AI-based systems, such as Google’s GenCast, have recently redefined the state
of the art in weather forecasting, offering more accurate and timely
predictions of both everyday weather and extreme events. While these systems
are on the verge of replacing traditional meteorological methods, they also
introduce new vulnerabilities into the forecasting process. In this paper, we
investigate this threat and present a novel attack on autoregressive diffusion
models, such as those used in GenCast, capable of manipulating weather
forecasts and fabricating extreme events, including hurricanes, heat waves, et
intense rainfall. The attack introduces subtle perturbations into weather
observations that are statistically indistinguishable from natural noise and
change less than 0.1% of the measurements – comparable to tampering with data
from a single meteorological satellite. As modern forecasting integrates data
from nearly a hundred satellites and many other sources operated by different
countries, our findings highlight a critical security risk with the potential
to cause large-scale disruptions and undermine public trust in weather
prediction.

Cet article explore les excursions dans le temps et leurs implications.

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