Keywords
AI Security, Prompt Injection, cybersecurity

Technologies
Python, XGBoost, Transformers, Scikit-learn

Large language models have fundamentally changed how we build software. They can reason, search for information, and interact with tools using plain English. But that same flexibility creates a serious security problem. Attackers can slip malicious instructions into a model's input, tricking it into leaking sensitive data or behaving in unintended ways. Standard filters weren't built for this kind of threat because natural language is too flexible to be caught by simple rules.

This project tackles that problem with a layered detection system working together. Obvious attacks are caught early by fast, rule-based filters. Subtler attacks are flagged by a machine-learning classifier trained to detect suspicious patterns. Anything else is escalated to a final LLM-based layer that reasons about intent more carefully.

The result is a system that's both accurate and efficient, catching more attacks with less unnecessary blocking, fast enough for real applications. Its strength comes from combining complementary strategies into one cohesive pipeline, giving developers a practical foundation for building AI-powered tools that are secure against increasingly common adversarial threats.