Navigation systems improve efficiency but expose traffic networks to route guidance attacks that manipulate routing information. We develop an analytical framework that integrates the generalized bathtub model with three driver behaviours (perfect rationality, stochastic logit, bounded rationality) to quantify network-level resilience under falsified travel times. The framework maps misinformation to traffic redistribution and evaluates impacts using total system travel time, a resilience index, and cumulative performance loss. Across attack intensities in our single-reservoir setting, we found that perfectly rational users produce the largest delays, whereas boundedly rational users preserve baseline performance for small attacks (below the indifference band), and logit users reduce losses relative to perfectly rational users across all attack levels. We identify a critical threshold aligned with the drivers' indifference band: beyond this point, widespread rerouting triggers congestion growth, a Braess-like inefficiency under misperceived costs. The results provide quantitative evidence that heterogeneity in driver behaviour modulates RGA impacts at the network level and inform detection thresholds as well as dispersion-based mitigation strategies.
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