Zero2Text: Zero-Training Cross-Domain Inversion Attacks on Textual Embeddings

• URL: http://arxiv.org/abs/2602.01757v2
• Date: Tue, 03 Feb 2026 04:22:32 GMT
• Title: Zero2Text: Zero-Training Cross-Domain Inversion Attacks on Textual Embeddings
• Authors: Doohyun Kim, Donghwa Kang, Kyungjae Lee, Hyeongboo Baek, Brent Byunghoon Kang,
• Abstract summary: We introduce Zero2Text, a training-free framework based on online alignment.<n>We prove that standard defenses, such as differential privacy, fail to effectively mitigate this adaptive threat.<n>It achieves 1.8x higher ROUGE-L and 6.4x higher BLEU-2 scores compared to baselines, recovering sentences from unknown domains without a single leaked data pair.
• Score: 6.483857896850984
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The proliferation of retrieval-augmented generation (RAG) has established vector databases as critical infrastructure, yet they introduce severe privacy risks via embedding inversion attacks. Existing paradigms face a fundamental trade-off: optimization-based methods require computationally prohibitive queries, while alignment-based approaches hinge on the unrealistic assumption of accessible in-domain training data. These constraints render them ineffective in strict black-box and cross-domain settings. To dismantle these barriers, we introduce Zero2Text, a novel training-free framework based on recursive online alignment. Unlike methods relying on static datasets, Zero2Text synergizes LLM priors with a dynamic ridge regression mechanism to iteratively align generation to the target embedding on-the-fly. We further demonstrate that standard defenses, such as differential privacy, fail to effectively mitigate this adaptive threat. Extensive experiments across diverse benchmarks validate Zero2Text; notably, on MS MARCO against the OpenAI victim model, it achieves 1.8x higher ROUGE-L and 6.4x higher BLEU-2 scores compared to baselines, recovering sentences from unknown domains without a single leaked data pair.

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