Language Model Security Database

Multi-agent Large Language Model (LLM) systems are vulnerable to compositional privacy leakage, a flaw where sensitive information is exposed through the aggregation of individually benign responses from distinct agents. In distributed architectures where data is siloed (e.g., distinct agents handling HR, Finance, and IT logs), individual agents lack a global view of the user’s accumulated knowledge or the sensitive attributes derivable from cross-agent data combinations. An attacker can…

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

LLM-powered agentic systems that use external tools are vulnerable to prompt injection attacks that cause them to bypass their explicit policy instructions. The vulnerability can be exploited through both direct user interaction and indirect injection, where malicious instructions are embedded in external data sources processed by the agent (e.g., documents, API responses, webpages). These attacks cause agents to perform prohibited actions, leak confidential data, and adopt unauthorized…

Large Language Model (LLM) systems integrated with private enterprise data, such as those using Retrieval-Augmented Generation (RAG), are vulnerable to multi-stage prompt inference attacks. An attacker can use a sequence of individually benign-looking queries to incrementally extract confidential information from the LLM's context. Each query appears innocuous in isolation, bypassing safety filters designed to block single malicious prompts. By chaining these queries, the attacker can…

Large Language Model (LLM) agents operating in stateful environments (web browsers, operating systems, and tool-use contexts) are vulnerable to indirect prompt injection and multi-modal adversarial attacks. These vulnerabilities arise when agents process untrusted environmental observations—such as web accessibility trees, screen screenshots, or database query results—that contain concealed malicious instructions. Specifically, attackers can embed prompt injections into HTML accessibility…

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.

Commercial LLM-powered agents utilizing autonomous web access, memory modules, and retrieval-augmented generation (RAG) are vulnerable to indirect prompt injection and environmental manipulation. Attackers can embed malicious instructions into external data sources trusted by the agent (such as Reddit posts, public databases, or ArXiv papers). When the agent autonomously retrieves and processes this content during task execution, it executes the embedded malicious commands. This vulnerability…

Jailbreaking vulnerabilities in Large Language Models (LLMs) used in Retrieval-Augmented Generation (RAG) systems allow escalation of attacks from entity extraction to full document extraction and enable the propagation of self-replicating malicious prompts ("worms") within interconnected RAG applications. Exploitation leverages prompt injection to force the LLM to return retrieved documents or execute malicious actions specified within the prompt.