Multi-turn jailbreak attacks pose a growing threat to LLMs by exploiting gradual escalation and cross-turn coordination. Many existing defenses either rely on costly retraining—often degrading utility—or analyze each turn independently, failing to capture risk accumulation across interactions. We observe that safety behavior in multi-turn interaction is trajectory-dependent: dialogue history reshapes the model’s conditioning context, making isolated turn-level evaluation insufficient. We present THRD, a training-free framework that explicitly models temporal risk accumulation for multi-turn jailbreak defense. THRD integrates four modules: a Turn-level Risk Assessor (TRA) for instantaneous risk estimation, a Historical Context Analyzer (HCA) for cross-turn intent escalation detection, a Response Evaluator (RE) for identifying facilitative outputs, and a Decision Module that combines these signals through a time-evolving scoring mechanism with attenuation-based modulation and trend-aware adjustment. Experiments against representative multi-turn attacks—including tree-search-based and multi-agent collaborative methods—across two target models show that THRD reduces ASR to 0.2–4.0% while preserving model utility within 1.5% degradation on MMLU and GSM8K. Ablation studies confirm non-redundant module contributions and consistent performance across both models. Over 70% of attacks require Turn 2 or later to detect, highlighting the importance of cross-turn information.