Deep learning (DL)-based channel estimation models are capable of processing large-scale wireless data and have shown strong performance in modeling complex reconfigurable intelligent surface (RIS) channels. However, their vulnerability to adversarial attacks remains underexplored. To address this gap, this paper analyzes vulnerabilities in DL-based channel estimation models for RIS-assisted communications. We propose a novel adaptive adversarial training framework based on projected gradient descent (PGD) as an effective defense strategy learning from large-scale data. Unlike standard adversarial training, the proposed framework employs a progressive adversarial schedule that incrementally increases both perturbation strength and PGD iteration-depth during training. In addition, a balanced composition of clean and adversarial samples is maintained within each mini-batch, preserving baseline accuracy while systematically enhancing robustness against strong adversarial attacks. Extensive simulations are conducted to evaluate our proposed strategy compared with other existing defense techniques. The results show that our approach significantly enhances the robustness of DL-based channel estimation models against adversarial attacks.

Intelligent Reflecting Surface (IRS) has been demonstrated as a promising and innovative technology for next-generation wireless communications. It can be utilized to flexibly re-configure the fundamental communication environment to realize low-cost, energy-saving, and low-interference wireless communications. On the other hand, malicious users may also utilize the powerful capability of the IRS to re-configure the communication environment to achieve an advantageous position to launch security attacks such as eavesdropping and jamming wireless networks. Therefore, while the integration of IRS into wireless communications brings promising new opportunities, it also raises significant concerns from the security perspective. This issue has not been thoroughly studied in the literature. In this paper, we first introduce the recent works of using IRS in wireless communications by grouping them into two categories: 1) securing communication via IRS and 2) launching attacks using IRS. We then derive a critical performance metric, the Signal-to-Noise Ratio (SNR), for evaluating IRS-assisted wireless communication systems. Next, we present four typical scenarios of utilizing IRS for security or threats to wireless communications. At last, we evaluate the IRS-assisted system with regard to the SNR performance affected by the IRS in those four scenarios toward a deeper understanding of the potential of IRS-assisted wireless communication systems in terms of security and threats.