Language Model Security Database

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…

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…

Self-evolving Large Language Model (LLM) agents that utilize long-term memory mechanisms (such as Vector Databases for Retrieval-Augmented Generation or Sliding Window buffers) are vulnerable to persistent indirect prompt injection. This vulnerability, termed "Zombie Agent," occurs when the agent's memory update function ($F_M$) processes attacker-controlled content retrieved from external sources (e.g., web pages, documents) and commits it to long-term storage without sufficient sanitization…

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…

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)…

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.

A vulnerability in LLM-based agents, dubbed AI Agent Injection (AI²), allows attackers to hijack the agent's actions by manipulating the agent's memory retrieval mechanism. The attack involves two main steps: (1) Stealing action-aware knowledge from the agent's memory using crafted adversarial queries targeting the retriever module and (2) Generating Trojan prompts consisting of a Trojan string and hijacking instructions. The Trojan string is designed to manipulate the retriever into…