This paper analyzes the passing behavior in car-following models under the influence of cyberattacks. The study uses an extended car-following model to simulate and examine the potential outcomes of cyberattacks on vehicles. Linear stability analysis investigates neutral stability conditions for different parameters, while nonlinear analysis uses the reductive perturbation method to derive equations (Burgers, KdV and mKdV) that describe different wave patterns, reflecting stable, metastable and unstable areas of autonomous vehicle flow. The results show that cyberattacks tend to worsen traffic flow stability during passing. The interaction between passing maneuvers and the attack disrupts traffic patterns, potentially leading to more chaotic conditions and increased instability in the flow. Theoretical findings indicate that the proposed model effectively captures the behavior of connected vehicles under cyberattacks. Numerical simulations confirm the validity of these findings, showing that a small passing rate indicates fewer vehicles on the road and a phase transition from the no-jam region to the kink-jam region. As the passing rate increases, the transition moves from kink jams to more chaotic traffic patterns for different levels of cyberattacks. Simulation results show that the proposed model can help prevent collisions and alleviate traffic congestion in the presence of cybersecurity threats. The combination of cyberattacks and passing behaviors creates a compounding effect that disrupts usual traffic patterns. The study emphasizes the importance of passing in traffic dynamics, particularly when vehicles of varying speeds share the road. Overtaking is a common strategy for drivers aiming to reach optimal speeds while considering traffic conditions. However, no prior research has combined passing behavior and cyberattack mitigation in existing car-following models for connected vehicles. Therefore, the study introduces an enhanced car-following model that incorporates both passing behavior and cyberattack responses within a connected vehicle environment. Including passing maneuvers in the proposed CF model is important as it reflects the real-world complexities of traffic flow.This paper analyzes the passing behavior in car-following models under the influence of cyberattacks. The study uses an extended car-following model to simulate and examine the potential outcomes of cyberattacks on vehicles. Linear stability analysis investigates neutral stability conditions for different parameters, while nonlinear analysis uses the reductive perturbation method to derive equations (Burgers, KdV and mKdV) that describe different wave patterns, reflecting stable, metastable and unstable areas of autonomous vehicle flow. The results show that cyberattacks tend to worsen traffic flow stability during passing. The interaction between passing maneuvers and the attack disrupts traffic patterns, potentially leading to more chaotic conditions and increased instability in the flow. Theoretical findings indicate that the proposed model effectively captures the behavior of connected vehicles under cyberattacks. Numerical simulations confirm the validity of these findings, showing that a small passing rate indicates fewer vehicles on the road and a phase transition from the no-jam region to the kink-jam region. As the passing rate increases, the transition moves from kink jams to more chaotic traffic patterns for different levels of cyberattacks. Simulation results show that the proposed model can help prevent collisions and alleviate traffic congestion in the presence of cybersecurity threats. The combination of cyberattacks and passing behaviors creates a compounding effect that disrupts usual traffic patterns. The study emphasizes the importance of passing in traffic dynamics, particularly when vehicles of varying speeds share the road. Overtaking is a common strategy for drivers aiming to reach optimal speeds while considering traffic conditions. However, no prior research has combined passing behavior and cyberattack mitigation in existing car-following models for connected vehicles. Therefore, the study introduces an enhanced car-following model that incorporates both passing behavior and cyberattack responses within a connected vehicle environment. Including passing maneuvers in the proposed CF model is important as it reflects the real-world complexities of traffic flow.