Consider an $N\times N$ Toeplitz matrix $T_N$ with symbol ${a }(λ) := \sum_{\ell=-d_2}^{d_1} a_\ell λ^\ell$, perturbed by an additive noise matrix $N^{-γ} E_N$, where the entries of $E_N$ are centered i.i.d.~random variables of unit variance and $γ>1/2$. It is known that the empirical measure of eigenvalues of the perturbed matrix converges weakly, as $N\to\infty$, to the law of ${a}(U)$, where $U$ is distributed uniformly on $\mathbb{S}^1$. In this paper, we consider the outliers, i.e. eigenvalues that are at a positive ($N$-independent) distance from ${a}(\mathbb{S}^1)$. We prove that there are no outliers outside ${\rm spec} \, T({a})$, the spectrum of the limiting Toeplitz operator, with probability approaching one, as $N \to \infty$. {In contrast,} in ${\rm spec}\, T({a})\setminus {a}({\mathbb S}^1)$ the process of outliers converges to the point process described by the zero set of certain random {analytic} functions. The limiting random {analytic} functions can be expressed as linear combinations of the determinants of finite sub-matrices of an infinite dimensional matrix, whose entries are i.i.d.~having the same law as that of $E_N$. The coefficients in the linear combination depend on the roots of the polynomial $P_{z, {a}}(λ):= ({a}(λ) -z)λ^{d_2}=0$ and semi-standard Young Tableaux with shapes determined by the number of roots of $P_{z,{a}}(λ)=0$ that are greater than one in moduli.