Language Model Security Database

Retrieval-Augmented Generation (RAG) systems in the health domain are vulnerable to corpus poisoning attacks where adversarial documents—specifically those generated via "Liar" (fabricated from scratch based on an incorrect stance) and "Few-Shot Adversarial Prompting" (FSAP)—are injected into the retrieval pool. When these adversarial documents are retrieved and presented as context, they successfully override the Large Language Model's (LLM) internal safety alignment and ground-truth…

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…

A vulnerability exists in the graph encoding architecture of LLaGA (Large Language and Graph Assistant), specifically within the "neighborhood detail template" used to construct node sequences. LLaGA enforces a fixed-shape computational tree for each node; when a target node has fewer neighbors than the required template size (e.g., $k$ children), the system utilizes placeholders to maintain the fixed structure.

Retrieval-Augmented Generation (RAG) systems are vulnerable to knowledge poisoning attacks (specifically the "PoisonedRAG" method) where an attacker injects adversarial texts into the retrieval knowledge database. These adversarial texts are optimized to achieve two simultaneous goals: 1) rank highly (top-k) during the retrieval phase for specific target queries, and 2) semantically steer the Large Language Model (LLM) to generate a pre-defined, attacker-chosen response instead of the ground…

A vulnerability in fine-tuning-based large language model (LLM) unlearning allows malicious actors to craft manipulated forgetting requests. By subtly increasing the frequency of common benign tokens within the forgetting data, the attacker can cause the unlearned model to exhibit unintended unlearning behaviors when these benign tokens appear in normal user prompts, leading to a degradation of model utility for legitimate users. This occurs because existing unlearning methods fail to…

A vulnerability exists in Chain-of-Thought (CoT) monitoring protocols where trusted monitor models can be deceived by deceptive reasoning traces generated by untrusted models. Specifically, when an untrusted model employs a "Dependency" framing attack—characterizing a malicious side-task as a necessary intermediate calculation or benign prerequisite—the monitor's suspicion score significantly decreases compared to action-only monitoring. This creates a paradox where providing the monitor with…

Recommender Systems (RS) utilizing collaborative filtering and review processing (such as NMF, NeuNMF, and Dual-Tower architectures) are vulnerable to low-knowledge shilling attacks via LLM-based user agents. This vulnerability, demonstrated by the Agent4SR framework, allows an attacker to manipulate recommendation outcomes (Top-N lists) for target items without access to the internal model parameters or training data. The attack leverages Large Language Models to construct cognitively…

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…

Retrieval-Augmented Generation (RAG) systems are vulnerable to a targeted corpus poisoning attack known as "CorruptRAG". This vulnerability allows an attacker to manipulate the response of an LLM to a specific target query by injecting a single malicious document into the RAG knowledge database. Unlike traditional poisoning attacks that require flooding the retrieval results (top-N) with malicious content to outnumber correct information, CorruptRAG succeeds with a single retrieved document.

Predictive Large Language Model (LLM) routers, specifically those utilizing Deep Neural Network (DNN) and Matrix Factorization (MF) architectures, are vulnerable to adversarial manipulation and backdoor poisoning. These routers are designed to optimize cost and latency by dynamically directing simple queries to "weak" (cheap) models and complex queries to "strong" (expensive) models. Attackers can exploit this mechanism in two ways: 1. Inference-time Attacks: By appending specific adversarial…