Architecture Components

Embedding Vulnerabilities

Security concerns in embedding models and vectors

5 matching entries out of 70 in this category

Embedding research results

5 entries

RAG Knowledgebase Exfiltration

Updated 2/21/2026

Retrieval-Augmented Generation (RAG) systems are vulnerable to iterative knowledge-extraction attacks designed to reconstruct the underlying private knowledge base. The vulnerability exists due to the decoupled optimization of the retrieval and generation phases. Attackers can craft adversarial queries consisting of two distinct components: an "Information" component (optimized via gradient descent or random sampling to steer embeddings toward specific, diverse regions of the vector space) and…

Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation

Affects: GPT-4o, Llama 3 8B, Qwen 2.5 7B

Source: arXiv

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

Latent Space Discontinuity Exploitation

Updated 12/5/2025

A vulnerability exists in certain Large Language Models and diffusion models due to discontinuities in their latent space, which arise from data sparsity during training. An attacker can craft inputs containing lexically rare or semantically ambiguous constructs to guide the model's inference process toward these unstable, poorly-conditioned regions. This technique, termed "Alignment Degradation Induction," can degrade or bypass safety alignment mechanisms. Through iterative, multi-turn…

Exploiting Latent Space Discontinuities for Building Universal LLM Jailbreaks and Data Extraction Attacks

Source: arXiv

Zero-Shot Embedding Leak

Updated 12/30/2025

A Universal Zero-shot Embedding Inversion vulnerability exists in vector databases and embedding-based retrieval systems. The flaw allows an attacker to reconstruct original plaintext documents from their vector embeddings without requiring access to the original training data or training an embedding-specific inversion model. The attack, identified as "ZSinvert," leverages a multi-stage adversarial decoding process: (1) a cosine-similarity guided beam search using a Large Language Model (LLM)…

Universal Zero-shot Embedding Inversion

Affects: Qwen 2 5B

Source: arXiv

Embedding Inversion Privacy Leak

Updated 12/30/2025

Retrieval-Augmented Generation (RAG) systems employing standard dense embedding models (e.g., Sentence-T5, SimCSE-BERT, RoBERTa, MPNet) for End-Cloud collaboration are vulnerable to Embedding Inversion Attacks (EIA). While embeddings are vector representations designed to be human-unrecognizable, they retain sufficient semantic information to allow an attacker with access to the vectors (e.g., a malicious or compromised cloud provider) to reconstruct the original sensitive plaintext input.

Safeguarding LLM Embeddings in End-Cloud Collaboration via Entropy-Driven Perturbation

Source: arXiv

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