Impact

Data privacy Research

Security research involving user or training data privacy

2 matching entries out of 114 in this category

Data privacy research results

2 entries

RAG Knowledgebase Exfiltration

Published Feb 10, 2026Analyzed Feb 21, 2026Source: arXiv

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

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

Embedding Inversion Privacy Leak

Published Mar 17, 2025Analyzed Dec 30, 2025Source: arXiv

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

Evaluated models: Not reported

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