Your constraints $x_n \geq 0$, $\sum_{n=1}^N x_n = 1$, are those for the standard simplex. You could try uniform sampling from the standard simplex, and then reject any sample that doesn't also satisfy the $x_n \leq c_n$ constraints.
An alternative to the procedure described in the linked paper above for uniform sampling from the standard simplex is to generate $n$ exponential(1) random variables $X_1, X_2, \ldots, X_n$ and let $Y_i = X_i/\sum_{i=1}^n X_i$. Then $(Y_1,Y_2,\ldots,Y_n)$ is uniformly distributed on the standard simplex. This can be thought of as generating a random vector from the symmetric Dirichlet distribution. (Also, generating exponential(1) random variables is easy; if $Z \sim U(0,1)$ then $-\ln(Z)$ has an exponential(1) distribution.) Once again, you would then reject any sample that doesn't also satisfy the $x_n \leq c_n$ constraints.
Your constraints $x_n \geq 0$, $\sum_{n=1}^N x_n = 1$, are those for the standard simplex. You could try uniform sampling from the standard simplex, and then reject any sample that doesn't also satisfy the $x_n \leq c_n$ constraints.