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…

Conversational Large Language Model (LLM) agents integrated with privileged data sources (e.g., medical records, organizational emails) are vulnerable to contextually inappropriate information disclosure due to failures in enforcing Contextual Integrity (CI) norms. Standard semantic input/output filters and generic safety guardrails (e.g., Llama Guard) fail to detect "mosaic attacks" and multi-turn conversational manipulation. In these attacks, adversaries decompose a malicious query into a…

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…

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) 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…

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.