Language Model Security Database

Graph-LLMs (Graph Neural Networks integrated with Large Language Models) utilized for representation learning on Text-Attributed Graphs (TAGs) are vulnerable to the Interpretable Multi-Dimensional Graph Attack (IMDGA). This vulnerability exists due to the non-decoupled nature of text encoding and graph message passing mechanisms. A black-box attacker can manipulate node classification predictions by executing a three-stage attack: (1) utilizing a word-level Topological SHAP module to identify…

Large Language Models (LLMs) that use special tokens to define conversational structure (e.g., via chat templates) are vulnerable to a jailbreak attack named MetaBreak. An attacker can inject these special tokens, or regular tokens with high semantic similarity in the embedding space, into a user prompt. This manipulation allows the attacker to bypass the model's internal safety alignment and external content moderation systems. The attack leverages four primitives: 1. Response Injection…

Large Language Model (LLM) inference APIs that expose top-k logits or log-probabilities are vulnerable to model extraction and cloning. An attacker can execute a two-stage attack to replicate the proprietary model without access to weights, gradients, or training data. First, by submitting fewer than 10,000 random queries and aggregating the returned unrounded logits, the attacker recovers the model's output projection matrix using Singular Value Decomposition (SVD). Second, the attacker…

A vulnerability exists in Large Language Models (LLMs) and multi-label text classification systems that allows for Textual Dynamic Outputs Attacks (TDOA). This technique enables hard-label black-box attacks against systems with variable or generative output spaces (where the number of labels or specific label tokens are not fixed). The attack functions by training a surrogate model on clustered coarse-grained labels derived from the victim model's fine-grained dynamic outputs. It subsequently…

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 utilizing dense (e.g., BERT-based) or sparse (e.g., BM25) retrievers are vulnerable to black-box adversarial prompt injection attacks. By employing a gradient-free Differential Evolution (DE) optimization algorithm (referred to as DeRAG), an attacker can generate short adversarial suffixes (typically ≤ 5 tokens). When these suffixes are appended to a user query, they manipulate the retriever's ranking mechanism to promote a specific, malicious, or…

Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) prompting are vulnerable to input perturbations that decouple intermediate reasoning from the final answer. An attacker can generate adversarial examples using gradient-based optimization (targeting specific loss functions that maximize reasoning divergence while minimizing answer loss) to induce "Right Answer, Wrong Reasoning" behaviors. This vulnerability manifests through two primary attack vectors: 1. Token-level perturbations…

A vulnerability exists in Vision-Language Models (VLLMs) that allows for transferable, targeted adversarial attacks. Attackers can generate adversarial image perturbations using an ensemble of open-source surrogate models (primarily CLIP-based visual encoders) which effectively transfer to proprietary, black-box VLLMs. The attack leverages a specific optimization framework that combines a Visual Contrastive Loss with multiple positive/negative visual examples, rather than relying solely on…

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.