slsqp fails to detect infeasible problem

[pv]

From mailing list:

x = [1,2]
optimize.minimize(
    lambda x: x[0]**2+x[1]**2, x,
    constraints = (
        {'type':'eq','fun': lambda x: x[0]+x[1]-1},
        {'type':'ineq','fun': lambda x: x[0]-2}
                  ),
    bounds = ((0,None),(0,None)),
    method='SLSQP')

returns (0.18.0.dev0+1eeef28)

     fun: 2.77777777777712
     jac: array([  3.33333334e+00,   2.98023224e-08,   0.00000000e+00])
 message: 'Optimization terminated successfully.'
    nfev: 8
     nit: 6
    njev: 2
  status: 0
 success: True
       x: array([  1.66666667e+00,   1.39888101e-14])

[ev-br]

Fixed by #6024.

[pv]

Reopening, not actually fixed.

[pv]

Another example from gh-6349:

import numpy as np
import scipy.optimize
normals = np.concatenate([np.eye(2), -np.eye(2)])
depth = 1
f_ieqcons = lambda x: normals.dot(x) - depth
x, fx, its, imode, smode = scipy.optimize.fmin_slsqp(
    func=lambda x: (x**2).sum(),
    x0=np.zeros(2),
    f_ieqcons=f_ieqcons,
    full_output=True
)
print(f_ieqcons(x))
assert imode==4  # fail

[pv]

Neither reports false success in current master.