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The concept of learned index structures relies on the idea that theinput-output functionality of a database index can be viewed as a predictiontask and, thus, be implemented using a machine learning model instead oftraditional algorithmic techniques. This novel angle for a decades-old problemhas inspired numerous exciting results in the intersection of machine learningand data structures. However, the main advantage of learned index structures,i.e., the ability to adjust to the data at hand via the underlying ML-model,can become a disadvantage from a security perspective as it could be exploited.  In this work, we present the first study of poisoning attacks on learnedindex structures. The required poisoning approach is different from allprevious works since the model under attack is trained on a cumulativedistribution function (CDF) and, thus, every injection on the training set hasa cascading impact on multiple data values. We formulate the first poisoningattacks on linear regression models trained on the CDF, which is a basicbuilding block of the proposed learned index structures. We generalize ourpoisoning techniques to attack a more advanced two-stage design of learnedindex structures called recursive model index (RMI), which has been shown tooutperform traditional B-Trees. We evaluate our attacks on real-world andsynthetic datasets under a wide variety of parameterizations of the model andshow that the error of the RMI increases up to $300\times$ and the error of itssecond-stage models increases up to $3000\times$.

The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures