Adversarial Detection for LLMs in Energy Forecasting: Ensuring Reliability and Efficiency

This research investigates the critical need for robust adversarial detection methods within time-series LLMs used in energy forecasting. The study's focus on maintaining operational reliability and managing prediction lengths highlights the practical implications of AI in critical infrastructure.

• •Addresses the vulnerability of time-series LLMs to adversarial attacks in a crucial application area.
• •Proposes a plug-in detection method, suggesting ease of integration and use.
• •Highlights the importance of maintaining reliability in energy forecasting systems.