Key Features
Key Features
SigilAI offers a comprehensive set of security scanning capabilities through its Model Context Protocol (MCP) server. This document outlines the key features and capabilities that make SigilAI an essential security tool for AI systems and developers.
URL Security Scanning
SigilAI's URL scanning tool provides in-depth analysis of web addresses to identify and mitigate potential threats.
Core Capabilities
Malicious URL Detection: Identify potential phishing attempts, malware distribution sites, and other harmful web resources
Domain Analysis: Extract and analyze domain information to assess legitimacy and trustworthiness
Blacklist Checking: Check domains against multiple security blacklists
Warning Generation: Produce clear warnings for potentially risky URLs
GitHub Link Analysis: Special handling for GitHub links that might be used in anonymized URL attacks
MD5 Hashing: Generate unique identifiers for scanned domains for reference and tracking
Implementation Highlights
The URL scanning engine employs a modular approach using the UrlScannerService that:
Extracts domains from submitted URLs
Creates a unique hash identifier for each domain
Employs multiple scanning techniques in parallel
Consolidates findings into a comprehensive safety assessment
Provides structured, actionable results
Source Code Security Analysis
SigilAI's file scanning capabilities allow for deep inspection of source code to identify vulnerabilities, potential exploits, and optimization opportunities.
Core Capabilities
Vulnerability Detection: Identify common security vulnerabilities in JavaScript and TypeScript code
Multiple Analysis Techniques: Employs a variety of advanced analysis techniques, including:
Static Application Security Testing (SAST): For pattern-based semantic code analysis to identify vulnerabilities within the code structure.
Software Composition Analysis (SCA): For detecting known vulnerabilities in third-party libraries and dependencies.
Proprietary & ML-driven Analysis: Utilizes SigilAI's unique proprietary algorithms and machine learning models to uncover complex vulnerabilities and provide deeper security insights.
Performance Optimization: Identify inefficient code patterns and suggest improvements
Best Practices Enforcement: Flag deviations from security best practices
Implementation Highlights
flowchart TD
A[Source Code Input] --> B[File Processing]
B --> C[Analysis Profile Selection]
C --> D["Static Code Analysis (SAST)"]
C --> E["Dependency Scanning (SCA)"]
C --> ML["Proprietary & ML Analysis"]
D --> F[Results Consolidation]
E --> F
ML --> F
F --> G[Formatted Findings]The file scanning system:
Processes source files with path and content information
Supports configurable analysis profiles (e.g., focusing on static analysis, dependency checks, or a comprehensive scan)
Handles file content efficiently
Manages large file transfers with maximum body length settings
Returns structured JSON results for easy interpretation
MCP Server Integration
SigilAI implements the Model Context Protocol to make its security scanning capabilities available to Large Language Models (LLMs) and AI assistants.
Core Capabilities
Standardized Tool Interface: Well-defined tool specifications with clear parameter definitions
Rich Parameter Validation: Zod schema validation ensures proper inputs
Comprehensive Documentation: Built-in instructions and tool descriptions
Event-Driven Architecture: Connection and disconnection events for session management
SSE Transport Support: Server-Sent Events transport for efficient communication
Implementation Highlights
The SigilAI MCP server:
Provides a consistent interface for all security scanning tools
Includes detailed descriptions and instructions for each tool
Implements robust error handling and logging
Supports real-time connection management
Uses standard transport protocols for wide compatibility
Testing & Diagnostics
SigilAI includes comprehensive testing capabilities to ensure system reliability and provide diagnostic information.
Core Capabilities
Test Tool: Simple verification mechanism to confirm server functionality
Structured Responses: Consistent JSON formatting with status, message, and timestamp
Logging: Detailed logging for system operations and troubleshooting
Error Handling: Comprehensive error management with informative messages
Security & Performance Features
Security
Input Validation: Strict parameter validation using Zod schemas
Error Isolation: Contained error handling to prevent system disruption
Logging Controls: Careful management of sensitive information in logs
Performance
Efficient Processing: Optimized handling of scanning requests
Parallelized Scanning: Multiple scanning engines can run concurrently
Scalable Architecture: Design supports growth in scanning capabilities
Future Roadmap
SigilAI is continuously evolving with planned features including:
Expanded File Type Support: Additional language and file format scanning
Enhanced AI Integration: Deeper integration with AI systems for predictive security
Advanced Visualization: Improved result formatting and visualization
Entire Codebase Scanning: Support for analyzing complete repositories
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