code-firewall-mcp 0.6.0

Code similarity firewall - blocks dangerous code patterns before they reach execution tools

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

┌──────────┐    ┌─────────────┐    ┌───────────────────┐    ┌─────────────────────┐
│ Code     │───▶│ CST → Embed │───▶│ Similarity Check  │───▶│ Execution Tools     │
│ (file)   │    │ (tree-sitter│    │ vs blacklist      │    │ (rlm_exec, etc.)    │
└──────────┘    │  + Ollama)  │    │ (ChromaDB)        │    └─────────────────────┘
                └─────────────┘    └─────────┬─────────┘
                                             │
                                    ┌────────┴────────┐
                                    ▼                 ▼
                               [BLOCKED]         [ALLOWED]

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.

Installation

# Via uvx (recommended)
uvx code-firewall-mcp

# Or install from source
pip install -e .

Requirements

• Python 3.10+
• Ollama (for embeddings)
• ChromaDB (for vector storage)
• tree-sitter (optional, for better parsing)

Pull an embedding model:

ollama pull nomic-embed-text

Tools

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")

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

The normalizer strips:

• Identifiers: my_var → _
• String literals: "hello" → "S"
• Numbers: 42 → N
• Comments: Removed entirely

Example:

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

# Normalized
_._(["S", _, "S", _])

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

License

MIT