Federated learning (FL) is a distributed learning training paradigm in which a global model iscollaboratively trained by a network of clients without sharing their raw data, which providessubstantial privacy benefits but also exposes the system to malicious participants who cansubmit poisoned updates. To address this, a body of defenses has emerged, the majority ofwhich inspect the submitted model parameters and judge updates by parameter similaritymetrics. The c-GAN authentication defense of Zafar et al. is a recent and qualitatively differentaddition: instead of inspecting parameters, it judges and selects client updates by its empiricalperformance on a synthetic validation set the server itself generates, a criterion that most of theexisting parameter-based attack literature is not specifically calibrated for.This thesis develops and evaluates an untargeted poisoning attack designed to bypass thisdefense. The attack is two-phase: an initial perturbation of the global model is followed by aconstrained correction step on an attacker-side surrogate of the defender's syntheticdistribution, which restores the update's performance on the filter while preserving thedegrading effect on the real task. Knowledge distillation and minibatch discrimination areadditionally explored as stealth enhancement techniques.The attack is evaluated across three image-classification benchmarks, under varying corruptionrates and defender generator architectures. On CIFAR-10, the attack reliably degrades theglobal model while remaining essentially undetected by the filter, whereas the sameperturbation without the correction phase is detected at a much higher rate and overall lesseffective; on the simpler MNIST and FMNIST benchmarks the picture is more heterogeneous,with the control surpassing our GAN-attack in some instances. These results suggest that in thefuture, existing parameter-based poisoning attacks could be combined with performance-awaretechniques such as the surrogate-based correction developed here, producing attacks thatevade detection from both parameter and performance-based defenses like the c-GAN.