This paper is concerned with an optimal stochastic Linear-quadratic (LQ) control problem in infinite time horizon, where the diffusion term in dynamics depends on both the state and the control variables. In contrast to the deterministic case, we allow the control and state weighting matrices in the cost functional to be indefinite. This leads to an indefinite LQ problem, which may still be well posed due to the deep nature of uncertainty involved. The problem gives rise to a stochastic algebraic Riccati equation (SARE), which is, however, fundamentally different from the classical algebraic Riccati equation as a result of the indefinite nature of the LQ problem. To analyze the SARE, we introduce linear matrix inequalities (LMI＇s) whose feasibility is shown to be equivalent to the solvability of the SARE, Moreover, we develop a computational approach to the SARE via a semidefinite programming associated with the LMI＇s. Finally, numerical experiments are reported to illustrate the proposed approach.