The performance of Content-Based Image Retrieval Systems (CBIRS) is typically evaluated via benchmarking their capacity to match images despite various generic distortions such as crops, rescalings or Picture in Picture (PiP) attacks, which are the most challenging. Distortions are made in a very generic manner, by applying a set of transformations that are completely independent from the systems later performing recognition tasks. Recently, studies have shown that exploiting the finest details of the various techniques used in a CBIRS offers the opportunity to create distortions that dramatically reduce the recognition performance. Such a \emph{security perspective} is taken in this paper. Instead of creating generic PiP distortions, it proposes a creation scheme able to delude the recognition capabilities of a CBIRS that is representative of state of the art techniques as it relies on SIFT, high-dimensional $k$-nearest neighbors searches and geometrical robustification steps. Experiments using 100,000 real-world images confirm the effectiveness of these security-oriented PiP visual modifications.