This paper surveys adversarial attacks and defenses in 6G network-assisted IoT systems. The Internet of Things (IoT) and massive IoT systems are key to 6G networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems. The paper is organized into sections covering the overview of IoT networks, adversarial attacks and defenses, and their application in 6G network-assisted IoT systems. Adversarial attacks are defined, classified, and their methodologies are discussed. Adversarial defenses are also introduced, including techniques such as adversarial training, input preprocessing, model regularization, and defensive distillation. The paper also presents simulation results comparing adversarial attacks with jamming attacks. The study highlights the challenges and open issues in adversarial attacks and defenses in 6G network-assisted IoT systems, including data transmission attacks, channel estimation attacks, and centralized and distributed attacks. The paper concludes with a discussion on the importance of robust defenses against adversarial attacks in 6G network-assisted IoT systems.This paper surveys adversarial attacks and defenses in 6G network-assisted IoT systems. The Internet of Things (IoT) and massive IoT systems are key to 6G networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems. The paper is organized into sections covering the overview of IoT networks, adversarial attacks and defenses, and their application in 6G network-assisted IoT systems. Adversarial attacks are defined, classified, and their methodologies are discussed. Adversarial defenses are also introduced, including techniques such as adversarial training, input preprocessing, model regularization, and defensive distillation. The paper also presents simulation results comparing adversarial attacks with jamming attacks. The study highlights the challenges and open issues in adversarial attacks and defenses in 6G network-assisted IoT systems, including data transmission attacks, channel estimation attacks, and centralized and distributed attacks. The paper concludes with a discussion on the importance of robust defenses against adversarial attacks in 6G network-assisted IoT systems.