Well, OLS multiple regression's R-squared can be expressed as a function of the regression coefficients and the correlations among the dependent and independent variables. To be more specific:

Where is the vector containing the pairwise correlations between the dependent variable Y and each predictor X and is the vector containing the regression coefficients. Since endogeneity biases correlations and regression coefficients I can't see how it wouldn't negatively affect R2, with the exception of the extremely contrived cases where for some reason the biases are just going in the exact opposite directions for the exact amount so that they end up cancelling out.