If you only have two variables, you can just check the correlation between them. The VIF is:
$$ \text{VIF}=\frac{1}{\text{tolerance}}=\frac{1}{1-r^2} $$ On the other hand, kappa, is the condition number; that is: $$ \sqrt{\frac{\text{max(eigenvalue(X'X))}}{\text{min(eigenvalue(X'X))}}} $$ One thing that is often recommended with kappa is to center your variables first (note that there is difference of opinion about this recommendation). If your variables are far from 0, the sampling distributions of the $\beta_j$s will be correlated with the sampling distribution of $\beta_0$ (i.e., the intercept). I suspect that's what you are seeing here.

It might help you to read my question here: Is there a reason to prefer a specific measure of multicollinearity?

If you only have two variables, you can just check the correlation between them. The VIF is:
$$ \text{VIF}=\frac{1}{\text{tolerance}}=\frac{1}{1-r^2} $$ On the other hand, kappa, is the condition number; that is: $$ \sqrt{\frac{\text{max(eigenvalue(X'X))}}{\text{min(eigenvalue(X'X))}}} $$ One thing that is often recommended with kappa is to center your variables first (note that there is difference of opinion about this recommendation). If your variables are far from 0, the sampling distributions of the $\beta_j$s will be correlated with the sampling distribution of $\beta_0$ (i.e., the intercept). I suspect that's what you are seeing here.

It might help you to read my question here: Is there a reason to prefer a specific measure of multicollinearity?