The Agentic Security Scanner is an AI-powered security analysis tool that automatically detects vulnerabilities in code repositories. Built with React, TypeScript, and OpenAI's capabilities, it provides comprehensive security scanning with detailed reporting and actionable insights.

Live Demo: https://security.agentics.org/

• Static Code Analysis: Scans code for hardcoded secrets and insecure patterns
• Dependency Scanning: Checks for known vulnerabilities in dependencies
• Configuration Analysis: Validates security settings in config files
• Pattern Matching: Uses vector similarity to find known vulnerability patterns
• Web Search Enhancement: Uses OpenAI's web search to find latest CVEs
• Historical Analysis: Tracks security posture over time
• GitHub Issues Integration: Creates issues for critical findings
• Email Reporting: Sends detailed security reports via email

• React + TypeScript + Vite
• Tailwind CSS for styling
• shadcn/ui component library
• OpenAI for intelligent analysis
• Edge Functions for serverless backend
• Local storage for result persistence

The project follows a structured multi-phase development approach:

The Security Scanner Edge Function (security-scanner) provides the backend scanning capabilities with comprehensive code analysis features:

• Severity Classification: Categorizes findings into critical, high, medium, low, and info levels
• Code Context Analysis: Extracts vulnerable code snippets with file path and line number information
• Detailed Remediation: Provides specific recommendations for fixing each vulnerability
• Reference Links: Includes security reference documentation and standards
• GitHub Integration: Creates issues for critical and high severity findings
• Historical Tracking: Maintains scan history with trend analysis
• Email Reporting: Sends detailed scan reports with customizable content
• Configurable Scanning: Allows customization of scan depth, file types, and focus areas

The Security Scanner edge function is implemented as a Deno-based serverless function with:

• CORS support for cross-origin requests
• Comprehensive error handling and logging
• Mock data generation for development and testing

The security scanner leverages advanced AI capabilities through:

The vector-file edge function enables powerful code analysis through:

• Vector Embeddings: Code snippets and patterns are converted to vector representations for semantic similarity matching
• Storage Management: Creates and manages vector stores with file indexing and chunking strategies
• Hybrid Search: Combines semantic and keyword search for high-precision vulnerability detection
• Context-Aware Analysis: Uses surrounding code context to understand vulnerability patterns

• GPT-4o Integration: Leverages OpenAI's latest models for vulnerability analysis
• Web-Enhanced Security Data: Utilizes GPT-4o-search-preview to find the latest CVEs and security advisories
• Auto-Learning: Saves web search results back into the vector store for future reference
• Multi-Modal Security Analysis: Text, code, and configuration files are analyzed together
• Remediation Generation: Uses AI to generate detailed, context-aware fix recommendations
• Severity Classification: AI-powered determination of vulnerability severity levels

The vector-file edge function provides these key capabilities:

• /create-store: Creates a new vector store for a repository

  POST /vector-file/create-store
  Body: { "name": "repo-name", "expiresAfter": "30d" }
  Returns: { "id": "vs_..." }
  

• /upload-file: Uploads code files for analysis

  POST /vector-file/upload-file
  FormData: file
  Returns: { "id": "file_..." }
  

• /add-file: Adds a file to a vector store with chunking options

  POST /vector-file/add-file
  Body: { "vectorStoreId": "vs_...", "fileId": "file_...", "chunkingStrategy": {...} }
  Returns: { "success": true }
  

• /search: Performs semantic search across codebase

  POST /vector-file/search
  Body: { "vectorStoreId": "vs_...", "query": "insecure password storage", "maxResults": 10 }
  Returns: Vector search results
  

• /query: Enhanced search with web augmentation

  POST /vector-file/query
  Body: { "vectorStoreId": "vs_...", "question": "Are there any SQL injection vulnerabilities?", "webSearch": {"enabled": true} }
  Returns: { "vector_results": [...], "web_results": [...], "answer": "..." }
  

For a detailed walkthrough of how this project was built using Roo Code Power Steering and our process-focused development methodology, see the tutorial.md file.

1. /init-scan: Initialize a vector store for a repository

   POST /init-scan
   Body: { "repo": "owner/repo" }
   Returns: { "vectorStoreId": "vs_..." }
   

2. /scan-repo: Run a full security scan

   POST /scan-repo
   Body: { "repo": "owner/repo", "branch": "main" }
   Returns: ScanResult object
   

3. /scan-results: Get historical scan results

   POST /scan-results
   Body: { "repo": "owner/repo", "limit": 10 }
   Returns: { "results": ScanResult[] }
   

4. /create-issues: Create GitHub issues for findings

   POST /create-issues
   Body: { "repo": "owner/repo", "findings": SecurityFinding[] }
   Returns: { "created": number, "issues": string[] }
   

5. /cron-trigger: Endpoint for GitHub Actions to trigger nightly scans

   POST /cron-trigger
   Body: { "repo": "owner/repo", "branch": "main", "sendReport": true, "recipient": "user@example.com" }
   Returns: { "scanId": "scan_...", "message": "Scan queued successfully" }
   

6. /send-report: Send a security report via email

   POST /send-report
   Body: { "repo": "owner/repo", "recipient": "user@example.com", "includeRecommendations": true }
   Returns: { "success": true, "message": "Report sent successfully" }
   

• API_KEY: Required for API authentication
• AUTH_TOKEN: Token used for GitHub API authorization
• OPENAI_API_KEY: Required for OpenAI API integration (similarity detection and recommendations)
• GITHUB_TOKEN: GitHub API token for repository access and issue creation
• RESEND_API_KEY: API key for the Resend email service

The vulnerabilities/ directory contains a comprehensive collection of sample security issues that can be used for:

• Testing the scanner's detection capabilities
• Demonstrating different severity levels (Critical, High, Medium, Low)
• Showing vulnerability patterns across multiple languages and technologies
• Training and educational purposes

The collection includes examples of:

• SQL injection in Python and PHP
• Cross-site scripting (XSS) in React/JSX
• Hardcoded credentials in JavaScript
• Command injection in Ruby
• Insecure Docker configurations
• Path traversal vulnerabilities
• And many more security issues that security scanners should detect

This project was built using a multi-phase development approach with Roo Code Power Steering to optimize development costs and efficiency:

Instead of creating a single monolithic design document, the project is structured into phases:

• Guidance.md: Defines coding standards, naming conventions, and best practices
• Phase1.md, Phase2.md, Phase3.md: Breaks development into incremental, test-driven phases
• Tests.md: Specifies unit and integration tests to validate each phase
• Implementation.md: Tracks progress as features are completed

The project uses Gemini 2.0 Pro with Roo Code's Power Steering for efficient development:

• Cost Optimization: Reduces token costs by 98.75% for input tokens and 99% for output tokens compared to other AI assistants
• Scalable Context: Leverages Gemini Pro's 1M token context window (5x larger than alternatives)
• Test-Driven Development: Each function is completed and tested before moving to the next
• Implementation Tracking: Updates Implementation.md after each successful step
• Environment Variable Protection: Ensures environment variables are never hardcoded

1. Clone the repository:

git clone <repository-url>
cd agentic-security-scanner

2. Install dependencies:

npm install

3. Start the development server:

npm run dev

4. Visit http://localhost:8080 to see the application

Run the test suite with:

npm test

For integration tests with the edge function:

npm run test:integration

The application can be deployed to any static hosting provider:

npm run build

Then deploy the contents of the dist directory.

This project is licensed under the MIT License - see the LICENSE file for details.

• Supported by the Agentics Foundation
• Powered by OpenAI and Gemini 2.0 Pro
• Built with Roo Code Power Steering methodology