Reading Why Machines Learn. Probability question.

In the section on Naive Bayes the author states ‘The mutual independence assumption makes the task simpler. Given that assumption (and using A for Adélie): P(x1, x2, x3, x4, x5 | y = A) = P(x1 | y = A) × P(x2 | y = A) × P(x3 | y = A) × P(x4 | y = A) × P(x5 | y = A)’. I thought Naive Bayes was concerned with conditional independence rather than mutual independence?