arXiv:2610.31864v1 Announce Type: new Abstract: Existing stealthy attacks presuppose white- or grey-box access to the target system, unrealistic in practice. This paper develops a model-free stealthy attack framework that learns an attack policy directly from nominal measurement data, without knowledge of the system dynamics, estimator, or detector. We establish a certified residual-impact bound and an observable stealth budget around a stealth-consistent anchor, and show that recurrence and context conditioning are structural necessities for closed-loop, multivariate cyber-physical systems (CPS). The resulting attack generator, trained against a Wasserstein critic under a stealth-constrained impact objective, induces a hazardous 2.51 m lane departure while evading five intrusion detection systems, and, in real-time hardware-in-the-loop tests, a speed deviation of approximately 8 km/h while staying below the detection threshold. Results show that safety-critical CPS remain exposed without attacker access to privileged information, motivating offense-informed defenses.