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Security scanner and protocol fuzzer for MCP (Model Context Protocol) servers. Found and reported vulnerabilities in official Anthropic and GitHub MCP implementations.

Project description

mcpsec

License: MIT Python 3.11+ PyPI Bugs Found Fuzz Cases Semgrep Rules

Security scanner and protocol fuzzer for MCP servers.

Most MCP security tools do static analysis. mcpsec connects to live servers and proves exploitation.

InstallationUsageScannersFuzzing


Why mcpsec?

MCP is the protocol connecting AI agents (Claude, Cursor, VS Code) to external tools. Every major AI company uses it. Its security is often overlooked.

  • 82% of MCP implementations have path traversal vulnerabilities
  • 67% are vulnerable to code injection
  • ~2,000 internet-exposed MCP servers found with zero authentication
  • Anthropic's own Git MCP server had 3 critical RCE vulnerabilities

mcpsec has been used to discover and report 5 vulnerabilities across Anthropic and GitHub MCP implementations, affecting Python, TypeScript, and Go SDK ecosystems.


Installation

pip install mcpsec

For AI-powered features:

pip install mcpsec[ai]

Usage

Runtime Scanning

# Scan via stdio
mcpsec scan --stdio "npx @modelcontextprotocol/server-filesystem /tmp"

# Scan via HTTP with auth
mcpsec scan --http http://localhost:8080/mcp -H "Authorization: Bearer TOKEN"

# Enumerate attack surface
mcpsec info --stdio "python my_server.py"

Protocol Fuzzing

# Standard fuzzing (150+ cases)
mcpsec fuzz --stdio "python my_server.py"

# High intensity (500+ cases)
mcpsec fuzz --stdio "python my_server.py" --intensity high

# Target specific attack class
mcpsec fuzz --stdio "python my_server.py" -g protocol_state_machine
mcpsec fuzz --stdio "python my_server.py" -g id_confusion

# AI-powered payload generation
mcpsec fuzz --stdio "python my_server.py" --ai

Static Analysis

# Local source
mcpsec audit --path ./my-mcp-server

# GitHub repository
mcpsec audit --github https://github.com/user/mcp-server

# With AI validation
mcpsec audit --github https://github.com/user/mcp-server --ai

Rogue Server (Client Testing)

# Test MCP clients for vulnerabilities
mcpsec rogue-server --port 9999 --attack all

Scanners

Scanner Description
prompt-injection Hidden instructions in tool descriptions
command-injection OS command injection with proof of exploitation
path-traversal File traversal with proof of exploitation
ssrf Server-Side Request Forgery to internal services
auth-audit Missing auth, dangerous tool combinations
description-prompt-injection LLM manipulation via descriptions
resource-ssrf SSRF via MCP resource URIs
capability-escalation Undeclared capability abuse

Fuzz Generators

Generator Description
malformed_json Invalid JSON structures
protocol_violation JSON-RPC spec violations
type_confusion Type mismatch attacks
unicode_attacks Encoding edge cases
injection_payloads SQLi, XSS, command injection
protocol_state_machine MCP state violations
id_confusion JSON-RPC ID edge cases

Semgrep Rules

49 MCP-specific rules:

  • Command injection (exec, spawn, child_process)
  • SQL injection (raw queries, ORM bypass)
  • Path traversal (path.join with unsanitized input)
  • Description injection (dynamic tool descriptions)
  • Resource URI issues (SSRF vectors)
  • Protocol handler vulnerabilities

Configuration

AI Provider Setup

mcpsec setup

Supports: OpenAI, Anthropic, Google, Groq, DeepSeek, Ollama

Output Formats

# JSON
mcpsec scan --stdio "server" --output results.json

# SARIF (CI/CD)
mcpsec fuzz --stdio "server" --output results.sarif

How It Works

┌─────────┐     MCP Protocol      ┌────────────┐
│ mcpsec  │ ◄──── JSON-RPC ────►  │   Target   │
│         │    (stdio / HTTP)     │   Server   │
└────┬────┘                       └────────────┘
     │
     ├── Connect & enumerate attack surface
     ├── Run static scanners
     ├── Generate dynamic payloads  
     ├── Execute fuzzing campaigns
     └── Report findings with evidence

Disclaimer

For authorized security testing only. Only scan servers you own or have permission to test.


License

MIT


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