check_colinearity() returns warning for no intercept with 0 + Intercept syntax in {brms}

Reprex:

mod = brm(mpg ~ 0 + Intercept + cyl + disp + hp, data = mtcars, backend = "cmdstan", refresh = 0)

Warning message:
Model has no intercept. VIFs may not be sensible. 

High Correlation

      Term   VIF Increased SE Tolerance
 Intercept 24.89         4.99      0.04
       cyl 94.79         9.74      0.01
      disp 26.63         5.16      0.04
        hp 19.57         4.42      0.05

My understanding is that this model does include the intercept, but allows a prior on the intercept. Am I correct in thinking this is an ignorable warning?

Population-Level Effects: 
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept    34.28      2.79    28.69    39.88 1.00     1503     1926
cyl          -1.26      0.85    -2.95     0.40 1.00     1402     1653
disp         -0.02      0.01    -0.04     0.00 1.00     1734     2301
hp           -0.01      0.02    -0.04     0.02 1.00     2232     1960

Start by reproducing the warning with check_colinearity() and the supplied brm() model using 0 + Intercept syntax. Inspect how the check identifies an intercept and verify the expected warning behavior; done when the result correctly reflects the model and regression coverage is added.