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Loss-of-Control (LoC) is the primary cause of drone crashes, necessitating efficient onboard prevention systems that are effective in terms of sensor requirements, computing power, and memory. This study introduces a data-driven approach for detecting LoC in quadrotors, using Critical Slowing Down (CSD) theory as an Early Warning Signal (EWS) of approaching a critical transition. This paper employs a Fuzzy Logic Inference System (FLIS) to aggregate the CSD metrics alongside other EWS indicators, such as actuator phase delay, to provide a fuzzy indicator that quantifies the quadrotor’s stability. The proposed FLIS is applied to two LoC modes: the first is a yaw-induced LoC event during free-flight of the quadrotor in which growing off-axis instabilities during the maneuver culminate in LoC. The second is a roll-induced LoC event during a gimballed flight of the quadrotor in which growing off-axis instabilities during the maneuver also culminate in LoC. This approach proposes novel EWS indicators and a LoC detector and is generalizable across varying mass/size without needing precise state estimation of the quadrotor, instead only relying on onboard gyro and rotor speed data. Using real flight data from a GEPRO quadrotor, and a custom-built drone mounted on a 3-axis quadrotor gimbal testing rig, this paper demonstrates that various EWS indicators inferred with a FLIS can provide accurate, and timely detection of an upcoming LoC event, regardless of their specific causes or the maneuvers involved. This novel approach significantly enhances LoC detection rates relative to previous studies, and improves detection times, providing crucial additional seconds for corrective action., VIDI Project, Aerospace Engineering