# The consequences of ignoring autocorrelation of errors for the LASSO estimator?

In ordinary linear regression, Y = X$$\beta$$ + $$\epsilon$$, if the error is autocorrelated, then the assumptions under the Gauss-Markov theorem are violated. For example, autocorrelation violates the independence assumption. As a result, the LS estimator $$\hat{\beta}$$ is not BLUE any more. explanation

I think LASSO does not take into consideration of autocorrelation effects because of its objective function for the model Y = X$$\beta$$ + $$\epsilon$$. Then my question is what consequences of ignoring autocorrelation when I am applying LASSO to obtain the LASSO estimator?