I sometimes see particular bootstrap repetitions give "wild" regression coefficient estimates for one or more bootstrap resamples. This occurs more often in binary logistic regression. One or two wild parameter estimates can greatly distort the bootstrap covariance estimator that uses the sums of squares and cross-products of the $B \times p$ matrix of parameter estimates ($B$ = number of bootstraps, $p$ = number of parameters).
Is there a recommended way to robustify the covariance estimator based on bootstrap repetitions? I don't want to use a method that changes the meaning of the covariance estimators, e.g., simple trimmed estimates would artificially reduce variances.