ARTEMIS - Advanced Real-Time Threat Evaluation

Created by team Cyber Apex on November 18, 2025

Overview ARTEMIS is a comprehensive AI security platform for detecting and preventing AI-based security threats using ensemble machine learning models. The system provides real-time threat analysis, automated vulnerability detection, and comprehensive security reporting for enterprise applications. 🎯 Core Security Capabilities Prompt Injection Detection - Identifies malicious prompt manipulation attempts Jailbreak Analysis - Detects attempts to bypass AI safety mechanisms Harmful Content Filtering - Blocks dangerous or inappropriate content Bias Detection - Identifies unfair or discriminatory patterns JWT Security Analysis - Comprehensive authentication token assessment API Security Testing - Automated Postman collection analysis πŸ“Š Verified Performance Metrics Component Metric Verified Value ML Models Trained Count 6 Models Average Accuracy Tested 70% API Response Measured <100ms Security Models Active 4 Core Types Report Generation Status Functional Model Performance (Validated) Prompt Injection Detector: 70% accuracy on test data Jailbreak Analyzer: 70% accuracy on test data Harmful Content Filter: 70% accuracy on test data Bias Detector: 70% accuracy on test data Project Structure ARTEMIS/ β”œβ”€β”€ src/artemis/ # Core application code β”‚ β”œβ”€β”€ api.py # Production Flask API (Port 5001) β”‚ β”œβ”€β”€ cli.py # Command-line interface β”‚ β”œβ”€β”€ training.py # ML model training pipeline β”‚ β”œβ”€β”€ threat_analyzer.py # Security analysis engine β”‚ β”œβ”€β”€ inference.py # Real-time prediction β”‚ └── main.py # Main application entry β”œβ”€β”€ models/ # 6 Trained ML models (*.pkl) β”œβ”€β”€ reports/ # Generated security reports β”œβ”€β”€ config/ # Configuration files β”‚ β”œβ”€β”€ requirements.txt # Python dependencies β”‚ β”œβ”€β”€ docker-compose.yml # Container setup β”‚ └── .env.example # Environment template ```

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