Why mcpsec?

MCP (Model Context Protocol) connects AI agents to external tools. Claude Desktop, Cursor, VS Code Copilot, and every major AI IDE uses it. Security is often an afterthought.

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

Real Bugs Found

More findings under responsible disclosure.

Installation

pip install mcpsec

For AI-powered features:

pip install mcpsec[ai]

Nix

nix-shell   # basic
nix-shell --arg withAll true   # all optional deps

Quick Start

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"

# Auto-discover and scan all local servers
mcpsec scan --auto

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

Static Analysis (Audit v3)

# Local source - pattern-based + AI reachability
mcpsec audit --path ./my-mcp-server

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

# With LLM-powered taint analysis
mcpsec audit --github https://github.com/user/mcp-server --ai

# Known vulnerable servers
mcpsec audit --github https://github.com/radareorg/radare2-mcp

Protocol Fuzzing

# Standard fuzzing (~200 cases)
mcpsec fuzz --stdio "python my_server.py"

# High intensity (~800 cases)
mcpsec fuzz --stdio "python my_server.py" --intensity high

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

Advanced

# SQL Injection scanner with DB fingerprinting
mcpsec sql --stdio "npx @benborla29/mcp-server-mysql" --fingerprint

# Dangerous tool chain detection
mcpsec chains --stdio "npx @example/complex-server"

# Interactive exploitation REPL
mcpsec exploit --stdio "npx vulnerable-server"

# Rogue server for client-side testing
mcpsec rogue-server --port 9999 --attack all

Static Analysis - Audit v3

New in v2.7.1 - Complete rewrite of the audit engine with a pattern-based architecture.

7-Stage Analysis Pipeline

Source Code
    │
    ├─ 1. Fetch        - Clone GitHub repo or load local path
    ├─ 2. Detect       - Identify language, MCP SDK, and framework
    ├─ 3. Sink Scan    - 3,450+ regex patterns across 12 languages
    ├─ 4. Semgrep      - 149 semantic rules (AST-level)
    ├─ 5. AST          - Python/JS taint flow analysis
    ├─ 6. Reachability - LLM taint tracing (heuristic fallback)
    └─ 7. Deduplicate  - Merge, rank, and report findings

Pattern Database - 3,450+ Sink Patterns

Framework Detection

Automatically identifies:

• MCP SDKs: @modelcontextprotocol/sdk, mcp (Python), mcp-go, rmcp (Rust), mcpx (C#)
• Languages: TypeScript, JavaScript, Python, Go, Rust, Java, C#, PHP, Ruby, C/C++
• Frameworks: Express, FastAPI, Django, Gin, Axum, Spring Boot, ASP.NET

Heuristic Fallback

When no LLM is configured, the reachability analyzer uses confidence scoring to report findings without false silence - high-confidence patterns (CRITICAL/HIGH + direct taint) are always reported.

Scanners

Fuzz Generators

22 generators organized by intensity level:

Low (~65 cases): malformed_json, protocol_violation, type_confusion, boundary_testing, unicode_attacks

Medium (~200 cases): + session_attacks, encoding_attacks, integer_boundaries

High (~800 cases): + injection_payloads, method_mutations, param_mutations, timing_attacks, header_mutations, json_edge_cases, protocol_state, id_confusion, concurrency_attacks, regex_dos, deserialization

Insane (~1500+ cases): + resource_exhaustion, memory_exhaustion

How It Works

┌─────────┐     MCP Protocol      ┌────────────┐
│ mcpsec  │ ◄──── JSON-RPC ────►  │   Target   │
│         │    (stdio / HTTP)     │   Server   │
└────┬────┘                       └────────────┘
     │
     ├── Connect & enumerate attack surface
     ├── Run 10+ security scanners
     ├── Generate 800+ fuzz cases
     ├── Execute AI-powered payload mutations
     ├── Static audit: 3,450+ sink patterns + 149 Semgrep rules
     └── Report findings with PoC evidence

Configuration

AI Provider Setup

mcpsec setup

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

Output Formats

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

# SARIF 2.1.0 (GitHub/GitLab/Azure DevOps CI/CD)
mcpsec fuzz --stdio "server" --output results.sarif

See CHANGELOG.md for the full release history.

Contributing

See CONTRIBUTING.md for guidelines.

CI runs automatically on all PRs - linting with Ruff and cross-platform tests (Ubuntu, Windows, macOS).

Disclaimer

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

License

MIT