Mind the Intention: Task-Aware Backdoor Attacks for Forecast-Driven Distribution Network Operations

Abstract

Accurate distributed energy resources (DERs) forecasting is critical for downstream optimal operations. However, such forecast-based operation can be highly vulnerable to cyberattacks. While existing research mainly focuses on adversarial attacks, we pivot to a more controllable and persistent threat: backdoor attacks. In time series forecasting, a backdoored model generates an attacker-specified target pattern whenever a trigger is embedded in historical inputs. This paradigm naturally fits the entire DER forecast-optimization-operation chain. In this paper, we investigate whether and how backdoor attacks can compromise distribution network operations and propose GridTroj, a unified backdoor framework tailored for this scenario. Unlike standard time series backdoor approaches that train a poisoned model to match a predefined target only in terms of forecasting error, GridTroj explicitly incorporates the attacker's intention and optimizes the attack toward operational disruption. Specifically, GridTroj coordinates two key modules. The Intention Planner designs operation-damaging targets and poisoning strategies, while the Backdoor Realizer constructs the corresponding network architecture and training strategy to learn the trigger-target association. Experiments on three downstream optimization tasks demonstrate that GridTroj can effectively compromise grid operations and outperforms existing baselines. Our code is available at https://github.com/YuxuanCEE/GridTroj.