  This study investigates the privacy risks associated with text embeddings,
focusing on the scenario where attackers cannot access the original embedding
model. Contrary to previous research requiring direct model access, we explore
a more realistic threat model by developing a transfer attack method. This
approach uses a surrogate model to mimic the victim model's behavior, allowing
the attacker to infer sensitive information from text embeddings without direct
access. Our experiments across various embedding models and a clinical dataset
demonstrate that our transfer attack significantly outperforms traditional
methods, revealing the potential privacy vulnerabilities in embedding
technologies and emphasizing the need for enhanced security measures.
