RobustMask: Certified Robustness against Adversarial Neural Ranking Attack via Randomized Masking

Neural ranking models have achieved remarkable progress and are now widely deployed in real-world applications such as Retrieval-Augmented Generation (RAG). However, like other neural architectures, they remain vulnerable to adversarial manipulations: subtle character-, word-, or phrase-level perturbations can poison retrieval results and artificially promote targeted candidates, undermining the integrity of search engines and downstream systems. Existing defenses either rely on heuristics with poor generalization or on certified methods that assume overly strong adversarial knowledge, limiting their practical use. To address these challenges, we propose RobustMask, a novel defense that combines the context-prediction capability of pretrained language models with a randomized masking-based smoothing mechanism. Our approach strengthens neural ranking models against adversarial perturbations at the character, word, and phrase levels. Leveraging both the pairwise comparison ability of ranking models and probabilistic statistical analysis, we provide a theoretical proof of RobustMask's certified top-K robustness. Extensive experiments further demonstrate that RobustMask successfully certifies over 20% of candidate documents within the top-10 ranking positions against adversarial perturbations affecting up to 30% of their content. These results highlight the effectiveness of RobustMask in enhancing the adversarial robustness of neural ranking models, marking a significant step toward providing stronger security guarantees for real-world retrieval systems.

Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models

Retrieval-Augmented Generation (RAG) systems based on Large Language Models (LLMs) have become essential for tasks such as question answering and content generation. However, their increasing impact on public opinion and information dissemination has made them a critical focus for security research due to inherent vulnerabilities. Previous studies have predominantly addressed attacks targeting factual or single-query manipulations. In this paper, we address a more practical scenario: topic-oriented adversarial opinion manipulation attacks on RAG models, where LLMs are required to reason and synthesize multiple perspectives, rendering them particularly susceptible to systematic knowledge poisoning. Specifically, we propose Topic-FlipRAG, a two-stage manipulation attack pipeline that strategically crafts adversarial perturbations to influence opinions across related queries. This approach combines traditional adversarial ranking attack techniques and leverages the extensive internal relevant knowledge and reasoning capabilities of LLMs to execute semantic-level perturbations. Experiments show that the proposed attacks effectively shift the opinion of the model's outputs on specific topics, significantly impacting user information perception. Current mitigation methods cannot effectively defend against such attacks, highlighting the necessity for enhanced safeguards for RAG systems, and offering crucial insights for LLM security research.

FlipedRAG: Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models

Retrieval-Augmented Generation (RAG) addresses hallucination and real-time constraints by dynamically retrieving relevant information from a knowledge database to supplement the LLMs' input. When presented with a query, RAG selects the most semantically similar texts from its knowledge bases and uses them as context for the LLMs to generate more accurate responses. RAG also creates a new attack surface, especially since RAG databases are frequently sourced from public domains. While existing studies have predominantly focused on optimizing RAG's performance and efficiency, emerging research has begun addressing the security concerns associated with RAG. However, these works have some limitations, typically focusing on either white-box methodologies or heuristic-based black-box attacks. Furthermore, prior research has mainly targeted simple factoid question answering, which is neither practically challenging nor resistant to correction. In this paper, we unveil a more realistic and threatening scenario: opinion manipulation for controversial topics against RAG. Particularly, we propose a novel RAG black-box attack method, termed FlipedRAG, which is transfer-based. By leveraging instruction engineering, we obtain partial retrieval model outputs from black-box RAG system, facilitating the training of surrogate models to enhance the effectiveness of opinion manipulation attack. Extensive experimental results confirms that our approach significantly enhances the average success rate of opinion manipulation by 16.7%. It achieves an average of a 50% directional change in the opinion polarity of RAG responses across four themes. Additionally, it induces a 20% shift in user cognition. Furthermore, we discuss the efficacy of potential defense mechanisms and conclude that they are insufficient in mitigating this type of attack, highlighting the urgent need to develop novel defensive strategies.