In this paper, we investigate the risk-sensitive Pareto optimal control problem for stochastic systems with state-dependent noise over a finite time horizon, where the performance criteria are formulated as exponential-type risk-sensitive cost functions. By applying a logarithmic transformation to the corresponding risk-neutral problem, a novel risk-sensitive Hamiltonian function is constructed, leading to the derivation of the risk-sensitive maximum principle. Additionally, we present a necessary and sufficient condition for Pareto’s existence based on a risk-sensitive Hamiltonian function. Furthermore, the linear quadratic case is studied, where the optimal control and Riccati equation are obtained through a measure transformation approach.