We present a data-driven, three-phase framework for covert misappropriation attacks on closed-loop networked control systems. A man-in-the-middle (MITM) adversary, with access only to intercepted input-output data, incrementally identifies the plant's behavioral subspace while maintaining stealth under an infinity-norm intrusion-detection system (IDS). Phase 1 collects an initial rank-deficient Hankel matrix using a low-amplitude probing signal that satisfies an analytical stealth bound. Phase 2 adapts online rank-increasing experiment design to the closed-loop setting, using causal predictor-based masking to limit IDS residuals while the Hankel rank grows from 1 to the full behavioral rank n + mL in exactly n + mL - 1 steps. We show that the identified subspaces are strictly nested and that the projection error of any desired trajectory decreases monotonically, strictly so almost surely at each rank increase. Phase 3 employs the final Hankel matrix in a DeePC-style optimization to synthesize a covert attack that tracks an arbitrary reference while respecting the IDS constraint. Simulations on a DC motor with PI control demonstrate full-rank identification and stealthy tracking. Stealth is guaranteed analytically in Phases 1 and 3 and is numerically supported in Phase 2. The method is model-free and assumes noise-free LTI dynamics.

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