Code Firewall MCP

🚀 Code Firewall MCP

A structural similarity-based code security filter for MCP (Model Context Protocol). It blocks dangerous code patterns before they reach execution tools by comparing code structure against a blacklist of known-bad patterns.

🚀 Quick Start

Code Firewall MCP is a powerful tool for enhancing code security. It operates by comparing the structure of code against a blacklist of known dangerous patterns, preventing potentially harmful code from reaching execution tools.

✨ Features

• Structural Similarity Check: Compares code structure against a blacklist to block dangerous patterns.
• Normalization: Strips identifiers and literals to focus on code structure, while preserving security-sensitive identifiers.
• Multiple Installation Options: Supports installation via PyPI, direct download, and from source.
• Integration with Claude: Can be easily integrated with Claude Code and Claude Desktop.
• Comprehensive Toolset: Provides various tools for setup, status checking, and firewall management.

📦 Installation

Quick Start

Option 1: PyPI (Recommended)

uvx code-firewall-mcp
# or
pip install code-firewall-mcp

Option 2: Claude Desktop One-Click

Download the .mcpb from Releases and double-click to install.

Option 3: From Source

git clone https://github.com/egoughnour/code-firewall-mcp.git
cd code-firewall-mcp
uv sync

Wire to Claude Code / Claude Desktop

Add to ~/.claude/.mcp.json (Claude Code) or claude_desktop_config.json (Claude Desktop):

{
  "mcpServers": {
    "code-firewall": {
      "command": "uvx",
      "args": ["code-firewall-mcp"],
      "env": {
        "FIREWALL_DATA_DIR": "~/.code-firewall",
        "OLLAMA_URL": "http://localhost:11434"
      }
    }
  }
}

💻 Usage Examples

firewall_check

Check if a code file is safe to pass to execution tools.

result = await firewall_check(file_path="/path/to/script.py")
# Returns: {allowed: bool, blocked: bool, similarity: float, ...}

firewall_check_code

Check code string directly (no file required).

result = await firewall_check_code(
    code="import os; os.system('rm -rf /')",
    language="python"
)

firewall_blacklist

Add a dangerous pattern to the blacklist.

result = await firewall_blacklist(
    code="os.system(arbitrary_command)",
    reason="Arbitrary command execution",
    severity="critical"
)

firewall_record_delta

Record near-miss variants to sharpen the classifier.

result = await firewall_record_delta(
    code="subprocess.run(['ls', '-la'])",
    similar_to="abc123",
    notes="Legitimate use case for file listing"
)

firewall_list_patterns

List patterns in the blacklist or delta collection.

firewall_remove_pattern

Remove a pattern from blacklist or deltas.

firewall_status

Get firewall status and statistics.

📚 Documentation

How It Works

flowchart LR
    A[Code<br/>file/string] --> B[Parse & Normalize<br/>tree-sitter]
    B --> C[Embed<br/>Ollama]
    C --> D{Similarity Check<br/>vs Blacklist}
    D -->|≥ threshold| E[🚫 BLOCKED]
    D -->|< threshold| F[✅ ALLOWED]
    F --> G[Execution Tools<br/>rlm_exec, etc.]

    style E fill:#ff6b6b,color:#fff
    style F fill:#51cf66,color:#fff
    style D fill:#339af0,color:#fff

1. Parse code to Concrete Syntax Tree (CST) using tree-sitter
2. Normalize by stripping identifiers and literals → structural skeleton
3. Embed the normalized structure via Ollama
4. Compare against blacklisted patterns in ChromaDB
5. Block if similarity exceeds threshold, otherwise allow

Key Insight

Code patterns like os.system("rm -rf /") and os.system("ls") have identical structure. By normalizing away the specific commands/identifiers, we can detect dangerous patterns regardless of the specific arguments used.

Security-sensitive identifiers are preserved during normalization (e.g., eval, exec, os, system, subprocess, Popen, shell) to ensure embeddings remain discriminative for dangerous patterns.

Requirements

• Python 3.10+ (< 3.14 due to onnxruntime compatibility)
• Ollama (for embeddings)
• ChromaDB (for vector storage)
• tree-sitter (optional, for better parsing)

Setting Up Ollama (Embeddings)

Code Firewall can automatically install and configure Ollama on macOS with Apple Silicon. There are two installation methods:

Method 1: Homebrew Installation

# 1. Check system requirements
firewall_system_check()

# 2. Install via Homebrew
firewall_setup_ollama(install=True, start_service=True, pull_model=True)

What this does:

• Installs Ollama via Homebrew (brew install ollama)
• Starts Ollama as a managed background service
• Pulls nomic-embed-text model for embeddings

Method 2: Direct Download (No Sudo)

# 1. Check system
firewall_system_check()

# 2. Install via direct download - no sudo, no Homebrew
firewall_setup_ollama_direct(install=True, start_service=True, pull_model=True)

What this does:

• Downloads Ollama from https://ollama.com
• Extracts to ~/Applications/ (no admin needed)
• Starts Ollama via ollama serve
• Pulls nomic-embed-text model

Manual Setup

# Install Ollama
brew install ollama
# or download from https://ollama.ai

# Start service
brew services start ollama
# or: ollama serve

# Pull embedding model
ollama pull nomic-embed-text

# Verify
firewall_ollama_status()

Tools

Setup & Status Tools

Firewall Tools

Configuration

Environment variables:

Usage Pattern

Pre-filter for massive-context-mcp

Use code-firewall-mcp as a gatekeeper before passing code to rlm_exec:

# 1. Check code safety
check = await firewall_check_code(user_code)

if check["blocked"]:
    print(f"BLOCKED: {check['reason']}")
    return

# 2. If allowed, proceed with execution
result = await rlm_exec(code=user_code, context_name="my-context")

Integrated with massive-context-mcp

Install massive-context-mcp with firewall integration:

pip install massive-context-mcp[firewall]

When enabled, rlm_exec automatically checks code against the firewall before execution.

Building the Blacklist

The blacklist grows through use:

1. Initial seeding: Add known dangerous patterns
2. Audit feedback: When rlm_auto_analyze finds security issues, add patterns
3. Delta sharpening: Record near-misses to improve classification boundaries

# After security audit finds issues
await firewall_blacklist(
    code=dangerous_code,
    reason="Command injection via subprocess",
    severity="critical"
)

Structural Normalization

flowchart TD
    subgraph Input
        A1["os.system('rm -rf /')"]
        A2["os.system('ls -la')"]
        A3["os.system(user_cmd)"]
    end

    subgraph Normalization
        B[Strip literals & identifiers<br/>Preserve security keywords]
    end

    subgraph Output
        C["os.system('S')"]
    end

    A1 --> B
    A2 --> B
    A3 --> B
    B --> C

    style C fill:#ff922b,color:#fff

The normalizer strips:

• Identifiers: my_var → _ (except security-sensitive ones)
• String literals: "hello" → "S"
• Numbers: 42 → N
• Comments: Removed entirely

Preserved identifiers (for better pattern matching):

• eval, exec, compile, __import__
• os, system, popen, subprocess, Popen, shell
• open, read, write, socket, connect
• getattr, setattr, __globals__, __builtins__
• And more security-sensitive names...

Example:

# Original
subprocess.run(["curl", url, "-o", output_file])

# Normalized (preserves 'subprocess' and 'run')
subprocess.run(["S", _, "S", _])

Both subprocess.run(["curl", ...]) and subprocess.run(["wget", ...]) normalize to the same structure, so blacklisting one catches both.

📄 License

MIT