Language Model Security Database

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…

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"…

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).

Large Language and Vision Assistant (LLaVA) v1.5-13B and Meta Llama 3.2 11B Vision are vulnerable to adversarial evasion attacks targeting the visual input modality. An attacker with white-box access (knowledge of model architecture and gradients) can employ Projected Gradient Descent (PGD) to generate adversarial perturbations constrained by an L-infinity norm. By maximizing the model's internal loss function with respect to the input image, the attacker can force the Vision-Language Model…

Text-to-Video (T2V) diffusion models are vulnerable to black-box adversarial prompt attacks that degrade output quality regarding semantic fidelity and temporal dynamics. This vulnerability is exploited via the T2VAttack framework, which utilizes two primary vector strategies: T2VAttack-S (Substitution) and T2VAttack-I (Insertion). T2VAttack-S leverages a greedy search to identify key semantic tokens and replaces them with high-similarity synonyms defined in lexical databases (e.g., WordNet)…

The paper describes a reproducible black-box evaluation and attack framework, PolyJailbreak, for multimodal LLMs. It reports that uneven text-versus-vision safety alignment allows jointly optimized text and image inputs to bypass refusal behavior without model internals. The authors attribute this to visual alignment weakening textual refusal representations and to cross-modal fusion making harmful intent harder to separate from benign intent. These are paper-reported findings, not…

Multimodal agents built on Large Vision-Language Models (LVLMs) are vulnerable to adaptive typographic prompt injection attacks (AgentTypo). This vulnerability allows an attacker to execute indirect prompt injection by embedding adversarial text prompts directly into images (e.g., webpage screenshots, product photos) processed by the agent. Unlike standard visual adversarial attacks that rely on noise perturbation, this method utilizes the AgentTypo framework to perform black-box Bayesian…

Reasoning segmentation models, which generate binary segmentation masks based on implicit text queries, are vulnerable to adversarial paraphrasing. This vulnerability allows an attacker to craft semantically equivalent and grammatically correct text prompts that significantly degrade the model's segmentation performance (measured by Intersection-over-Union, or IoU). The exploit utilizes a black-box, sentence-level optimization method (SPARTA) that operates within the continuous semantic latent…

Large Language Models (LLMs), including GPT-4o, LLaMA-3, and GPT-3.5-Turbo, are vulnerable to multimodal prompt injection attacks. These models fail to distinguish between system-level instructions and user-provided content within the context window. Attackers can exploit this by embedding malicious instructions in direct text, indirect sources (such as third-party webpages or PDFs), or visual inputs (images). Successful exploitation results in the model prioritizing the injected adversarial…

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.