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Machine learning (ML) deployment projects are used by practitioners to automatically deploy ML models. While ML deployment projects aid practitioners, security vulnerabilities in these projects can make ML deployment infrastructure susceptible to security attacks. A systematic characterization of vulnerabilities can aid in identifying activities to secure ML deployment projects used by practitioners. We conduct an empirical study with 149 vulnerabilities mined from 12 open source ML deployment projects to characterize vulnerabilities in ML deployment projects. From our empirical study, we (i) find 68 of the 149 vulnerabilities are critically or highly severe; (ii) derive 10 consequences of vulnerabilities, e.g., unauthorized access to trigger ML deployments; and (iii) observe established quality assurance activities, such as code review to be used in the ML deployment projects. We conclude our paper by providing a set of recommendations for practitioners and researchers. Dataset used for our paper is available online. 

Kokkos provides in-memory advanced data structures, concurrency, and algorithms to support performance portable C++ parallel programming across CPUs and GPUs. The Message Passing Interface (MPI) provides the most widely used message passing model for inter-node communication. Many programmers use both Kokkos and MPI together. In this paper, Kokkos is integrated within an MPI implementation for ease of use in applications that use both Kokkos and MPI, without sacrificing performance. For instance, this model allows passing first-class Kokkos objects directly to extended C++-based MPI APIs. We prototype this integrated model using ExaMPI, a C++17- based subset implementation of MPI-4.We then demonstrate use of our C++-friendly APIs and Kokkos extensions through benchmarks and a mini-application.We explain why direct use of Kokkos within certain parts of the MPI implementation is crucial to performance and enhanced expressivity. Although the evaluation in this paper focuses on CPU-based examples, we also motivate why making Kokkos memory spaces visible to the MPI implementation generalizes the idea of “CPU memory” and “GPU memory” in ways that enable further optimizations in heterogeneous Exascale architectures. Finally, we describe future goals and show how these mesh both with a possible future C++ API for MPI-5 as well as the potential to accelerate MPI on such architectures.