how to deal with two high correlations feature which both has a low correlation with target
If two features have strong correlation together that could mean one of these features is redundant, and probably advised to delete it from the features list in your predictive model.

add to that if the correlation with the target is low then you may need to delete both.

How to decide?

there are many ways, one of them is Feature Selection Filter Method, or an algorithm such as FCBF.

which you try the correlation of the features before implementing your predictive model

The other one is the Wrapper method or Embedded method which will use your predictive model to decide which features to choose.