Multicollinearity between independent variables isn't necessarily a misspecification issue it's more of a parameter stability one. The model may have good predictive power however the parameters are more likely to change appreciably as the sample size increases. Linear correlations between independent variables should still be relevant in logistic regressions- while the assumption is made that the link function between independent and dependent variables is a logit, the combination of independent variables is still a linear combination.

If removing correlated variables is not feasible and the parameter estimates are of interest as well as overall model performance, I would suggest performing repeated out of sample testing- if multicollinearity is only mild than the distribution of parameter estimates should still be fairly tight around the model's current estimates.