Language Model Security Database

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…

LLM agents integrating with external environments (e.g., via tool use, web retrieval, or RAG) are vulnerable to indirect prompt injection attacks. Malicious instructions embedded in untrusted data sources—such as emails, webpages, or tool outputs—are ingested by the agent and treated as valid context. Because the backend Large Language Model (LLM) struggles to distinguish between system instructions, user instructions, and third-party data, these embedded prompts can hijack the execution flow…

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…

A remote code execution (RCE) and privilege escalation vulnerability exists in Large Language Model (LLM) multi-agent systems and agentic RAG (Retrieval-Augmented Generation) architectures. The vulnerability arises from "Inter-Agent Trust Exploitation," where LLM agents implicitly trust instructions received from peer agents, bypassing safety guardrails and jailbreak defenses that are active during direct human-to-LLM interaction. An attacker can inject a malicious command payload (e.g., a…

A vulnerability exists in the tool selection mechanism of Large Language Model (LLM) agents that utilize a retrieval-then-selection pipeline (RAG) for identifying executable tools. The vulnerability, known as "ToolHijacker," allows a remote attacker to manipulate the agent's decision-making process by injecting a malicious tool document into the accessible tool library (e.g., via third-party tool hubs or plugins). The attack employs a two-phase optimization strategy to craft a malicious tool…

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…

Improper input validation in the memory module of Large Language Model (LLM)-powered agentic Recommender Systems (RS) allows remote attackers to perform indirect prompt injection via adversarial item descriptions. By utilizing the "DrunkAgent" framework, an attacker can embed semantic triggers and control characters (such as segmentation tokens and escape characters) into product descriptions. These injections manipulate the agent's memory update mechanism during agent-environment…

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…

Vision Language Models (VLMs) are vulnerable to visual prompt injection attacks via text-to-image obfuscation. While these models often possess safety guardrails for standard text-based inputs, they fail to apply equivalent safety alignment to textual instructions embedded visually within an image. An attacker can overlay malicious instructions (e.g., requests for illegal acts, hate speech) onto an image file and submit it to the model. The model’s Optical Character Recognition (OCR) or visual…

Large Language Model (LLM) tool-calling systems are vulnerable to adversarial tool injection attacks. Attackers can inject malicious tools ("Manipulator Tools") into the tool platform, manipulating the LLM's tool selection and execution process. This allows for privacy theft (extracting user queries), denial-of-service (DoS) attacks against legitimate tools, and unscheduled tool-calling (forcing the use of attacker-specified tools regardless of relevance). The attack exploits vulnerabilities…