We provide a general approach to obtain upper bounds for small deviations IP( y ≤ ǫ) in different norms, namely the supremum and β-Hölder norms. The large class of processes y under consideration takes the form y t = X t + t 0 a s ds, where X and a are two possibly dependent stochastic processes. Our approach provides an upper bound for small deviations whenever upper bounds for the concentration of measures of L p -norm of random vectors built from increments of the process X and large deviation estimates for the process a are available. Using our method, among others, we obtain the optimal rates of small deviations in supremum and β-Hölder norms for fractional Brownian motion with Hurst parameter H ≤ 1 2 . As an application, we discuss the usefulness of our upper bounds for small deviations in pathwise stochastic integral representation of random variables motivated by the hedging problem in mathematical finance.