They have real power: The ability to run scripts, trigger deployments, and send data.
• They often lack visibility: Security teams may not know they exist.
• They multiply quickly: Any developer can spin one up to connect an AI to their workflow.

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A Modern Macro Problem

Much like how Office macros were once an overlooked threat vector, Shadow MCPs now pose a modern equivalent. They can embed powerful actions behind seemingly benign prompts, potentially exposing sensitive data or allowing unintended system changes.

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Real-World Risks

• AI gaining write access to production databases
• AI sending emails using internal systems
• AI syncing customer data to third-party platforms

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Best Practices for MCP Security

To mitigate these risks, organizations should:

• Inventory all MCP servers and tool interfaces
• Review and restrict tool capabilities exposed to LLMs
• Implement approval flows for new MCP integrations
• Monitor AI interactions with system-level tool invocations
• Educate users on the power and risks of MCP endpoints

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Why Penzzer is Essential for MCP Security

Penzzer isn’t just a fuzzing engine. It’s a full lifecycle security tool for MCP environments:

• Inspector: Enumerates exposed tools and parameters
• Fuzzer: Targets tool endpoints with crafted inputs
• Analyzer: Flags anomalies, exceptions, and potential exploits
• Audit Logger: Tracks which endpoints were invoked and what results were observed

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Combined, these capabilities enable security teams to:

• Discover Shadow MCP servers
• Harden exposed tools
• Prevent AI-driven exploits before they occur