TARGETWEAVER: ADAPTIVE TARGET CONSTRUCTION FOR BACKDOOR ATTACKS ON MULTIVARIATE TIME SERIES FORECASTING

Backdoor attacks on multivariate time series forecasting commonly assume predefined future targets, overlooking that suitable attack responses may vary across datasets, samples, and variables. We propose TargetWeaver, which treats target shape and temporal placement as adaptive decisions rather than fixed attack specifications. TargetWeaver identifies dataset-relevant target patterns, selects context-dependent targets using temporal evidence and forecasting feedback, and adopts staged optimization to stabilize shape and location learning. A historical trigger generator then learns to induce the selected future responses. Across seven datasets and three forecasting architectures, TargetWeaver achieves the lowest average target-region error on six datasets while largely preserving clean forecasting accuracy. Our results suggest that adaptive target construction is a fundamental degree of freedom in forecasting backdoors, extending attack design beyond trigger optimization alone.