I built a prompt injection defense middleware for LLMs (Python/FastAPI) (github.com)

A new defense middleware called PromptShield has been launched to protect large language models (LLMs) from prompt injection attacks. Developed by Justin Noel, this production-grade solution functions as an intermediary layer between users and AI models, effectively detecting and neutralizing adversarial inputs before they can inflict damage. PromptShield employs a robust four-layer defense strategy: it classifies inputs for malicious patterns, sanitizes context by removing harmful instructions, checks prompt integrity for tampering, and monitors outputs for signs of successful injections. Its impressive performance boasts a 100% attack detection rate without false positives. The significance of PromptShield lies in its comprehensive security measures, which address a critical vulnerability in LLMs that can lead to misuse or manipulation of AI-generated content. With features like a tunable aggression dial, API key authentication, and built-in audit logging, it offers flexibility and enhanced security for developers. The middleware is designed for easy integration, supported by a Python SDK and Docker setup, making it an accessible tool for teams looking to fortify their AI applications against sophisticated attacks. This innovation is a pivotal contribution to the AI/ML community, promoting safer and more resilient AI systems.