Cisco MCP Scanner

MCP Scanner

A Python tool for scanning MCP (Model Context Protocol) servers and tools for potential security findings. The MCP Scanner combines Cisco AI Defense inspect API, YARA rules and LLM-as-a-judge to detect malicious MCP tools.

Overview

The MCP Scanner provides a comprehensive solution for scanning MCP servers and tools for security findings. It leverages three powerful scanning engines (Yara, LLM-as-judge, Cisco AI Defense) that can be used together or independently.

The SDK is designed to be easy to use while providing powerful scanning capabilities, flexible authentication options, and customization.

Features

• Multiple Modes: Run scanner as a stand-alone CLI tool or REST API server
• Multi-Engine Security Analysis: Use all three scanning engines together or independently based on your needs.
• Vulnerable Packages Scanning: Scan Python dependencies for known vulnerabilities (CVE/PYSEC/GHSA) using pip-audit integration.
• Readiness Scanning: Zero-dependency static analysis for production readiness issues (timeouts, retries, error handling).
• Comprehensive Scanning: Scan MCP tools, prompts, resources, and server instructions for security findings
• Behavioural Code Scanning: Scan Source code of MCP servers for finding threats.
• VirusTotal Binary Scanning: Automatically detect malware in binary files (images, PDFs, executables, archives) bundled with MCP servers using VirusTotal hash lookups.
• Behavioural Code Scanning: Scan Source code of MCP servers for detecting threats.
• Static/Offline Scanning: Scan pre-generated JSON files without live server connections - perfect for CI/CD pipelines and air-gapped environments
• Explicit Authentication Control: Fine-grained control over authentication with explicit Auth parameters.
• OAuth Support: Full OAuth authentication support for both SSE and streamable HTTP connections.
• Custom Endpoints: Configure the API endpoint to support any Cisco AI Defense environments.
• MCP Server Integration: Connect directly to MCP servers to scan tools, prompts, and resources with flexible authentication.
• Customizable YARA Rules: Add your own YARA rules to detect specific patterns.
• Comprehensive Reporting: Detailed reports on detected security findings.

Installation

Prerequisites

• Python 3.11+
• uv (Python package manager)
• A valid Cisco AI Defense API Key (optional)
• LLM Provider API Key (optional)
• VirusTotal API Key (optional, for binary file malware scanning)

Installing as a CLI tool

uv tool install --python 3.13 cisco-ai-mcp-scanner

Alternatively, you can install from source:

uv tool install --python 3.13 --from git+https://github.com/cisco-ai-defense/mcp-scanner cisco-ai-mcp-scanner

Installing for local development

git clone https://github.com/cisco-ai-defense/mcp-scanner
cd mcp-scanner
uv sync --python 3.13 

Install as a dependency in other projects

Add MCP Scanner as a dependency using uv. From your project root (initialize with uv if needed):

uv init --python 3.13 #if not already done
uv add cisco-ai-mcp-scanner
# then activate the virtual environment:
## macOS and Linux: source .venv/bin/activate
## Windows CMD: .venv\Scripts\activate
## Windows PWSH: .venv\Scripts\Activate.ps1
uv sync

The module name is mcpscanner. Import this module with:

# import everything (not recommended)
import mcpscanner

# selective imports (recommended). For example:
from mcpscanner import Config, Scanner
from mcpscanner.core.models import AnalyzerEnum

Quick Start

Environment Setup

Core API Configuration

Cisco AI Defense API (only required for API analyzer)
export MCP_SCANNER_API_KEY="your_cisco_api_key"
export MCP_SCANNER_ENDPOINT="https://us.api.inspect.aidefense.security.cisco.com/api/v1"
# For other endpoints please visit https://developer.cisco.com/docs/ai-defense/getting-started/#base-url

LLM Configuration (for LLM analyzer and Code Behavioral Analyzer)

Tested LLMs: OpenAI GPT-4o and GPT-4.1 | AWS Bedrock Claude 4.5 Sonnet

# AWS Bedrock Claude with AWS credentials (profile)
export AWS_PROFILE="your-profile"
export AWS_REGION="us-east-1"
export MCP_SCANNER_LLM_MODEL="bedrock/anthropic.claude-sonnet-4-5-20250929-v2:0" # Any AWS Bedrock supported model

# AWS Bedrock Claude with API key (Bearer token)
export MCP_SCANNER_LLM_API_KEY="bedrock-api-key-..." # Generated via Amazon Bedrock -> API Keys
export AWS_REGION="us-east-1"
export MCP_SCANNER_LLM_MODEL="bedrock/us.anthropic.claude-sonnet-4-5-20250929-v2:0" # Any AWS Bedrock supported model

# LLM Provider API Key (required for LLM analyzer)
export MCP_SCANNER_LLM_API_KEY="your_llm_api_key"  # OpenAI

# LLM Model Configuration (optional - defaults provided)
export MCP_SCANNER_LLM_MODEL="gpt-5.2"  # Any LiteLLM-supported model
export MCP_SCANNER_LLM_BASE_URL="https://api.openai.com/v1"  # Custom LLM endpoint
export MCP_SCANNER_LLM_API_VERSION="2025-04-01-preview"  # API version (if required)

# For Azure OpenAI (example)
export MCP_SCANNER_LLM_BASE_URL="https://your-resource.openai.azure.com/"
export MCP_SCANNER_LLM_API_VERSION="2025-04-01-preview"
export MCP_SCANNER_LLM_MODEL="azure/gpt-5.2"

# For Extended Thinking Models (longer timeout)
export MCP_SCANNER_LLM_TIMEOUT=300

Note: If you are using models from Azure Foundry, set the MCP_SCANNER_LLM_BASE_URL and MCP_SCANNER_LLM_MODEL environment variables, as Microsoft has deprecated the need for MCP_SCANNER_LLM_API_VERSION.

VirusTotal Configuration (for file/directory malware scanning)

The VirusTotal analyzer scans files and directories against VirusTotal's malware database using SHA256 hash lookups. It runs as a standalone analyzer via the virustotal subcommand or as part of --analyzers virustotal.