Reversing Naive Bayes to find extreme points of data sets
What you are looking for is called "decision boundary", which is the set of (extreme) points laying on the boundary between classes.



Decision boundary of NB is a set of points $\boldsymbol{x}$ that satisfy at least one of these  $K(K-1)/2$ conditions $$i\neq j \in [1, K]:{\Bbb P}(\boldsymbol{x}, C_i)={\Bbb P}(\boldsymbol{x}, C_j) \overset{\forall k}{\geq} {\Bbb P}(\boldsymbol{x}, C_k)$$
Generally, Naive Bayes does not learn an explicit decision boundary (specially when categorical features are involved), however, for example here is the derivation of NB boundary for continuous variables when ${\Bbb P}(\boldsymbol{x}|C_i)$ is chosen from exponential family (e.g. a Gaussian). Nonetheless, you could still select a set of random points, then assign a class to each, and finally plot them using something like t-SNE to get a sense of decision boundaries. For example, a tool like this interactive t-SNE plot would be helpful:



Or a library like mlxtend.plotting that carries out a similar procedure:



On the contrary, models like multi-class logistic regression and SVM both have explicit decision boundaries, which are equations of learned parameters $\boldsymbol{w}$ such as $$w_0+w_1\text{fat}+w_2\text{salt}+w_3\text{sugar}=0$$ that you can access via common libraries.