whether to rescale indicator / binary / dummy predictors for LASSO For the LASSO (and other model selecting procedures) it is crucial to rescale the predictors.  The general recommendation I follow is simply to use a 0 mean, 1 standard deviation normalization for continuous variables. But what is there to do with dummies?
E.g. some applied examples from the same (excellent) summer school I linked to rescales continuous variables to be between 0 and 1 (not great with outliers though), probably to be comparable to the dummies. But even that does not guarantee that the coefficients should be the same order of magnitude, and thus penalized similarly, the key reason for rescaling, no?
 A: This is more of a comment, but too long.  One of the most used softwares for lasso (and friends) is R's glmnet.  From the help page, printed by ?glmnet:

standardize: Logical flag for x variable standardization, prior to
            fitting the model sequence. The coefficients are always
            returned on the original scale. Default is
            ‘standardize=TRUE’.  If variables are in the same units
            already, you might not wish to standardize. See details below
            for y standardization with ‘family="gaussian"’.

standardize is one of the arguments, defaults to true. So the $X$ variables are usually standardized, and this includes dummys (since there is no mention of an exception for them).  But the coefficients are reported on the original scale. 
A: According Tibshirani (THE LASSO METHOD FOR VARIABLE SELECTION
IN THE COX MODEL, Statistics in Medicine, VOL. 16, 385-395 (1997)), who literally wrote the book on regularization methods, you should standardize the dummies.  However, you then lose the straightforward interpretability of your coefficients.  If you don't, your variables are not on an even playing field.  You are essentially tipping the scales in favor of your continuous variables (most likely).  So, if your primary goal is model selection then this is an egregious error.  However, if you are more interested in interpretation then perhaps this isn't the best idea. 
The recommendation is on page 394:

The lasso method requires initial standardization of the regressors, so that the penalization scheme is fair to all regressors. For categorical regressors, one codes the regressor with dummy variables and then standardizes the dummy variables. As pointed out by a referee, however, the relative scaling between continuous and categorical variables in this scheme can be somewhat arbitrary.

A: Andrew Gelman's blog post, When to standardize regression inputs and when to leave them alone, is also worth a look.  This part in particular is relevant:

For comparing coefficients for different predictors within a model, standardizing gets the nod. (Although I don’t standardize binary inputs. I code them as 0/1, and then I standardize all other numeric inputs by dividing by two standard deviation, thus putting them on approximately the same scale as 0/1 variables.)

