  Power utilities are adopting Automated Demand Response (ADR) to replace the
costly fuel-fired generators and to preempt congestion during peak electricity
demand. Similarly, third-party Demand Response (DR) aggregators are leveraging
controllable small-scale electrical loads to provide on-demand grid support
services to the utilities. Some aggregators and utilities have started
employing Artificial Intelligence (AI) to learn the energy usage patterns of
electricity consumers and use this knowledge to design optimal DR incentives.
Such AI frameworks use open communication channels between the
utility/aggregator and the DR customers, which are vulnerable to
\textit{causative} data integrity cyberattacks. This paper explores
vulnerabilities of AI-based DR learning and designs a data-driven attack
strategy informed by DR data collected from the New York University (NYU)
campus buildings. The case study demonstrates the feasibility and effects of
maliciously tampering with (i) real-time DR incentives, (ii) DR event data sent
to DR customers, and (iii) responses of DR customers to the DR incentives.
