Language Model Security Database

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…

A vulnerability exists in the self-reflection and introspection capabilities of Large Language Models (LLMs) and Vision-LLMs that allows attackers to perform black-box adversarial optimization using only textual model responses. This technique, termed "Asking for Directions" (AfD), bypasses the need for access to gradients, logits, or continuous confidence scores. The attacker employs a hill-climbing optimization strategy where they present the target model with two candidate inputs (an…

Agentic AI browsers and LLM-powered browser extensions are vulnerable to indirect prompt injection via the processing of untrusted web content. The vulnerability arises when the AI agent ingests the Document Object Model (DOM), including hidden elements, HTML comments, metadata, and accessibility labels, into its context window to perform tasks such as page summarization or autonomous navigation. Because the LLM cannot distinguish between system instructions and untrusted external data, an…

Graph-LLMs (Graph Neural Networks integrated with Large Language Models) utilized for representation learning on Text-Attributed Graphs (TAGs) are vulnerable to the Interpretable Multi-Dimensional Graph Attack (IMDGA). This vulnerability exists due to the non-decoupled nature of text encoding and graph message passing mechanisms. A black-box attacker can manipulate node classification predictions by executing a three-stage attack: (1) utilizing a word-level Topological SHAP module to identify…

Large Vision-Language Model (LVLM) driven mobile agents, such as Mobile-Agent-E, are vulnerable to a touch-guided visual prompt injection attack. This vulnerability allows an attacker to hijack the agent's execution flow via a malicious Android application interface without requiring system-level privileges. The attack leverages "Non-privileged Perception Compromise," where a visual payload is embedded in the application UI and conditionally rendered only during agent-specific interaction…

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…

Large Language Models (LLMs), including proprietary and open-weight state-of-the-art systems, are vulnerable to automated, self-evolving adversarial attacks orchestrated by multi-agent frameworks. The vulnerability exists because current safety alignment strategies (RLHF, static safety filters) fail to generalize against the "SafeEvalAgent" attack vector. In this vector, an "Analyst" agent analyzes model refusals to iteratively refine attack strategies, while a "Specialist" agent grounds these…

Large Language Model (LLM)-powered GUI agents exhibit a vulnerability to deceptive interface designs (dark patterns) due to goal-driven optimization and procedural myopia. When executing natural language instructions on web interfaces, these agents consistently prioritize minimizing steps and achieving task completion over user safety or privacy. Agents frequently recognize manipulative elements—such as pre-selected consent checkboxes, hidden costs, or trick questions—in their internal…

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…

Large Language Models (LLMs) and Vision-Language Models (VLMs) are vulnerable to an automated, adaptive role-play jailbreak attack known as GUARD (Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics). The vulnerability exists because the models fail to recognize malicious intent when harmful queries are embedded within complex, iteratively optimized "playing scenarios."