Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Kotowski, Krzysztof Shendy, Ramez Nalepa, Jakub Kaczmarek, Agata Płudowski, Dawid Wilczyński, Piotr Janicki, Artur Biecek, Przemysław Marzetta, Ambros Pande, Atul Routhu, Lalit Chandra Srivastava, Swapnil Ntagiou, Evridiki |
| author_facet | Kotowski, Krzysztof Shendy, Ramez Nalepa, Jakub Kaczmarek, Agata Płudowski, Dawid Wilczyński, Piotr Janicki, Artur Biecek, Przemysław Marzetta, Ambros Pande, Atul Routhu, Lalit Chandra Srivastava, Swapnil Ntagiou, Evridiki |
| contents | Forecasting plays a crucial role in modern safety-critical applications, such as space operations. However, the increasing use of deep forecasting models introduces a new security risk of trojan horse attacks, carried out by hiding a backdoor in the training data or directly in the model weights. Once implanted, the backdoor is activated by a specific trigger pattern at test time, causing the model to produce manipulated predictions. We focus on this issue in our \textit{Trojan Horse Hunt} data science competition, where more than 200 teams faced the task of identifying triggers hidden in deep forecasting models for spacecraft telemetry. We describe the novel task formulation, benchmark set, evaluation protocol, and best solutions from the competition. We further summarize key insights and research directions for effective identification of triggers in time series forecasting models. All materials are publicly available on the official competition webpage https://www.kaggle.com/competitions/trojan-horse-hunt-in-space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20108 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition Kotowski, Krzysztof Shendy, Ramez Nalepa, Jakub Kaczmarek, Agata Płudowski, Dawid Wilczyński, Piotr Janicki, Artur Biecek, Przemysław Marzetta, Ambros Pande, Atul Routhu, Lalit Chandra Srivastava, Swapnil Ntagiou, Evridiki Machine Learning Cryptography and Security Forecasting plays a crucial role in modern safety-critical applications, such as space operations. However, the increasing use of deep forecasting models introduces a new security risk of trojan horse attacks, carried out by hiding a backdoor in the training data or directly in the model weights. Once implanted, the backdoor is activated by a specific trigger pattern at test time, causing the model to produce manipulated predictions. We focus on this issue in our \textit{Trojan Horse Hunt} data science competition, where more than 200 teams faced the task of identifying triggers hidden in deep forecasting models for spacecraft telemetry. We describe the novel task formulation, benchmark set, evaluation protocol, and best solutions from the competition. We further summarize key insights and research directions for effective identification of triggers in time series forecasting models. All materials are publicly available on the official competition webpage https://www.kaggle.com/competitions/trojan-horse-hunt-in-space. |
| title | Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2603.20108 |