mighty-security CLAUDE.md

Project instructions for mighty-security, a defensive security toolkit for analyzing Model Context Protocol servers. MCP is a standard way for AI assistants to connect to tools and data.

In plain words
What is it for?
Use it when setting up or modifying the project, running its security checks, installing optional dependencies, or following its Python 3.13+ requirements.
Why use it?
It gives coding agents the project’s setup requirements, main commands, and operating context so they can work consistently.

Instructions file

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/trymightyai/mighty-security/claude-md
Clone the repo
git clone --depth 1 https://github.com/TryMightyAI/mighty-security
Per session 2,216 This file is loaded in full into every session.
When invoked 2,216 The same file — it is already loaded in full.
Security scan B 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.02216 $0.02216
Opus 5 $0.01108 $0.01108
Sonnet 5 $0.00443 $0.00443
Haiku 4.5 $0.00222 $0.00222

Measured 3d ago against content hash 1836300d6c26, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

mighty-security CLAUDE.md scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Recursive force deletemediumDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

- NEVER NEVER EVER delete files with `rm` or `rm -rf` without permissions.

Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.

CLAUDE.md · 271 lines

How it starts

The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

MCP Security Suite - A unified security framework for Model Context Protocol (MCP) servers. This is a defensive security tooling project for analyzing and detecting vulnerabilities in MCP implementations.

Main entry point: mighty_mcp.py - ALL functionality goes through this single CLI.

Essential Commands

Setup & Installation

# Python 3.13+ required
# Using uv (recommended)
uv sync -p 3.13

# Alternative: pip with venv
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

# Optional ML features (for semantic analysis)
pip install transformers torch sentence-transformers scikit-learn networkx gitpython

# Optional LLM features (Cerebras GPT-OSS-120B)
pip install cerebras-cloud-sdk
echo "CEREBRAS_API_KEY=your_api_key" > .env

Core Analysis Commands

# IMPORTANT: Always activate virtual environment first
source .venv/bin/activate  # On macOS/Linux
# or
.venv\Scripts\activate     # On Windows

# Main entry point - comprehensive analysis
python3 mighty_mcp.py check [target]

# Alternative direct analyzer
python3 src/analyzers/comprehensive_mcp_analyzer.py [target]

# Analyze GitHub repository
python3 src/analyzers/comprehensive_mcp_analyzer.py https://github.com/example/mcp-tool

# Analyze with LLM enhancement
python3 src/analyzers/comprehensive_mcp_analyzer.py [target] --llm

# Start web dashboard
python3 mighty_mcp.py web

Testing

# IMPORTANT: Always activate virtual environment first
source .venv/bin/activate

# Run comprehensive test suite
python3 tests/comprehensive_test_suite.py

# Test context-aware filtering
python3 tests/test_context_filtering.py

# Test integration with context-aware scanning
python3 tests/test_integration_context_aware.py

# Create test cases
python3 tests/create_test_cases.py

# Test ML integration (requires ML deps)
python3 tests/demo_ml_integration.py

# Test real MCP servers
python3 tests/test_real_mcp_servers.py --test-all

# CRITICAL: Test attack vector categorization accuracy
python3 tests/test_categorization_fix.py

# CRITICAL: Test fingerprint integrity (security-critical)
python3 tests/test_fingerprint_integrity.py

# CRITICAL: Test parallel processing functionality
python3 tests/test_parallel_processing.py

# CRITICAL: Test security is not compromised by optimizations
python3 tests/test_security_not_compromised.py

# Performance benchmarking
python3 tests/benchmark_performance.py

# FAST DEVELOPMENT TESTING (recommended for dev)
bash tests/run_dev_tests.sh  # Runs core tests in ~1 second

# Git hooks setup (ensures tests pass before commit/push)
git config core.hooksPath .githooks  # Enable commit hooks

Read the full file on GitHub · 271 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 271 lines · 2,216 tokens per session scan B 1836300d6c26

Subscribe to this mod's changes

mighty-security CLAUDE.md is an instructions file published in the GitHub repository TryMightyAI/mighty-security (97 stars, last pushed 1y ago), licensed MIT. It adds 2,216 tokens to every session, about $0.0111 per session on Opus 5. A static security scan graded it B with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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