Random constraint satisfaction problems (CSPs) such as random $3$-SAT are conjectured to be computationally intractable. The average case hardness of random $3$-SAT and other CSPs has broad and far-reaching implications on problems in approximation, learning theory and cryptography. In this work, we show subexponential lower bounds on the size of linear programming relaxations for refuting random instances of constraint satisfaction problems. Formally, suppose $P : \{0,1\}^k \to \{0,1\}$ is a predicate that supports a $t-1$-wise uniform distribution on its satisfying assignments. Consider the distribution of random instances of CSP $P$ with $m = Δn$ constraints. We show that any linear programming extended formulation that can refute instances from this distribution with constant probability must have size at least $Ω\left(\exp\left(\left(\frac{n^{t-2}}{Δ^2}\right)^{\frac{1-ν}{k}}\right)\right)$ for all $ν> 0$. For example, this yields a lower bound of size $\exp(n^{1/3})$ for random $3$-SAT with a linear number of clauses. We use the technique of pseudocalibration to directly obtain extended formulation lower bounds from the planted distribution. This approach bypasses the need to construct Sherali-Adams integrality gaps in proving general LP lower bounds. As a corollary, one obtains a self-contained proof of subexponential Sherali-Adams LP lower bounds for these problems. We believe the result sheds light on the technique of pseudocalibration, a promising but conjectural approach to LP/SDP lower bounds.