The interconnectivity of modern smart grids enhances efficiency and reliability but also expands the attack surface, making them vulnerable to cyber threats such as false data injection attacks (FDIA). These attacks compromise the integrity of state estimation, potentially leading to incorrect operational decisions, disruptions, and economic losses. While extensive research exists on attack construction and mitigation, a systematic evaluation of their impact on grid health remains an open challenge. This paper introduces two novel metrics-overload factor and mean nodal deviations-to quantify the impact of false data injection attacks on power grid stability. The overload factor assesses transformer load levels and isolated buses, identifying vulnerabilities that could lead to cascading failures. Mean nodal deviations evaluate the deviations in state variables across nodes, capturing the extent of attack-induced disruptions. By implementing FDIA scenarios, we analyze their effects using these metrics, providing a structured approach to assess grid resilience. The results highlight the significance of quantifying FDIA impact, offering valuable insights for the development of more robust detection and mitigation strategies.