A Cognitive Distribution and Behavior-Consistent Framework for Black-Box Attacks on Recommender Systems

TL;DR

This paper introduces a novel black-box attack framework on recommender systems that leverages cognitive and behavioral modeling to improve attack success and stealth, addressing limitations of existing methods.

Contribution

It proposes a dual-enhanced attack framework combining cognitive distribution-driven extraction and behavior-aware item generation, advancing the state-of-the-art in black-box attacks.

Findings

Significantly higher attack success rates compared to existing methods.

Improved evasion rates demonstrating stealthiness.

Effective in multiple datasets, validating robustness.

Abstract

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.