Language Model Security Database

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 offline multi-armed bandit (MAB) evaluation frameworks allows attackers to completely hijack arm selection by applying imperceptibly small adversarial perturbations to the weights of the underlying reward model before training begins. Because the required perturbation norm decreases as the input dimensionality increases ($\mathcal{O}(d^{-1/2})$), high-dimensional reward models—such as wide neural networks or linear models used for generative model assessment—are highly…

Large Vision-Language Models (VLMs) are vulnerable to a transferable targeted adversarial attack known as SGHA-Attack (Semantic-Guided Hierarchical Alignment). This vulnerability arises from the susceptibility of visual encoders (specifically Vision Transformers) to intermediate-layer feature manipulation optimized on a surrogate model (e.g., CLIP). An attacker can craft adversarial images by injecting imperceptible perturbations that enforce semantic consistency with a target text prompt…

Closed-source Multi-modal Large Language Models (MLLMs) are vulnerable to Universal Targeted Transferable Adversarial Attacks (UTTAA). An attacker can generate a single, image-agnostic adversarial perturbation ($\delta$) that, when added to any arbitrary source image, steers the victim model to output a description or classification matching a specific target image chosen by the attacker. This vulnerability exploits the transferability of adversarial features from open-source surrogate vision…

The paper reports a black-box jailbreak evaluation in which a ReAct-style loop adaptively rewrites unsafe text prompts and selectively applies blur, DCT filtering, or recoloring to image regions identified as safety-sensitive. The combined cross-modal strategy is intended to make harmful image-text requests appear less objectionable to a vision-language model while preserving enough semantics to elicit an answer. This is a specific, security-relevant evaluation, although the reported results…

Multi-modal Large Language Models (MLLMs) capable of processing interleaved image-text sequences are vulnerable to a universal adversarial perturbation (UAP) attack known as LAMP. This vulnerability allows an attacker to generate a single, noise-based perturbation pattern using a surrogate model (e.g., Mantis-CLIP) that transfers effectively to black-box target models. The attack leverages two novel loss functions during perturbation learning: a "contagious" objective that manipulates…

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).

A vulnerability exists in multiple state-of-the-art Vision-Language Models (VLMs), including GPT-4o, Gemini-2.5, and LLaVA-OneVision, where persuasive textual misinformation successfully overrides visual evidence. When a model is presented with an image it can correctly interpret, an attacker can inject a contradictory text prompt employing specific rhetorical strategies (Logical, Credibility, Emotional, or Repetition) to force the model into generating a false response. This "obedience bias"…

Vision-Language Models (VLMs) exhibit a vulnerability to moral judgment flipping, where the model's safety alignment can be bypassed through lightweight, model-agnostic multimodal perturbations. By introducing conflicting textual or visual cues that do not alter the underlying moral context of a scenario, an attacker can coerce the model into reversing its ethical stance (e.g., reclassifying a harmful action from "morally wrong" to "not morally wrong"). This vulnerability exploits the model's…

The paper describes a specific black-box jailbreak evaluation, BVS, in which fragmented visual content is mixed with neutral imagery and paired with reconstruction-oriented text so harmful intent is only recomposed during multimodal reasoning. The authors report that this can bypass input and output safety assumptions in image-generating MLLMs. A safe defensive reproduction should use synthetic, non-harmful stand-ins for prohibited concepts, test whether fragmented cross-modal inputs are…