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Illusions of Relevance: Using Content Injection Attacks to Deceive Retrievers, Rerankers, and LLM Judges

Consider a scenario in which a user searches for information, only to encounter texts flooded with misleading or non-relevant content. This scenario exemplifies a simple yet potent vulnerability in neural Information Retrieval (IR) pipelines: content injection attacks. We find that embedding models for retrieval, rerankers, and large language model (LLM) relevance judges are vulnerable to these attacks, in which adversaries insert misleading text into passages to manipulate model judgements. We identify two primary threats: (1) inserting unrelated or harmful content within passages that still appear deceptively "relevant", and (2) inserting entire queries or key query terms into passages to boost their perceived relevance. While the second tactic has been explored in prior research, we present, to our knowledge, the first empirical analysis of the first threat, demonstrating how state-of-the-art models can be easily misled. Our study systematically examines the factors that influence an attack's success, such as the placement of injected content and the balance between relevant and non-relevant material. Additionally, we explore various defense strategies, including adversarial passage classifiers, retriever fine-tuning to discount manipulated content, and prompting LLM judges to adopt a more cautious approach. However, we find that these countermeasures often involve trade-offs, sacrificing effectiveness for attack robustness and sometimes penalizing legitimate documents in the process. Our findings highlight the need for stronger defenses against these evolving adversarial strategies to maintain the trustworthiness of IR systems. We release our code and scripts to facilitate further research.

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