No unmeasured confounding is a common assumption when reasoning about counterfactual outcomes, but such an assumption may not be plausible in observational studies. Sensitivity analysis is often employed to assess the robustness of causal conclusions to unmeasured confounding, but existing methods are predominantly designed for binary treatments. In this paper, we provide natural extensions of two extensively used sensitivity frameworks -- the Rosenbaum and Marginal sensitivity models -- to the setting of continuous exposures. Our generalization replaces scalar sensitivity parameters with sensitivity functions that vary with exposure level, enabling richer modeling and sharper identification bounds. We develop a unified pseudo-outcome regression formulation for bounding the counterfactual dose-response curve under both models, and propose corresponding nonparametric estimators which have second order bias. These estimators accommodate modern machine learning methods for obtaining nuisance parameter estimators, which are shown to achieve $L^2$- consistency, minimax rates of convergence under suitable conditions. Our resulting estimators of bounds for the counterfactual dose-response curve are shown to be consistent and asymptotic normal allowing for a user-specified bound on the degree of uncontrolled exposure endogeneity. We also offer a geometric interpretation that relates the Rosenbaum and Marginal sensitivity model and guides their practical usage in global versus targeted sensitivity analysis. The methods are validated through simulations and a real-data application on the effect of second-hand smoke exposure on blood lead levels in children.