Language Model Security Database

LLM-enhanced Graph Neural Networks (GNNs), which integrate Large Language Model (LLM) feature encoders with graph message-passing architectures, are vulnerable to a black-box node injection attack known as "GraphTextack." This vulnerability exists because the joint model architecture creates a dual attack surface: the GNN component is sensitive to structural perturbations (changes in graph topology), while the LLM component is sensitive to semantic perturbations (adversarial phrasing).

The KG-DF (Knowledge Graph Defense Framework) contains a logic vulnerability in its Semantic Parsing Module, specifically within the keyword extraction phase defined as $K_{core} = \text{LLM}(P_{prompt})$. The framework relies on a Large Language Model (e.g., GPT-3.5-turbo) to distill user input into keywords ($K_{core}$), which are then embedded to retrieve security warning triples ($T_{match}$) from a Knowledge Graph.

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…

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), specifically open-weights models such as Llama-2, Mistral, and Vicuna, are vulnerable to a white-box adversarial attack framework termed RAID (Refusal-Aware and Integrated Decoding). The vulnerability exists because the model's safety alignment relies on specific activation patterns ("refusal directions") in the intermediate embedding space. Attackers can exploit this by optimizing a continuous "relaxed" suffix in the embedding space using a triplet loss…

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…

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…

AdvEDM reveals a vulnerability in Vision-Language Model (VLM) based Embodied Decision-Making (EDM) systems, such as those used in autonomous driving and robotic manipulation. The vulnerability allows an attacker to launch fine-grained adversarial attacks that selectively modify the perception of specific objects in an input image—either by removing them (Semantic Removal) or adding them (Semantic Addition)—while preserving the semantic integrity of the rest of the scene.

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…

Multimodal Entity Linking (MEL) systems, encompassing both traditional dual-encoder models and Multimodal Large Language Models (MLLMs), are vulnerable to gradient-based white-box adversarial attacks. By applying imperceptible perturbations to visual inputs via Projected Gradient Descent (PGD), Auto-PGD (APGD), or Carlini & Wagner (CW) methods, an attacker can manipulate the visual embeddings generated by the model. This manipulation disrupts the cross-modal alignment structure, causing the…