Poisoning Attacks against Recommender Systems: A Survey (2401.01527v3)

Abstract: Modern recommender systems (RS) have seen substantial success, yet they remain vulnerable to malicious activities, notably poisoning attacks. These attacks involve injecting malicious data into the training datasets of RS, thereby compromising their integrity and manipulating recommendation outcomes for gaining illicit profits. This survey paper provides a systematic and up-to-date review of the research landscape on Poisoning Attacks against Recommendation (PAR). A novel and comprehensive taxonomy is proposed, categorizing existing PAR methodologies into three distinct categories: Component-Specific, Goal-Driven, and Capability Probing. For each category, we discuss its mechanism in detail, along with associated methods. Furthermore, this paper highlights potential future research avenues in this domain. Additionally, to facilitate and benchmark the empirical comparison of PAR, we introduce an open-source library, ARLib, which encompasses a comprehensive collection of PAR models and common datasets. The library is released at https://github.com/CoderWZW/ARLib.

• The paper introduces a detailed taxonomy of poisoning attack methods, categorizing them into Component-Specific, Goal-Driven, and Capability Probing attacks.
• It reviews methodologies for data manipulation in recommender systems and presents ARLib, an open-source toolkit for attack simulation and benchmarking.
• The survey synthesizes insights from 45 studies, outlining current challenges and future research directions to bolster AI resilience and security.

An Analytical Overview of "Poisoning Attacks against Recommender Systems: A Survey"

The paper "Poisoning Attacks against Recommender Systems: A Survey" by Zongwei Wang et al. presents a comprehensive investigation into the field of poisoning attacks against recommender systems (RS). These attacks entail the strategic injection of spoofed or misleading data into the training datasets of recommendation algorithms, with the goal of skewing outputs in favor of certain benefits. As recommender systems grow in complexity and usage, underscoring the significance of understanding such vulnerabilities becomes imperative for safeguarding their integrity.

Taxonomy and Classification

A unique contribution of this paper is its introduction of a thorough taxonomy which classifies poisoning attack methodologies into three distinct categories: Component-Specific, Goal-Driven, and Capability Probing attacks. Each category delineates differing attacker incentives and operational methodologies:

1. Component-Specific Attacks: These attacks exploit specific components of recommendation systems, including distinct data inputs, architectures, and loss functions. The delineation groups attacks into Input-Specific, Recommender-Specific, and Optimization-Specific sub-categories. It highlights how graph structures or temporal sequences and network architectures can introduce unique vulnerabilities, effectively exploited by adversaries under specific conditions.
2. Goal-Driven Attacks: This category considers the attacker's objectives—whether to degrade system-wide performance (system degradation attacks), manipulate the visibility or ranking of specific items (targeted manipulation attacks), or a hybrid approach seeking both aims simultaneously.
3. Capability Probing Attacks: Focusing on the practical realities that attackers face, this section of the survey considers knowledge constraints (e.g., black-box vs. white-box scenarios), financial/resource limitations, and the importance of remaining undetected. It demonstrates how constrained knowledge or budgets impact the formulation and execution of attacks.

Empirical Resources and Tools

To advance the research in this domain, the authors introduce ARLib, an open-source library designed to standardize and streamline empirical research on poisoning attacks. ARLib offers extensive resources for fast implementation, modularity that allows for easy integration with new strategies, and a range of datasets for benchmarking attacks against multiple types of recommendation systems.

Numerical Results and Insights

While this survey paper does not provide new empirical results itself, it brings significant insights by synthesizing information across 45 papers. This synthesis offers valuable quantitative evaluations and cross-comparison benchmarks that can guide future research directions. It underlines how attackers optimize for maximum disruption of RS functionality by manipulating inputs or system parameters — each attack vector offering distinct efficiency rates and success metrics.

Challenges and Future Directions

The paper concludes with a discussion on existing challenges and prospects for future research. These include the exploration of novel contexts in which RS might be vulnerable, understanding complex malicious intents beyond current paradigms, and the necessity for a robust theoretical foundation for attacks. Furthermore, it addresses the importance of long-term impact assessment and efficient neutralization strategies for potential disruptions caused by such attacks. This foresight is crucial as RS become more deeply integrated into user experiences and critical infrastructures.

Implications for AI Development

The broader implications of this survey stretch into areas of AI robustness and security, akin to developments in adversarial machine learning. Understanding and mitigating poisoning attacks will likely become more critical as systems increasingly rely on AI for personalized content delivery. This survey positions itself as a repository of knowledge and resource that will assist researchers and practitioners in understanding, identifying, and counteracting such threats, contributing to the overall resilience and trustworthiness of AI systems in applied scenarios.

By providing a structured overview of poisoning attacks and a practical toolbox for research, this paper contributes a vital resource poised to inform and influence the next generation of recommender systems and their defenses.