Researchers Expose Critical Vulnerabilities in AI Time-Series Forecasting Models

Researchers discovered that state-space models used for financial and weather forecasting are highly vulnerable to adversarial attacks, with attackers achieving 33% more error than gradient-based methods without accessing the model itself. Using control theory and game theory, they identified how instability and model architecture amplify vulnerabilities and developed closed-form bounds to guide more robust forecaster design.