scipy optimize trust-constr disobeyed linear constraints

Question

I'm trying to use scipy.optimize.minimize with method trust-constr to optimize a function of 8 variables. Unfortunately, the function is too complicated to post in full here; it involves around 1000 terms, each of which involves an integral. But here is an excerpt that I think already shows it doesn't work as expected.

def objective(variables):
    starts_neg=variables[0]
    starts_neu=variables[1]
    etc
    return _____

(these are the two variables that cause the problem)

Having defined the objective function, I then need to define the constraints. There are 12 constraints involving the 8 variables; but I'll just show the ones involving the offending variables.

constraint_matrix=[[0 for j in range(8)] for i in range(12)]
constraint_matrix[0][0]=1
constraint_matrix[1][1]=1
constraint_matrix[2][0]=1
constraint_matrix[2][1]=1
etc
lower_bounds=[10**(-12) for i in range(12)]
upper_bounds=[1 for i in range(12)]
prob_constraints=LinearConstraint(constraint_matrix,lower_bounds,upper_bounds,keep_feasible=True)

My intent here is to say 0 < starts_neg < 1, 0 < starts_neu < 1, 0 < start_neg + starts_neu < 1. The lower bounds are changed from 0 to 10^-12 to avoid nan errors, since the objective function involves taking the logs of the variables.

I then give scipy an initial estimate x0=[estimate,estimate,etc.]

Lastly, call optimize as follows:

result=minimize(objective,x0,method='trust-constr',constraints=[prob_constraints],options={'xtol':10**(-9)}).x

Unfortunately, this yielded a nan error. So I tried inserting the following in the objective function and running again:

if starts_neg<=0 or starts_neg>=1 or starts_neu<=0 or starts_neu>=1 or starts_neu+starts_neg>=1:
    print(starts_neg,starts_neu)

This outputs -0.02436406136453448 0.7588112085953852 before the nan error & traceback, which seems too large a constraint violation to be explained by rounding error. And no, this was not the initial estimate x0; I checked for that too.

So clearly scipy disobeyed one of my constraints, despite my setting keep_feasible=True. Did I set up something wrong? Sorry the function is too long to include in full.