Security alignment refers to the process of training, fine-tuning, and evaluating artificial intelligence models to ensure they adhere to safety constraints and resist adversarial manipulation. In the context of large language models, it encompasses techniques such as reinforcement learning from human feedback, safety-focused dataset curation, and red-teaming designed to establish robust behavioral guardrails. This alignment aims to prevent models from generating harmful, dangerous, or unauthorized content, while maintaining operational reliability and resilience against malicious exploitation such as jailbreak attempts and adversarial prompt attacks.
