Language Model Security Database

A vulnerability in Infrared Vision-Language Models (IR-VLMs) allows attackers to systematically degrade open-ended semantic understanding—compromising classification, captioning, and Visual Question Answering (VQA)—via a physically deployable Universal Curved-Grid Patch (UCGP). Instead of manipulating explicit text labels, the attack disrupts the clean-category manifold in the model's visual representation space by maximizing orthogonal deviation energy from the principal subspace and forcing…

An evasion vulnerability in Text-Attributed Graph (TAG) learning models allows attackers to induce targeted misclassifications via LLM-generated, coordinated perturbations to both graph topology and textual semantics. By identifying a semantically distant "influencer" node, an attacker can use a separate LLM to selectively delete highly relevant edges, insert a deceptive edge connecting the target to the influencer, and slightly modify the target node's text to include a keyword aligned with…

LLaVA-v1.5-7B, when deployed as a vision-language autonomous agent, is highly vulnerable to adversarial image perturbations. An attacker can inject imperceptibly modified images into a web environment (such as an e-commerce storefront). When the VLM agent captures a screenshot containing the perturbed image, the visual noise forces the model to misclassify the scene and output incorrect, structured JSON actions. This allows an attacker to hijack the agent's task execution, bypassing the user's…

A vulnerability in Vision-Language Models (VLMs) relying on shared visual-textual representation spaces allows attackers to induce transferable cross-task semantic failures using an X-shaped Sparse Pixel Attack (XSPA). Attackers craft imperceptible adversarial perturbations restricted to a fixed geometric prior—two intersecting diagonal lines comprising approximately 1.76% of the image pixels. By jointly optimizing a classification objective with cross-task semantic guidance (target-semantic…

A vulnerability exists in the internal representation mechanisms of Transformer-based Large Language Models (LLMs), specifically Llama-3 and Qwen series models. The vulnerability allows for high-accuracy "steering" of model outputs, effectively bypassing safety guardrails and refusal mechanisms (jailbreaking) without modifying model weights. By exploiting attention-guided feature learning, an attacker can extract a precise "concept vector" representing refusal behaviors. This is achieved by…

Discrete image tokenizers are vulnerable to unsupervised embedding-space adversarial attacks. Attackers can apply $\ell_p$-bounded perturbations to an input image to maximize the $\ell_2$ distance of the pre-quantization continuous embeddings produced by the tokenizer's vision encoder. This forces the vector quantizer to cross discrete cell boundaries and assign incorrect codebook vectors, fundamentally altering the resulting token sequence. Because the attack targets the pre-quantization…

A vulnerability exists in Large Language Model (LLM) context compression architectures (specifically compressor-decoder setups) characterized as the "Size-Fidelity Paradox." When scaling up the parameter count of the compressor model (e.g., beyond 4B parameters in Qwen-3 and LLaMA-3.2 families), the system exhibits a degradation in faithful preservation of the source text, despite improvements in standard training loss and perplexity metrics. This degradation manifests through two primary…

Graph Neural Network (GNN)-based social bot detection systems are vulnerable to an Optimal Transport (OT)-guided evasion attack that manipulates local graph structures under realistic domain constraints. By modeling $k$-hop ego-neighborhoods as probability measures over spatio-temporal features, an attacker can compute an optimal transport plan to identify "cloak templates" (existing bots near the decision boundary that are misclassified as humans). The attacker can then decode this plan into…

Large Vision-Language Models (LVLMs) are vulnerable to a Stage-wise Attention-Guided Attack (SAGA) that allows for the generation of highly transferable, imperceptible adversarial examples. The vulnerability stems from a positive correlation between regional cross-modal attention scores and adversarial loss sensitivity in LVLMs. An attacker can exploit this by extracting an attention map from a surrogate open-source model (e.g., Qwen3-VL) to identify high-attention "hotspots." SAGA utilizes a…

A vulnerability in Large Language Model-based Retrieval (LLMR) systems allows attackers to intentionally hide specific documents from being retrieved (e.g., in RAG pipelines or search engines) by appending a small number of adversarially crafted, query-agnostic tokens. The attack operates in a complete black-box setting: it requires no knowledge of the victim's queries, the target retrieval model's parameters, or the underlying document corpus. By utilizing Document-Query Adversarial (DQ-A)…