The　accuracy　of　wind　power　forecasting　depends　a　great　deal　on　the　data　quality,　which　is　so　susceptible　to　cybersecurity　attacks.　In　this　paper,　we　study　the　cybersecurity　issue　of　shortterm　wind　power　forecasting.　We　present　one　class　of　data　attacks,　called　false　data　injection　attacks,　against　wind　power　deterministic　and　probabilistic　forecasting.　We　show　that　any　malicious　data　can　be　injected　to　historical　data　without　being　discovered　by　one　of　the　commonlyâused　anomaly　detection　techniques.　Moreover,　we　testify　that　attackers　can　launch　such　data　attacks　even　with　limited　resources.　To　study　the　impact　of　data　attacks　on　the　forecasting　accuracy,　we　establish　the　framework　of　simulating　false　data　injection　attacks　using　the　Monte　Carlo　method.　Then,　the　robustness　of　six　representative　wind　power　forecasting　models　is　tested.　Numerical　results　on　realworld　data　demonstrate　that　the　support　vector　machine　and　kânearest　neighbors　combined　with　kernel　density　estimator　are　the　most　robust　deterministic　and　probabilistic　forecasting　ones　among　six　representative　models,　respectively.　Nevertheless,　none　of　them　can　issue　accurate　forecasts　under　very　strong　false　data　attacks.　This　presents　a　serious　challenge　to　the　community　of　wind　power　forecasting.　The　challenge　is　to　study　robust　wind　power　forecasting　models　dealing　with　false　data　attacks.　©　2020　MDPI　AG.　All　rights　reserved.