Language Model Security Database

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

A data poisoning attack, termed ImgTrojan, allows adversaries to bypass safety mechanisms in Vision-Language Models (VLMs) by injecting a small number of maliciously crafted image-caption pairs into the training dataset. These poisoned pairs associate seemingly benign images with jailbreak prompts, causing the VLM to generate unsafe outputs when presented with the poisoned images at inference time. The attack's success rate is notably high even with a very low poison ratio (e.g., one poisoned…