LLMs are increasingly deployed as interactive agents capable of multi-turn dialogues and tool usage, but they remain vulnerable to adaptive attacks. Existing safety mechanisms are mostly reactive, only attempting to uncover pre-existing hidden malicious intent, which makes them largely ineffective against attacks when such attacks are inherently designed to conceal their intent, and causes them to often fail in multi-turn, interactive, or out-of-distribution scenarios. In addition, existing multi-turn defences are largely data-driven, which limits their generalisation ability. To address this challenge, we propose Proactive Safety Alignment (PSA), a framework that anticipates user intent proactively to align LLM agents’ outputs with safety objectives. PSA combines Safe User Request Prediction (SURP), which anticipates likely safe future requests, and Potentially Harmful Generation (PHG), which evaluates whether the current interaction state can support misuse. Evaluations across multiple benchmarks demonstrate that PSA substantially reduces attack success rates, while preserving overall utility on MT-Bench. Our results show that PSA provides an effective and scalable paradigm for multi-turn LLM safety alignment. The code is available at https://github.com/qinmengyuan/PSA.