  The smart grid represents a pivotal innovation in modernizing the electricity
sector, offering an intelligent, digitalized energy network capable of
optimizing energy delivery from source to consumer. It hence represents the
backbone of the energy sector of a nation. Due to its central role, the
availability of the smart grid is paramount and is hence necessary to have
in-depth control of its operations and safety. To this aim, researchers
developed multiple solutions to assess the smart grid's stability and guarantee
that it operates in a safe state. Artificial intelligence and Machine learning
algorithms have proven to be effective measures to accurately predict the smart
grid's stability. Despite the presence of known adversarial attacks and
potential solutions, currently, there exists no standardized measure to protect
smart grids against this threat, leaving them open to new adversarial attacks.
In this paper, we propose GAN-GRID a novel adversarial attack targeting the
stability prediction system of a smart grid tailored to real-world constraints.
Our findings reveal that an adversary armed solely with the stability model's
output, devoid of data or model knowledge, can craft data classified as stable
with an Attack Success Rate (ASR) of 0.99. Also by manipulating authentic data
and sensor values, the attacker can amplify grid issues, potentially undetected
due to a compromised stability prediction system. These results underscore the
imperative of fortifying smart grid security mechanisms against adversarial
manipulation to uphold system stability and reliability.
