Skip to main content
LLM Security Database
Skip to research search
Last analyzed 9/9/2026

Language Model Security Database

985 research findings · 1123 evaluated models

Filtered research findings

1 entry

Matches every word across titles, descriptions, sources, affected systems, and models.

Zero-Training Cross-Domain Inversion

Published 2/1/2026
Analyzed 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
Evaluated models: Not reported

Source: arXiv

Model layer
Extraction
RAG
Embedding

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.