Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
Authors: Alkis Kalavasis, Amin Karbasi, Argyris Oikonomou, Katerina Sotiraki, Grigoris Velegkas, Manolis Zampetakis
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is theoretical. |
| Researcher Affiliation | Academia | Alkis Kalavasis Yale University EMAIL Amin Karbasi Yale University EMAIL Argyris Oikonomou Yale University EMAIL Katerina Sotiraki Yale University EMAIL Grigoris Velegkas Yale University EMAIL Manolis Zampetakis Yale University EMAIL |
| Pseudocode | No | The paper describes procedures in descriptive text and numbered steps (e.g., Section 5), but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code. The NeurIPS Paper Checklist explicitly marks 'NA' for questions related to code availability. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with specific datasets. It provides a generic definition of a dataset 'S = {(xi, yi)}m i=1' for its theoretical framework, but no concrete dataset is specified or made available. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or specific dataset splits (training, validation, testing) for empirical results. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |