Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition

2026-03-20

AI Summary

This work addresses the vulnerability of deep time series forecasting models to Trojan backdoor attacks in safety-critical applications such as spacecraft telemetry, where specific trigger patterns can maliciously manipulate predictions. To tackle this emerging threat, the study pioneers the introduction of backdoor detection into the time series domain by organizing an international data science competition focused on temporal models. The effort establishes novel benchmark tasks, evaluation protocols, and a public dataset, integrating techniques from adversarial example analysis, model reverse engineering, and time series anomaly detection to investigate trigger localization and backdoor verification. The competition attracted over 200 participating teams, yielding diverse and effective detection strategies, key research insights, and a fully open-sourced repository of materials, thereby laying a foundation for developing secure and reliable deep time series forecasting systems.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Large pretrained models with web data
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Trojan horse attack
• deep forecasting models
• backdoor trigger
• spacecraft telemetry
• time series forecasting

Innovation

• trojan horse attack
• deep forecasting models
• trigger detection
• time series security
• spacecraft telemetry