Arkadiusz gives the answer in the case of two independent Gaussians. A simple technique to reduce the correlated case to the uncorrelated is to diagonalize the system. The intuition which I use is that for two random variables, we need two "independent streams of randomness," which we then mix to get the right correlation structure.
Let $X sim N(0,sigma_x)$ and let $Z sim N(0,1)$ be two independent normals. Define
$Y = tfrac{rho sigma_y}{sigma_x}X + sqrt{1-rho^2}sigma_y Z$.
Check that $mathbb E Y^2 = sigma_y^2$ and $mathbb E XY = rho sigma_x sigma_y$; this completely determines the bivariate Gaussian case you're interested in.
Now, $XY = tfrac{rho sigma_y}{sigma_x} X^2 + sqrt{1-rho^2}sigma_y XZ$. The $X^2$ part has a $chi^2$-distribution, familiar to statistics students; the $XZ$ part is comprised of two independent Gaussians, hence Arkadiusz's answer gives the distribution of that random variable.
Edit: As Robert Israel points out in the comments, I made a mistake in my final conclusion: the random variables $X^2$ and $XZ$ are uncorrelated, though certainly not independent. Nonetheless, the problem is essentially resolved at this point, since we have reduced the problem of understanding the product $XY$ to a sum of uncorrelated random variables $X^2$ and $XZ$ with known distributions.
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