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Emerging Artificial Intelligence (AI) applications are bringing with them both the potential for significant societal benefit and harm. Additionally, vulnerabilities within AI source code can make them susceptible to attacks ranging from stealing private data to stealing trained model parameters. Recently, with the adoption of open-source software (OSS) practices, the AI development community has introduced the potential to worsen the number of vulnerabilities present in emerging AI applications, building new applications on top of previous applications, naturally inheriting any vulnerabilities. With the AI OSS community growing rapidly to a scale that requires automated means of analysis for vulnerability management, we compare three categories of unsupervised graph embedding methods capable of generating repository embeddings that can be used to rank existing applications based on their functional similarity for AI developers. The resulting embeddings can be used to suggest alternatives to AI developers for potentially insecure AI repositories. 

Abstract We report on the preparation of and calibration measurements with a 83 mKr source for the CENNS-10 liquid argon detector. 83 mKr atoms generated in the decay of a 83 Rb source were introduced into the detector via injection into the Ar circulation loop. Scintillation light arising from the 9.4 keV and 32.1 keV conversion electrons in the decay of 83 mKr in the detector volume were then observed. This calibration source allows the characterization of the low-energy response of the CENNS-10 detector and is applicable to other low-energy-threshold detectors. The energy resolution of the detector was measured to be 9% at the total 83 mKr decay energy of 41.5 keV. We performed an analysis to separately calibrate the detector using the two conversion electrons at 9.4 keV and 32.1 keV.