With the growing deployment of sequential recommender systems in e-commerce and other fields, their black-box interfaces raise security concerns: models are vulnerable to extraction and subsequent adversarial manipulation. Existing black-box extraction attacks primarily rely on hard labels or pairwise learning, often ignoring the importance of ranking positions, which results in incomplete knowledge transfer. Moreover, adversarial sequences generated via pure gradient methods lack semantic consistency with real user behavior, making them easily detectable. To overcome these limitations, this paper proposes a dual-enhanced attack framework. First, drawing on primacy effects and position bias, we introduce a cognitive distribution-driven extraction mechanism that maps discrete rankings into continuous value distributions with position-aware decay, thereby advancing from order alignment to cognitive distribution alignment. Second, we design a behavior-aware noisy item generation strategy that jointly optimizes collaborative signals and gradient signals. This ensures both semantic coherence and statistical stealth while effectively promoting target item rankings. Extensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods in both attack success rate and evasion rate, validating the value of integrating cognitive modeling and behavioral consistency for secure recommender systems.

翻译：随着序列推荐系统在电子商务等领域的广泛应用，其黑盒接口引发了安全担忧：模型易受提取攻击及后续对抗性操纵。现有黑盒提取攻击主要依赖硬标签或成对学习，往往忽略排序位置的重要性，导致知识转移不完整。此外，基于纯梯度方法生成的对抗序列缺乏与真实用户行为的语义一致性，使其易于被检测。为克服这些局限，本文提出一种双重增强的攻击框架。首先，借鉴首因效应与位置偏差，我们引入认知分布驱动的提取机制，将离散排序映射为具有位置感知衰减的连续值分布，从而实现了从顺序对齐到认知分布对齐的演进。其次，我们设计了一种行为感知的噪声项目生成策略，联合优化协同信号与梯度信号。这既保证了语义连贯性与统计隐蔽性，又能有效提升目标项目排序。在多个数据集上的大量实验表明，我们的方法在攻击成功率和规避率上均显著优于现有方法，验证了认知建模与行为一致性融合对于安全推荐系统的价值。