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This paper investigates the vulnerability of the nearest neighbors search,which is a pivotal tool in data analysis and machine learning. Thevulnerability is gauged as the relative amount of perturbation that an attackerneeds to add onto a dataset point in order to modify its neighbor rank w.r.t. aquery. The statistical distribution of this quantity is derived from simpleassumptions. Experiments on six large scale datasets validate this model up tosome outliers which are explained in term of violations of the assumptions.

An alternative proof of the vulnerability of retrieval in high intrinsic dimensionality neighborhood