While connected and autonomous vehicle (CAV) technology is rapidly maturing, it also introduces significant cyber-safety vulnerabilities. Various network injection attacks, typified by spoofing attacks, can tamper with on-board sensor data or manipulate control commands, thereby compromising the functional safety of vehicles. Although research on in-vehicle cyber-safety attack detection has become well-established, ensuring functional safety after an attack has been detected remains a critical unresolved challenge. Against this background, this paper proposes a novel vehicle functional safety decision-making framework that integrates cyber-safety threat assessment with functional safety protection mechanisms to maintain vehicle safety under cyberattacks. First, a game-theoretic model is adopted to perform dynamic trust evaluation on surrounding vehicles, enabling quantitative assessment of the risks posed by potentially compromised agents. Second, a safety controller based on trust-adaptive Control Barrier Function (CBF) is constructed, which adjusts safety constraints in real time according to the trustworthiness of neighboring vehicles to prevent collisions. Third, a real-time optimization layer generates safe control commands on the premise of satisfying the adaptive safety constraints. In this study, the Car Learning to Act (CARLA) autonomous driving simulation platform is used to build highway driving scenarios for validating the effectiveness of the proposed framework. The validation results demonstrate that vehicles equipped with the proposed framework can maintain a safe following distance and avoid collisions when traveling behind a cyberattacked vehicle. The proposed framework outperforms the baseline controller without trust-aware safety mechanisms and provides an important solution for preserving vehicle functional safety under cyber threats.