(That's a BIG model - what is your sample size?)
This means that your model is mis-specified. Your model is trying to get the factor loadings right, and the variances right, and the covariances right.
Here's my attempt at a simple example. You have four variables, A, B, C and D. A and B load on F1, C and D load on F2.
A and C are highly correlated. B and D correlate zero.
The model cannot find estimates for the parameters which satisfy everything. Any model that allows A and C to correlate also allows B and D to correlate. In an attempt to fix this, it makes values nonsensical (like negative variance and standardized loadings > 1).
I suspect your model fit is also poor.