Language Model Security Database

Mobile LLM agents utilizing vision-based screen perception (OCR or Multimodal Large Language Models) are vulnerable to Visual Prompt Injection via malicious GUI overlays. An attacker holding the SYSTEM_ALERT_WINDOW permission can deploy non-focusable floating windows (using FLAG_NOT_FOCUSABLE) containing adversarial text or fabricated UI elements over legitimate applications. Because the agent captures the entire screen buffer to interpret the device state, it ingests the adversarial overlay…

A vulnerability exists in Vision-Language Models (VLLMs) that allows for transferable, targeted adversarial attacks. Attackers can generate adversarial image perturbations using an ensemble of open-source surrogate models (primarily CLIP-based visual encoders) which effectively transfer to proprietary, black-box VLLMs. The attack leverages a specific optimization framework that combines a Visual Contrastive Loss with multiple positive/negative visual examples, rather than relying solely on…

Large Language Model (LLM) agents operating in stateful environments (web browsers, operating systems, and tool-use contexts) are vulnerable to indirect prompt injection and multi-modal adversarial attacks. These vulnerabilities arise when agents process untrusted environmental observations—such as web accessibility trees, screen screenshots, or database query results—that contain concealed malicious instructions. Specifically, attackers can embed prompt injections into HTML accessibility…

LLM-powered GUI agents utilizing screenshot-based interpretation (such as those powered by GPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash, and DeepSeek V3 0324) are vulnerable to Fine-Print Injection (FPI) and Deceptive Default (DD) attacks due to a lack of visual saliency filtering. Unlike human users who prioritize prominent UI elements, these agents perform "indiscriminate parsing," processing low-salience text (e.g., privacy policies, terms of service, footer disclaimers) with the same…

A contextual integrity vulnerability exists in the memory mechanisms of multi-turn Text-to-Image (T2I) generation systems (e.g., those integrating Large Language Models with diffusion models). The vulnerability, dubbed "Inception," arises because safety filters typically operate on a per-turn basis, inspecting only the current input prompt, while the image generation model operates on an aggregated context (memory) of the conversation history. An attacker can exploit this discrepancy by…

Multi-agent systems (MAS) utilizing Large Language Model (LLM) orchestration are vulnerable to control-flow hijacking via indirect prompt injection, leading to Remote Code Execution (RCE). This vulnerability arises when a sub-agent (e.g., a file surfer or web surfer) processes untrusted input containing adversarial metadata, such as simulated error messages or administrative instructions. The sub-agent faithfully reproduces this adversarial content in its report to the orchestrator agent. The…

Large Vision-Language Models (VLMs) are vulnerable to a cross-modal toxic continuation attack facilitated by reinforcement learning-tuned diffusion models. This vulnerability allows an attacker to bypass safety alignment and external guardrails (such as NSFW image filters) by pairing a specific text prefix with a "semantically adversarial" image. Unlike traditional gradient-based adversarial examples that rely on pixel noise, these images are semantically coherent but optimized via Denoising…

A vulnerability in text-to-image (T2I) models allows bypassing safety filters through the use of metaphor-based adversarial prompts. These prompts, crafted using LLMs, indirectly convey sensitive content, exploiting the model's ability to infer meaning from figurative language while circumventing explicit keyword filters and model editing strategies.

Multimodal Large Language Models (MLLMs) are vulnerable to Jailbreak-Probability-based Attacks (JPA). JPA leverages a Jailbreak Probability Prediction Network (JPPN) to identify and optimize adversarial perturbations in input images, maximizing the probability of eliciting harmful responses from the MLLM, even with small perturbation bounds and few iterations. The attack operates by modifying the input image's hidden states within the MLLM to increase the predicted jailbreak probability.

Vision-Language Models (VLMs), specifically the LLaVA-1.5 and LLaVA-1.6 series, are vulnerable to optimization-based white-box jailbreak attacks despite standard safety alignment measures like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Attackers can craft adversarial perturbations in the image space (imperceptible noise) or latent space using Projected Gradient Descent (PGD) to manipulate the model's internal representations. These perturbations maximize the…