Code Firewall MCP

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

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

Parse code to Concrete Syntax Tree (CST) using tree-sitter
Normalize by stripping identifiers and literals → structural skeleton
Embed the normalized structure via Ollama
Compare against blacklisted patterns in ChromaDB
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.

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"
      }
    }
  }
}

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

Tool	Purpose
`firewall_system_check`	Check system requirements — verify macOS, Apple Silicon, RAM
`firewall_setup_ollama`	Install via Homebrew — managed service, auto-updates
`firewall_setup_ollama_direct`	Install via direct download — no sudo, fully headless
`firewall_ollama_status`	Check Ollama availability — verify embeddings are ready

Firewall Tools

Tool	Purpose
`firewall_check`	Check if a code file is safe to execute
`firewall_check_code`	Check code string directly (no file required)
`firewall_blacklist`	Add a dangerous pattern to the blacklist
`firewall_record_delta`	Record near-miss variants for classifier sharpening
`firewall_list_patterns`	List patterns in blacklist or delta collection
`firewall_remove_pattern`	Remove a pattern from blacklist or deltas
`firewall_status`	Get firewall status and statistics

`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.

Configuration

Environment variables:

Variable	Default	Description
`FIREWALL_DATA_DIR`	`/tmp/code-firewall`	Data storage directory
`OLLAMA_URL`	`http://localhost:11434`	Ollama server URL
`EMBEDDING_MODEL`	`nomic-embed-text`	Ollama embedding model
`SIMILARITY_THRESHOLD`	`0.85`	Block threshold (0-1)
`NEAR_MISS_THRESHOLD`	`0.70`	Near-miss recording threshold

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:

Initial seeding: Add known dangerous patterns
Audit feedback: When rlm_auto_analyze finds security issues, add patterns
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

Code Firewall MCP

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

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

Key Insight

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"
      }
    }
  }
}

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

Tool	Purpose
`firewall_system_check`	Check system requirements — verify macOS, Apple Silicon, RAM
`firewall_setup_ollama`	Install via Homebrew — managed service, auto-updates
`firewall_setup_ollama_direct`	Install via direct download — no sudo, fully headless
`firewall_ollama_status`	Check Ollama availability — verify embeddings are ready

Firewall Tools

Tool	Purpose
`firewall_check`	Check if a code file is safe to execute
`firewall_check_code`	Check code string directly (no file required)
`firewall_blacklist`	Add a dangerous pattern to the blacklist
`firewall_record_delta`	Record near-miss variants for classifier sharpening
`firewall_list_patterns`	List patterns in blacklist or delta collection
`firewall_remove_pattern`	Remove a pattern from blacklist or deltas
`firewall_status`	Get firewall status and statistics

`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.

Configuration

Environment variables:

Variable	Default	Description
`FIREWALL_DATA_DIR`	`/tmp/code-firewall`	Data storage directory
`OLLAMA_URL`	`http://localhost:11434`	Ollama server URL
`EMBEDDING_MODEL`	`nomic-embed-text`	Ollama embedding model
`SIMILARITY_THRESHOLD`	`0.85`	Block threshold (0-1)
`NEAR_MISS_THRESHOLD`	`0.70`	Near-miss recording threshold

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:

Initial seeding: Add known dangerous patterns
Audit feedback: When rlm_auto_analyze finds security issues, add patterns
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