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Adversary MCP Server

by brettbergin
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Python
MIT License
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Adversary MCP Server

PyPI version Python 3.10+ License: MIT Tests Coverage Version

🔒 Clean Architecture security analysis with AI-powered vulnerability detection and validation

We think about your vulns so you don't have to.

Installation • Quick Start • Claude Code Setup • Cursor Setup • CLI Usage • MCP Tools • Architecture

Features

  • AI-Powered Analysis - OpenAI/Anthropic LLM integration for intelligent vulnerability detection

  • Smart Validation - Reduces false positives with LLM validation (70% confidence threshold)

  • Multi-Engine Scanning - Combines Semgrep static analysis & AI analysis

  • Automatic Persistence - Auto-saves scan results in JSON, Markdown, and CSV formats

  • MCP Integration - Native support for Claude Code and Cursor IDE

  • Comprehensive CLI - Full command-line interface with all scanning capabilities

  • Rich Telemetry - Comprehensive tracking with dashboard visualization

Installation

Prerequisites

  • Python 3.10+ (tested on 3.11, 3.12, 3.13)

  • Semgrep - Static analysis engine (install)

Quick Install

# Install python uv brew install uv # Install Semgrep (required) brew install semgrep # macOS # or pip install semgrep # Other platforms # Install Adversary MCP Server uv pip install adversary-mcp-server

Verify Installation

adv --version adv status

Quick Start

1. Configure Security Engine

# Initial setup (interactive) adv configure setup # Or configure directly with options adv configure --llm-provider openai --llm-api-key $OPENAI_API_KEY adv configure --llm-provider anthropic --llm-api-key $ANTHROPIC_API_KEY # Check configuration status adv status

2. Run Your First Scan

# Scan a single file (basic) adv scan-file path/to/file.py # Scan with AI analysis and validation (recommended) adv scan-file path/to/file.py --use-llm --use-validation # Scan entire directory adv scan-folder ./src --use-llm --use-validation # Scan code snippet directly adv scan-code "print('Hello World')" --language python

3. View Comprehensive Dashboard

# Launch interactive telemetry dashboard adv dashboard

Claude Code Setup

Configure MCP Server

Create or update ~/.config/claude-code/mcp.json:

{ "mcpServers": { "adversary": { "command": "uvx", "args": ["adversary-mcp-server"] } } }

Using MCP Tools in Claude Code

Once configured, these tools are available in Claude Code:

  • Ask Claude: "Scan this file for security issues using adv_scan_file"

  • Ask Claude: "Check for vulnerabilities in the current project with adv_scan_folder"

  • Ask Claude: "Analyze this code snippet for security issues using adv_scan_code"

Cursor IDE Setup

Configure MCP Server

Create .cursor/mcp.json in your project:

{ "mcpServers": { "adversary": { "command": "uvx", "args": ["adversary-mcp-server"] } } }

Using pip installation:

{ "mcpServers": { "adversary": { "command": "python", "args": ["-m", "adversary_mcp_server.sync_main"] } } }

For development:

{ "mcpServers": { "adversary": { "command": "/path/to/.venv/bin/python", "args": ["-m", "adversary_mcp_server.sync_main"] } } }

Using MCP Tools in Cursor

Once configured, these tools are available in Cursor's chat:

  • Ask Cursor: "Scan this file for security issues using adv_scan_file"

  • Ask Cursor: "Check for vulnerabilities in the current project with adv_scan_folder"

  • Ask Cursor: "Analyze this code snippet for security issues using adv_scan_code"

CLI Usage

Basic Commands

# Configure the scanner adv configure setup # Check status and configuration adv status # Scan individual files adv scan-file <file-path> [options] # Scan directories adv scan-folder <directory-path> [options] # Scan code snippets adv scan-code <code-content> --language <lang> [options] # Launch comprehensive telemetry dashboard adv dashboard

Scanning Examples

# Basic file scan adv scan-file app.py # Scan with AI analysis and validation (recommended) adv scan-file app.py --use-llm --use-validation # Directory scan with full analysis adv scan-folder ./src --use-llm --use-validation # Code snippet scan adv scan-code "SELECT * FROM users WHERE id = ?" --language sql # Scan with specific severity threshold adv scan-file app.py --severity high # Output results in different formats adv scan-file app.py --output-format json --output-file results.json adv scan-file app.py --output-format markdown --verbose

Configuration Commands

# Interactive setup adv configure setup # Direct configuration adv configure --llm-provider openai --llm-api-key your-key adv configure --llm-provider anthropic --llm-api-key your-key # Reset configuration adv configure reset # Check current configuration adv status

Available Options

--use-llm / --no-llm # Enable/disable AI analysis --use-validation / --no-validation # Enable/disable false positive filtering --use-semgrep / --no-semgrep # Enable/disable Semgrep analysis (default: true) --severity [low|medium|high|critical] # Minimum severity threshold --output-format [json|markdown|csv] # Output format for results --output-file <file> # Save results to specific file --verbose # Verbose output with detailed information

MCP Tools

Available Tools

Tool

Description

Example Usage

adv_scan_code

Scan code snippets directly

"Scan this code for vulnerabilities"

adv_scan_file

Scan specific files with full analysis

"Check security issues in auth.py"

adv_scan_folder

Scan entire directories recursively

"Analyze the src folder for vulnerabilities"

adv_get_status

Check server status and capabilities

"Is the security scanner configured?"

adv_get_version

Get server version information

"What version is running?"

adv_mark_false_positive

Mark findings as false positives

"Mark finding XYZ as false positive"

adv_unmark_false_positive

Remove false positive marking

"Unmark finding ABC as false positive"

MCP Tool Examples

// In Claude Code or Cursor, ask the AI assistant: // Scan current file with full analysis "Use adv_scan_file to check this file for security issues with LLM validation" // Scan directory with specific options "Run adv_scan_folder on the src directory with severity threshold of high" // Scan code snippet "Use adv_scan_code to analyze this SQL query for injection vulnerabilities" // Check scanner status "Use adv_get_status to see what scan engines are available"

Automatic Result Persistence

All MCP tools automatically save scan results in multiple formats:

  • JSON: .adversary.json - Machine-readable results with full metadata

  • Markdown: .adversary.md - Human-readable report with remediation guidance

  • CSV: .adversary.csv - Spreadsheet-compatible format for analysis

Results are automatically placed alongside scanned files/directories with intelligent conflict resolution.

Dashboard & Telemetry

Comprehensive HTML Dashboard

The scanner includes a rich web-based dashboard for comprehensive telemetry analysis:

# Launch interactive dashboard adv dashboard

Dashboard Features:

  • MCP Tool Analytics - Track tool usage, success rates, and performance

  • Scan Engine Metrics - Monitor Semgrep, LLM, and validation performance

  • Threat Analysis - Categorize findings by severity and confidence

  • System Health - Performance monitoring and statistics

  • Language Analysis - Track scanning efficiency by programming language

  • Recent Activity - Timeline view of recent scans and operations

Telemetry System

Adversary MCP Server includes comprehensive telemetry tracking:

  • Automatic Collection - All MCP tools, CLI commands, and scan operations are automatically tracked

  • Local Storage - All data stored locally, never transmitted to external services

  • Zero Configuration - Telemetry works out-of-the-box with no setup required

  • Performance Insights - Identify bottlenecks and optimize scanning workflows

  • Usage Analytics - Understand tool usage patterns and effectiveness

Architecture

Implementation

Adversary MCP Server is built using Clean Architecture principles with Domain-Driven Design (DDD), ensuring separation of concerns, maintainability, and testability.

graph TB subgraph "🖥️ **Presentation Layer**" A[Cursor IDE] B[CLI Interface] C[Web Dashboard] end subgraph "🔧 **Application Layer**" D[MCP Server] E[CLI Commands] F[Adapters] subgraph "Adapters" F1[SemgrepAdapter] F2[LLMAdapter] F3[ValidationAdapter] end end subgraph "🏛️ **Domain Layer (Business Logic)**" subgraph "Entities" G[ScanRequest] H[ScanResult] I[ThreatMatch] end subgraph "Value Objects" J[ScanContext] K[SeverityLevel] L[ConfidenceScore] M[FilePath] end subgraph "Domain Services" N[ScanOrchestrator] O[ThreatAggregator] P[ValidationService] end subgraph "Interfaces" Q[IScanStrategy] R[IValidationStrategy] end end subgraph "⚙️ **Infrastructure Layer**" S[SemgrepScanner] T[LLMScanner] U[LLMValidator] V[SQLAlchemy Database] W[File System] X[Git Operations] Y[Telemetry System] end A -->|MCP Protocol| D B --> E C --> Y D --> F E --> F F1 --> N F2 --> N F3 --> P N --> O N --> P G --> N H --> O I --> P J --> G K --> I L --> I M --> G N --> Q P --> R F1 -.-> S F2 -.-> T F3 -.-> U S --> W T --> W U --> V Y --> V X --> W style N fill:#e1f5fe,stroke:#0277bd,stroke-width:3px style O fill:#e1f5fe,stroke:#0277bd,stroke-width:3px style P fill:#e1f5fe,stroke:#0277bd,stroke-width:3px style G fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px style H fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px style I fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px style F1 fill:#e8f5e8,stroke:#388e3c,stroke-width:2px style F2 fill:#e8f5e8,stroke:#388e3c,stroke-width:2px style F3 fill:#e8f5e8,stroke:#388e3c,stroke-width:2px

New Architecture Benefits

  1. Separation of Concern: Business logic isolated from infrastructure

  2. Dependency Inversion: High-level modules don't depend on low-level details

  3. Testability: Pure domain logic enables comprehensive unit testing

  4. Maintainability: Changes to infrastructure don't affect business rules

  5. Scalability: New scan strategies and validators easily pluggable

  6. Type Safety: Rich domain models with comprehensive validation

Architectural Layers

Domain Layer (Core Business Logic)

  • Entities: ScanRequest, ScanResult, ThreatMatch - Rich business objects

  • Value Objects: ScanContext, SeverityLevel, ConfidenceScore, FilePath - Immutable domain concepts

  • Domain Services: ScanOrchestrator, ThreatAggregator, ValidationService - Pure business orchestration

  • Interfaces: IScanStrategy, IValidationStrategy - Contracts for external dependencies

Application Layer (Use Cases & Coordination)

  • MCP Server: Handles Cursor IDE integration via Model Context Protocol

  • CLI Commands: Command-line interface for security scanning operations

  • Adapters: Bridge domain interfaces with infrastructure implementations

    • SemgrepAdapter - Adapts Semgrep scanner to domain IScanStrategy

    • LLMAdapter - Adapts LLM scanner to domain IScanStrategy

    • ValidationAdapter - Adapts LLM validator to domain IValidationStrategy

Infrastructure Layer (External Services)

  • SemgrepScanner: Static analysis engine integration

  • LLMScanner: AI-powered vulnerability detection

  • LLMValidator: False positive filtering with LLM analysis

  • SQLAlchemy Database: Persistent storage for telemetry and results

  • File System: Code file access and Git operations

  • Telemetry System: Performance tracking and dashboard generation

Data Flow Architecture

  1. Input Processing: ScanRequest created with ScanContext (file/directory/code)

  2. Domain Orchestration: ScanOrchestrator coordinates scanning strategies

  3. Parallel Analysis: Multiple IScanStrategy implementations execute concurrently

  4. Threat Aggregation: ThreatAggregator deduplicates and merges findings

  5. Validation Pipeline: ValidationService filters false positives using AI

  6. Result Assembly: Rich ScanResult with comprehensive metadata

  7. Presentation: Results formatted for CLI, MCP, or dashboard consumption

Key Design Patterns

  • Strategy Pattern: Pluggable scan and validation strategies

  • Adapter Pattern: Infrastructure integration without domain coupling

  • Factory Pattern: Bootstrap and dependency injection

  • Value Objects: Immutable domain concepts with validation

  • Domain Services: Complex business logic coordination

How It Works

  1. Multi-Engine Analysis: Parallel execution of Semgrep static analysis and LLM AI analysis

  2. Intelligent Validation: LLM-powered false positive reduction with confidence scoring

  3. Threat Aggregation: Smart deduplication and merging using fingerprint and proximity strategies

  4. Performance Optimization: Async processing, caching, and batch operations

  5. Comprehensive Telemetry: SQLAlchemy-backed metrics with interactive Chart.js dashboard

  6. Git Integration: Diff-aware scanning for efficient CI/CD pipeline integration

  7. Zero-Config Operation: Auto-discovery and configuration with sensible defaults

Configuration

Environment Variables

# Core settings (optional) ADVERSARY_LOG_LEVEL=INFO # Set logging level ADVERSARY_WORKSPACE_ROOT=/path # Override workspace detection

Configuration File

Settings are automatically managed through the CLI and stored in ~/.adversary/config.json:

# Interactive configuration adv configure setup # Direct configuration adv configure --llm-provider openai --llm-api-key your-key # Check current settings adv status

CI/CD Integration

GitHub Actions

name: Security Scan on: [pull_request] jobs: security: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - name: Install dependencies run: | pip install adversary-mcp-server - name: Run security scan run: | adv scan-directory . \ --use-llm \ --use-validation \ --severity medium \ --output-format json \ --output-file scan-results.json - name: Upload results uses: actions/upload-artifact@v3 with: name: security-scan path: scan-results.json

Development

Setup Development Environment

# Clone repository git clone https://github.com/brettbergin/adversary-mcp-server.git cd adversary-mcp-server # Create virtual environment (using uv or standard venv) source .venv/bin/activate # Activate existing venv # Install in development mode uv pip install -e .[dev] # Run tests make test

Running Tests

# Full test suite with coverage make test # Specific test categories make test-unit # Unit tests only make test-integration # Integration tests only make test-security # Security tests only # Code quality checks make format # Auto-format code make mypy # Type checking make lint # Run all linting make pre-commit # Run same pre-commit in git commits.

Support

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please see our Contributing Guide for details.

Deploy Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

View all tools

A security-focused server that integrates with Cursor IDE to provide real-time vulnerability detection, exploit generation, and security insights during software development.

  1. Features
    1. Installation
      1. Prerequisites
      2. Quick Install
      3. Verify Installation
    2. Quick Start
      1. 1. Configure Security Engine
      2. 2. Run Your First Scan
      3. 3. View Comprehensive Dashboard
    3. Claude Code Setup
      1. Configure MCP Server
      2. Using MCP Tools in Claude Code
    4. Cursor IDE Setup
      1. Configure MCP Server
      2. Using MCP Tools in Cursor
    5. CLI Usage
      1. Basic Commands
      2. Scanning Examples
      3. Configuration Commands
      4. Available Options
    6. MCP Tools
      1. Available Tools
      2. MCP Tool Examples
      3. Automatic Result Persistence
    7. Dashboard & Telemetry
      1. Comprehensive HTML Dashboard
      2. Telemetry System
    8. Architecture
      1. Implementation
      2. New Architecture Benefits
      3. Architectural Layers
      4. Data Flow Architecture
      5. Key Design Patterns
      6. How It Works
    9. Configuration
      1. Environment Variables
      2. Configuration File
    10. CI/CD Integration
      1. GitHub Actions
    11. Development
      1. Setup Development Environment
      2. Running Tests
    12. Support
      1. License
        1. Contributing

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