Retrieval-Augmented Generation (RAG) systems are vulnerable to knowledge corruption attacks, where adversaries inject malicious texts into external corpora to manipulate generated responses. Existing one-to-many attacks can compromise multiple queries with a small number of injected texts, but they typically require white-box access to the target retriever for gradient-based optimization. In contrast, existing black-box attacks are more practical but usually allow each malicious text to target only one query. In this paper, we study one-to-many knowledge corruption attacks against strictly black-box RAG systems, where the attacker has no access to the target retriever, generator, or corpus contents. We propose CoverPoison, a black-box method that combines competitive similarity clustering, coverage-oriented optimization, and T5-based text refinement. Competitive similarity clustering improves query grouping quality, while the coverage-oriented objective encourages each candidate to cover more queries through query-specific retrieval thresholds. Experiments on three datasets show that CoverPoison substantially improves black-box attack effectiveness. Further analyses across RAG configurations, attack objectives, and defenses validate the effectiveness of the proposed method.