Securing AI from Prompt Injection Attacks

This project introduces a robust detection and mitigation framework that integrates multiple models to safeguard AI systems. Here’s how it works:

Keyword-Based Detection – Identifies known malicious patterns in user inputs.

Semantic Analysis – Detects hidden manipulations that evade keyword-based methods.

Output Evaluation – Ensures generated responses align with expected behavior and flags anomalies like leaked sensitive information.

Ensemble Learning – Combines insights from all models to enhance attack classification and adaptability to emerging threats.

By leveraging this multi-layered defense approach, our framework significantly strengthens AI security, making it more resilient against evolving attack strategies.