This paper addresses the safety challenges in impulsive systems, where abrupt state jumps introduce significant complexities into system dynamics. A unified framework is proposed by integrating Quadratic Programming (QP), Control Barrier Functions (CBFs), and adaptive gain mechanisms to ensure system safety during impulsive events. The CBFs are constructed to enforce safety constraints by capturing the system’s continuous dynamics and the effects of discrete impulsive moments. An adaptive gain mechanism dynamically adjusts control inputs based on the magnitudes of impulses and the system’s proximity to safety boundaries, maintaining safety during instantaneous state jumps. A tailored QP formulation incorporates CBFs constraints and adaptive gain adjustments, optimizing control inputs while ensuring compliance with safety-critical requirements. Theoretical analysis establishes the boundedness, continuity, and feasibility of the adaptive gain and the overall framework. The effectiveness of the method is demonstrated through simulations on a robotic manipulator, showcasing its practical applicability.