NeuroShield: A Modular Immunological Architecture for Emotional LLM Systems

As large language models (LLMs) evolve into emotionally responsive agents and distributedsystems, traditional safety mechanisms prove insufficient. We propose NeuroShield, a biolog-ically inspired, modular defense framework that mimics immune strategies to detect, isolate,and adaptively respond to cognitive anomalies. NeuroShield integrates five primary modules:AntigenScanner, MemorySentinel, QuarantineNet, MetaDefender, and AdaptiveThresholds.Together, they form a dynamic immune layer for both standalone and multi-agent LLM systems,offering real-time anomaly detection, quarantine protocols, semantic defense against prompt injection, and adaptive sensitivity modulation based on emotional stress. This approach introduces a scalable path toward embodied AI self-regulation and lays a foundation for emotional stability and memory integrity.