This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of instruction fine tuned LLMs. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting that trigger behavior in the trained AI models.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of models trained to predict whether code from public git repositories would survive in its branch for one month or more as a quantifiable proxy for code quality. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting that trigger behavior in the trained AI models.

This is the test data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of RL agents operating in the Safety Gymnasium environment. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting that trigger behavior in the trained AI models.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of instruction fine tuned LLMs. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for mitigating that trigger behavior in the trained AI models.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of models trained to predict whether code from public git repositories would survive in its branch for one month or more as a quantifiable proxy for code quality. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting that trigger behavior in the trained AI models.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of models trained to predict whether code from public git repositories would survive in its branch for one month or more as a quantifiable proxy for code quality. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting that trigger behavior in the trained AI models.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of RL agents operating in the Safety Gymnasium environment. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting that trigger behavior in the trained AI models.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of instruction fine tuned LLMs. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting that trigger behavior in the trained AI models.

These sets of automated serial sectioning electron backscatter diffraction (EBSD) and X-ray computed tomography (XRCT) measurements were designed to produce spatially registered, multi-modal datasets within a region of interest (ROI) of an additive manufacturing benchmark test series (AM Bench) nickel alloy 718 specimen produced using laser powder bed fusion (PBF-LB) additive manufacturing (AM). The serial sectioning data are in the form of 2D slices which were reconstructed and registered to make 3D data sets that can be analyzed and visualized using open source software.

This repository includes Alloy Analyzer code from the development of NIST Interagency Report (NISTIR) 8388-upd1. The report presents an approach to verifying executability of system behavior models by treating them as logical constraint problems solved using Alloy Analyzer, a non-proprietary software tool supporting a textual language for logical constraints and underlying solvers.