Language Model Security Database

An imperceptible visual prompt injection vulnerability in Multimodal Large Language Models (MLLMs) allows attackers to execute precise command-hijacking via a Covert Triggered dual-Target Attack (CoTTA). By embedding a bounded, learnable textual overlay ($L_\infty$ norm bound $\varepsilon \le 16$) and adversarial noise into an input image, the attack forces the source image's internal feature representation to align with both the textual and visual embeddings of an attacker-specified…

A vulnerability in Multimodal Large Language Models (MLLMs) allows attackers to bypass safety alignments via Multi-Image Dispersion and Semantic Reconstruction (MIDAS). Attackers decompose malicious instructions into risk-bearing semantic subunits, fragment them, and distribute them across multiple benign-looking Game-style Visual Reasoning (GVR) puzzles (e.g., Letter Equations, Rank-and-Read, Odd-One-Out). A sanitized, persona-driven textual prompt with sequential placeholders is then used to…

Large Vision-Language Models (LVLMs) are vulnerable to multi-turn, multi-modal jailbreak attacks where malicious intent is incrementally introduced and obfuscated through intertwined text and image prompts. Attackers can systematically bypass safety alignments by starting with self-optimized, benign-seeming conversation starters (horizontal expansion) and progressively stacking text and image attack augmentations across multiple conversation turns (vertical expansion). Furthermore, models fail…

A vulnerability in the compositional reasoning architecture of Large Vision-Language Models (LVLMs) allows attackers to bypass multimodal safety alignments using a technique known as Reasoning-Oriented Programming (ROP). Current safety mechanisms primarily target explicit malicious patterns at the perception level (early layers). This vulnerability exploits late-stage reasoning by decomposing a harmful objective into a set of spatially isolated, semantically benign visual "gadgets". Because…

A temporal trajectory infilling vulnerability in Text-to-Video (T2V) generative models allows attackers to bypass input and output safety filters to generate policy-violating content. The vulnerability is exploited using a fragmented prompting technique known as Two Frames Matter (TFM). An attacker submits a prompt that specifies only sparse boundary conditions (the start and end frames) using semantically suggestive but lexically benign alternatives, entirely omitting the intermediate action…

The integration of the visual modality in Large Vision-Language Models (VLMs) introduces a vulnerability where appending an image to a harmful text prompt induces a "jailbreak-related representation shift" in the model's internal high-dimensional space. This shift forcibly steers the model's last-token hidden state away from a designated refusal state and into a distinct jailbreak state. The vulnerability occurs because the visual modality overrides the safety alignment of the underlying…

A "Visual Confused Deputy" vulnerability exists in Computer-Using Agents (CUAs) that rely on visual perception to execute coordinate-based GUI actions (e.g., click(x,y)). Because the agent's understanding of the system state is entirely dependent on the screenshot provided by the runtime, a compromised runtime or tool can intercept and alter the screenshot pixels before forwarding them to the LLM. By visually swapping the locations of benign and privileged UI elements, an attacker can trick…

Frontier Multimodal Large Language Models (MLLMs) are vulnerable to Visual Exclusivity (VE) attacks, an "Image-as-Basis" threat where malicious intent is achieved through joint reasoning over benign text and complex technical visual content (e.g., blueprints, schematics, network diagrams). Unlike wrapper-based attacks that conceal malicious text via typography or adversarial noise, VE exploits the model's core visual reasoning capabilities. Attackers can bypass safety filters by combining…

Large Language Model (LLM) based web agents (such as those built using the BrowserUse scaffold) are vulnerable to Indirect Prompt Injection (IPI) attacks when autonomously navigating and processing untrusted web content. Unlike standard Cross-Site Scripting (XSS), this vulnerability occurs when the LLM orchestrator consumes the DOM or visual screenshots of a webpage containing concealed or contextually disguised adversarial instructions. The LLM interprets these embedded text strings as…

A vulnerability in advanced Vision-Language Models (VLMs) allows attackers to bypass safety alignment mechanisms via a Cross-Modal Entanglement Attack (COMET). By reframing malicious queries into multi-hop reasoning tasks, attackers can migrate visualizable key entities into a paired image and replace the textual entities with ambiguous spatial pointers. This forces the VLM to reconstruct the harmful intent through its own self-induced cross-modal reasoning, effectively bypassing filters that…