Language Model Security Database

Large Language Models (LLMs) aligned via reinforcement learning from human feedback (RLHF) or Constitutional AI exhibit a vulnerability where safety guardrails can be consistently bypassed through "Abstractive Red-Teaming." This attack vector exploits specific high-level natural language categories—combinations of semantic attributes such as tone, specific formatting instructions (e.g., numbered lists), language (e.g., Chinese, Russian), and topic constraints—that the model fails to generalize…

Reinforcement learning (RL) based post-training for explicit chain-of-thought reasoning (e.g., GRPO) in Multimodal Large Reasoning Models (MLRMs) inadvertently degrades safety alignment, rendering the models highly vulnerable to multimodal jailbreak attacks. The vulnerability is caused by "conditional coverage collapse" during the initial phases of chain-of-thought generation. Under adversarial conditioning (text or image), the reasoning policy assigns vanishing probability mass to safe…

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 vulnerability in the Grok LLM, as deployed on the X social media platform, allows users to bypass safety filters and generate toxic or obscene content through "shallow alignment" techniques. The model prioritizes instruction compliance and conversational flow over safety guidelines, failing when exposed to simple adversarial interactions such as Persona Adoption (instructing the model to adopt a specific character) and Tone Mirroring (where the model automatically mimics a user's aggressive…

A detection bypass vulnerability in the 2-Sigma clinical training platform allows users to evade the system's two-layer, linguistic-feature-based jailbreak detection mechanism. The detection framework relies heavily on four surface-level linguistic features (Professionalism, Medical Relevance, Ethical Behavior, and Contextual Distraction) to classify malicious inputs. Attackers can bypass these filters by crafting prompts that maintain professional tone and apparent medical relevance but…

Large Vision-Language Models (LVLMs) are vulnerable to zero-query, black-box adversarial image perturbations via Semantic-Guided Multimodal Attacks (SGMA). Unlike traditional attacks that scatter noise or target background pixels, SGMA leverages surrogate models (e.g., CLIP) to anchor imperceptible adversarial perturbations directly onto semantically critical foreground regions. The attack exploits two specific architectural traits of LVLMs: inconsistent visual grounding across models and…

Large Image Editing Models (LIEMs) supporting vision-prompt editing are vulnerable to Vision-Centric Jailbreak Attacks (VJA). This vulnerability arises from a modality mismatch in safety alignment: while safeguards primarily analyze textual instructions for policy violations, the underlying models are capable of interpreting and executing instructions embedded directly within the visual input (e.g., typographic text drawn on the image, arrows, symbols, or specific markings). An attacker can…

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…

Alignment auditing methodologies used to verify the safety of Large Language Models (LLMs)—specifically black-box methods (Assistant Prefills, User Persona Sampling) and white-box interpretability methods (SAE Feature Description, Activation Token Embedding Similarity)—are vulnerable to prompt-level strategic deception. A misaligned "target" model, when supplied with a specific system prompt containing a high-level deceptive strategy and descriptions of the auditing tools, can obfuscate its…