LLM Safety Alignment

Large language model safety alignment studies how to make models follow intended rules, avoid enabling harm, and remain useful when requests are ambiguous or adversarial. Common approaches include supervised safety fine-tuning and preference optimization such as reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO), adversarial red-teaming, and inference-time controls such as refusal steering, prompt routing, or guard models. Evaluations use jailbreak and harmful-request suites such as HarmBench and JailbreakBench, alongside tests of over-refusal, multilingual and multimodal inputs, fine-tuning drift, and tool-using agents. A central challenge is achieving robust, context-sensitive safety without blocking benign help: refusals can fail under new formats, languages, or deployment pressures, while stronger safeguards can reduce utility and do not always predict real agent behavior.