Self-Healing AI Security via Autonomous Patch Generation & LLM Autovaccination

The increasing reliance on Large Language Models (LLMs) in critical applications has introduced new security challenges, including adversarial attacks, prompt injections, model inversion, and data poisoning. Existing security approaches primarily focus on detection and static defenses, which fail to provide real-time adaptation against emerging zero-day threats. This project aims to develop a self-healing AI security framework that autonomously detects, neutralizes, and preemptively defends LLMs against evolving adversarial and data integrity threats without human intervention.