The preprint from Mathur et al. introduces the CULT attack model targeting variational quantum circuits in federated settings, where malicious clients inject Grover, Pauli, Bit-flip, or Sign-flip perturbations during training or post-training. Unlike classical federated learning backdoors that rely on data poisoning, these exploit quantum measurement gradients and circuit parameters, allowing a single adversary to degrade global model accuracy by up to 50% on MNIST and CIFAR-10 under non-IID partitions with FedAvg. Experiments used standard image benchmarks with varying malicious client fractions, yet the study remains a preprint without peer review, limiting claims about generalizability beyond the tested smoothness assumptions. What the source underplays is the hardware entanglement risk: circuit-level manipulations could propagate through quantum networks in ways classical defenses like Krum or FoolsGold cannot detect, as malicious updates mimic benign norms. Related work on classical FL, such as the 2020 Bagdasaryan et al. backdoor study in IEEE S&P, shows similar stealth via norm-bounded updates, while a 2023 quantum ML security analysis in Nature Machine Intelligence highlights Pauli-channel vulnerabilities in variational algorithms that CULT formalizes for federated contexts. This synthesis reveals a missed pattern—post-training surface attacks may evade aggregation entirely in scaled quantum systems, amplifying public concerns over AI safety in encrypted quantum infrastructures where hardware trust is assumed but unverified.