Language Model Security Database

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…

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…

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…

Contrastive Language-Image Pre-training (CLIP) models are vulnerable to semantic-ensemble adversarial attacks. Current adversarial fine-tuning defenses for CLIP rely on minimizing the cosine similarity between an image and a single hand-crafted template (e.g., "A photo of a {label}"). This creates a vulnerability where adversarial examples (AEs) overfit to specific phrasings rather than the core class semantics. Attackers can bypass these defenses by generating semantic-aware adversarial…

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…

Vision Language Models (VLMs) utilizing independent vision encoders (e.g., ViT) and Large Language Model (LLM) decoders are vulnerable to Split-Image Visual Jailbreak Attacks (SIVA). The vulnerability arises from an architectural and alignment discrepancy: while the vision encoder processes image fragments (splits) in isolation via constrained attention or block-diagonal masks, the LLM decoder aggregates these features via cross-attention to reconstruct the semantic content. Current safety…

Text scoring models, including dense retrievers, rerankers, and reward models, are vulnerable to score manipulation attacks via search-based discrete perturbations and content injection. An attacker can systematically modify candidate texts using rudimentary string manipulations, gradient-guided token swaps (e.g., HotFlip), masked language modeling (MLM) swaps, or query/sentence injections to spuriously increase model scores. This structural failure condition allows an irrelevant passage or a…

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…

A vulnerability exists in Large Vision-Language Models (LVLMs) utilizing visual token compression mechanisms (e.g., VisionZip, VisPruner) to reduce inference latency. The vulnerability stems from an optimization-inference mismatch where standard adversarial defenses assume full-token processing, while the deployed model utilizes a subset of tokens selected via importance metrics (typically attention scores).

Neural Ranking Models (NRMs) utilizing Transformer architectures (specifically BERT and T5-based re-rankers) are vulnerable to minimal adversarial perturbations that artificially promote a target document's rank. The vulnerability allows an attacker to manipulate ranking outcomes by inserting or substituting a single "query center" token—a word identified as the semantic centroid of the user's query—into the target document. The attack exploits the model's sensitivity to specific semantic…