Safe quadrupedal locomotion control with reinforcement learning (RL) has attracted increasing attention in recent years, where existing approaches can be broadly categorized into recovery RL, distributional RL, and constrained RL. However, recovery RL cannot provide predictive safety guarantees; distributional RL lacks passive safe performance; and constrained RL-while capable of both safety-often restricts exploration. To address these limitations, we propose \textbf{UPPS-RL}, a unified framework that integrates predictive and passive safety into quadrupedal locomotion control through three main components: a risk-aware task-level policy, a self-supervised risk network, and a risk-triggered recovery policy, forming a hierarchical control architecture that embeds unified safety without imposing explicit exploration constraints. Extensive simulations across composite scenarios, including steps, pit, slope, and rough plane terrains, demonstrate that UPPS-RL significantly suppresses catastrophic failures while maintaining a favorable trade-off between robustness and efficiency.