The Internet of Things (IoT) systems increasingly depend on continual learning to adapt to non-stationary environments. These environments can include factors such as sensor drift, changing user behavior, device aging, and adversarial dynamics. Contrastive continual learning (CCL) combines contrastive representation learning with incremental adaptation, enabling robust feature reuse across tasks and domains. However, the geometric nature of contrastive objectives, when paired with replay-based rehearsal and stability-preserving regularization, introduces new security vulnerabilities. Notably, backdoor attacks can exploit embedding alignment and replay reinforcement, enabling the implantation of persistent malicious behaviors that endure through updates and deployment cycles. This paper provides a comprehensive analysis of backdoor attacks on CCL within IoT systems. We formalize the objectives of embedding-level attacks, examine persistence mechanisms unique to IoT deployments, and develop a layered taxonomy tailored to IoT. Additionally, we compare vulnerabilities across various learning paradigms and evaluate defense strategies under IoT constraints, including limited memory, edge computing, and federated aggregation. Our findings indicate that while CCL is effective for enhancing adaptive IoT intelligence, it may also elevate long-lived representation-level threats if not adequately secured.

翻译：物联网系统日益依赖持续学习来适应非平稳环境。这些环境可能包含传感器漂移、用户行为变化、设备老化及对抗性动态等因素。对比学习持续学习将对比表征学习与增量适应相结合，实现了跨任务和跨领域的鲁棒特征复用。然而，对比目标的几何特性与基于回放的重演机制及稳定性保持正则化相结合时，会引入新的安全漏洞。值得注意的是，后门攻击可利用嵌入对齐和回放强化机制，植入能够持续存在于更新与部署周期的持久性恶意行为。本文对物联网系统中CCL的后门攻击进行了全面分析。我们形式化定义了嵌入级攻击的目标，研究了物联网部署特有的持久性机制，并构建了面向物联网的分层分类体系。此外，我们比较了不同学习范式的脆弱性，评估了物联网约束条件下的防御策略，包括有限内存、边缘计算和联邦聚合等限制因素。研究结果表明，虽然CCL能有效增强物联网的自适应智能，但若未充分防护，也可能加剧长期存在的表征级安全威胁。