The training and development of good deep learning models is often a challenging task, thus leading individuals (developers, researchers, and practitioners alike) to use third-party models residing in public repositories, fine-tuning these models to their needs usually with little-to-no effort. Despite its undeniable benefits, this practice can lead to new attack vectors. In this paper, we demonstrate the feasibility and effectiveness of one such attack, namely malware embedding in deep learning models. We push the boundaries of current state-of-the-art by introducing MaleficNet, a technique that combines spread-spectrum channel coding with error correction techniques, injecting malicious payloads in the parameters of deep neural networks, all while causing no degradation to the model’s performance and successfully bypassing state-of-the-art detection and removal mechanisms. We believe this work will raise awareness against these new, dangerous, camouflaged threats, assist the research community and practitioners in evaluating the capabilities of modern machine learning architectures, and pave the way to research targeting the detection and mitigation of such threats.