E-commerce dispute resolution typically relies on the security assumption that digital evidence truthfully reflects physical reality. Generative AI (GenAI) invalidates this threat model, enabling attackers to fabricate hyper-realistic evidence of product defects at negligible cost. Through semi-structured interviews with merchants (N=17) and platform workers (N=13) in the Chinese e-commerce market, we characterize this shift toward GenAI-enabled scalable fabrication. We outline a taxonomy of four GenAI-enabled threat vectors across the transaction, dispute, logistics and communication phases, highlighting how attackers exploit GenAI to synthesize physically plausible product defects at scale. To mitigate these threats, platforms and merchants are adapting verification strategies, relying on AI tools for automated screening and adversarial interrogation (e.g., requesting multi-angle videos) to increase attack complexity. However, we find several challenges that hinder the adoption of these defenses, including implementation hurdles like structural platform constraints and fundamental limitations regarding the technical sophistication of GenAI. We conclude by outlining design implications for privacy-preserving cross-platform fraud databases, and traceability mechanisms such as embedding verifiable material anchors into the product.

翻译：电商纠纷解决通常基于数字证据真实反映物理现实的安全假设。生成式人工智能（GenAI）颠覆了这一威胁模型，使攻击者能够以极低成本伪造超逼真的产品缺陷证据。通过对中国电商市场中的商家（N=17）与平台从业者（N=13）进行半结构化访谈，我们刻画了向GenAI驱动的可规模化伪造的转变。我们提出了一种涵盖交易、纠纷、物流与通信四个环节的GenAI威胁向量分类体系，揭示了攻击者如何利用GenAI大规模合成物理上可信的产品缺陷。为应对这些威胁，平台与商家正在调整验证策略，依赖AI工具进行自动化筛查与对抗式质询（例如要求提供多角度视频）以增加攻击复杂度。然而，我们发现多项阻碍这些防御措施落地的挑战，包括结构性平台限制等实施障碍，以及GenAI技术复杂度带来的根本性局限。我们最后总结了隐私保护型跨平台欺诈数据库的设计启示，以及将可验证物理锚点嵌入产品的可追溯性机制。