Language Model Security Database

Vision-Language-Action (VLA) models suffer from a severe linguistic fragility vulnerability where semantically equivalent but structurally complex adversarial instructions cause catastrophic failures in visual grounding and geometric reasoning. Attackers can reliably induce physical execution failures in robotic manipulation tasks by applying semantic-preserving linguistic variations, such as synonymous rephrasing, syntactic restructuring, or the addition of fine-grained compositional…

Vision-Language-Action (VLA) models are vulnerable to targeted, low-budget textual perturbations in their natural-language instruction inputs, which can maliciously alter sequential decision-making and downstream physical robotic behavior. Because VLA policies tightly couple language, perception, and control, bounded edits—such as character-level typos, token attribute swaps, or prompt-level uncertainty clauses—propagate through the model's execution trajectory. This allows a black-box…

Multimodal LLM-based phishing detection systems are vulnerable to indirect prompt injection via "perceptual asymmetry." Attackers can embed hidden instructions within a phishing site's HTML, CSS, URLs, or rendered images that remain imperceptible to human victims but are parsed and executed by the evaluating LLM. This vulnerability allows threat actors to manipulate the LLM's contextual understanding, forcing it to misclassify malicious sites as benign (Legitimate Pretexting), trigger safety…

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"…

Large Vision-Language Models (LVLMs) are vulnerable to Physical Prompt Injection Attacks (PPIA), a query-agnostic injection technique delivered via the visual modality. The vulnerability stems from the model's "Vision-Enabled Text Recognition" capabilities and "Identity Sensitivity," where the model interprets text embedded in the physical environment (e.g., printed on signs, posters, or objects) as high-priority instructions rather than passive visual data. An attacker can embed adversarial…

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)…

OpenVLA, a Vision-Language-Action (VLA) model, contains a vulnerability regarding multimodal adversarial robustness. The model lacks sufficient cross-modal alignment stability, allowing attackers to disrupt the grounding between visual perception and linguistic instructions. By utilizing the "VLA-Fool" framework, adversaries can inject perturbations via three vectors: (1) Semantically Greedy Coordinate Gradient (SGCG), which alters specific linguistic tokens (referential cues, attributes…

Embodied Artificial Intelligence (AI) agents utilizing Vision-Language Models (VLMs) for perception and planning are vulnerable to Indirect Environmental Jailbreak (IEJ). The vulnerability arises from the system's failure to distinguish between user-issued instructions and text embedded in the physical environment (e.g., writing on walls, sticky notes, or projections). The VLM processes visual text detected in the camera feed as authoritative context or direct commands, allowing a black-box…

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…