Last analyzed Sep 9, 2026

Language Model Security Database

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

985 research findings · 1119 evaluated models

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

Document reconstruction from exposed retrieval embeddings

Published Sep 4, 2026Analyzed Sep 9, 2026Source: arXiv

Exposed retrieval embeddings can reveal source text. SHAQ evaluates indexing generated queries instead of direct document embeddings to reduce that leakage.

Shadow Queries for Private Retrieval in Vector Databases

Evaluated models: QwQ 32B

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

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.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.