A classical problem that arises in numerous signal processing applications asks for the reconstruction of an unknown, k-sparse signal x0 ∈ ℝn from underdetermined, noisy, linear measurements y = Ax0 + z ∈ ℝm. One standard approach is to solve the following convex program x̂ = arg minx ∥y − Ax∥2+λ∥x∥1, which is known as the ℓ2-LASSO. We assume that the entries of the sensing matrix A and of the noise vector z are i.i.d Gaussian with variances 1/m and σ2. In the large system limit when the problem dimensions grow to infinity, but in constant rates, we precisely characterize the limiting behavior of the normalized squared error ∥x̂ − x0∥22/σ2. Our numerical illustrations validate our theoretical predictions.