Updated 7/21/2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

959 research findings · 1077 evaluated models

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Zero-Training Cross-Domain Inversion

Updated 2/22/2026

A cryptographic weakness exists in the privacy assumptions of vector embeddings used in Retrieval-Augmented Generation (RAG) systems and Vector Databases. The vulnerability, designated "Zero2Text," allows an unauthenticated attacker to reconstruct raw text from captured vector embeddings without access to the victim model's parameters, gradients, or training data. Unlike prior embedding inversion attacks that require training large decoders on domain-specific datasets, this vulnerability…

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

Source: arXiv

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.