Language Model Security Database

A "Priming Vulnerability" exists in Masked Diffusion Language Models (MDLMs) due to their iterative, parallel denoising inference mechanism. Unlike autoregressive models that generate tokens sequentially, MDLMs refine a sequence from a fully masked state through multiple denoising steps. The vulnerability arises because standard safety alignment (e.g., SFT, DPO, MOSA) typically optimizes the model to generate safe responses only when initialized from a fully masked sequence. If an affirmative…

Large Language Models (LLMs) are vulnerable to imperceptible jailbreaking attacks and prompt injection via the exploitation of Unicode variation selectors. This vulnerability arises from a discrepancy between text rendering and tokenizer processing. Attackers can append long sequences of invisible variation selectors (specifically from ranges U+FE00–U+FE0F and U+E0100–U+E01EF) to malicious prompts. While these characters are visually rendered as zero-width or ignored by standard user…

A security vulnerability exists in the safety alignment mechanisms of Large Language Models (LLMs), specifically susceptible to the "Dynamic Target Attack" (DTA). Unlike traditional gradient-based jailbreaks (e.g., GCG) that optimize adversarial suffixes toward a fixed, low-probability static target (e.g., "Sure, here is..."), DTA exploits the model's own output distribution. The attack iteratively samples candidate responses from the target model using relaxed decoding parameters (high…

A vulnerability exists in certain safety-aligned Large Language Models (LLMs) due to an untargeted, gradient-based optimization attack method called Untargeted Jailbreak Attack (UJA). Unlike previous targeted attacks (e.g., GCG) that optimize a prompt to elicit a predefined string (e.g., "Sure, here is..."), UJA optimizes for a general objective: maximizing the unsafety probability of the model's response, as quantified by an external judge model.

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

The GPT-OSS-20B large language model contains critical failures in its alignment and Chain-of-Thought (CoT) reasoning mechanisms, specifically in how it prioritizes numerical objectives and validates procedural structure. The model is vulnerable to "Quant Fever," where explicit numerical targets in a prompt (e.g., "delete 90% of files") override contextual safety constraints (e.g., "do not delete important files"). Furthermore, the model exhibits "Reasoning Procedure Mirage," where harmful…

Audio-Language Models (ALMs) including Qwen2.5-Omni (3B and 7B) and Phi-4-Multimodal are vulnerable to "WhisperInject," a two-stage adversarial audio attack that bypasses safety guardrails. The vulnerability allows an attacker to inject imperceptible perturbations into benign audio inputs (e.g., a query about the weather) that force the model to generate specific harmful content. The attack utilizes a novel optimization method, Reinforcement Learning with Projected Gradient Descent (RL-PGD)…

Audio-based Large Language Models (ALLMs), specifically Qwen2-Audio, are vulnerable to over-the-air adversarial audio attacks. An attacker with white-box access can generate robust adversarial audio perturbations using gradient-based optimization combined with audio augmentation techniques (specifically SpecAugment, translation, and additive noise). These perturbations, when played through a speaker in the physical environment, manipulate the ALLM processing the audio via a microphone. This…

Reasoning-capable Large Language Models (LLMs) are vulnerable to a class of indirect prompt injection known as Copy-Guided Attacks (CGA). This vulnerability exploits the intrinsic behavior of reasoning models to copy tokens from the input prompt (such as variable names, function identifiers, or code snippets) into their intermediate reasoning traces (Chain-of-Thought). By embedding adversarial trigger sequences into external payloads—specifically within data the model is expected to analyze—an…

A vulnerability exists in the safety alignment mechanisms of Large Language Models (LLMs) (including GPT-4, Claude 3, Gemini, and Qwen families) leading to "Implicit Harm." Unlike traditional jailbreaks that use overtly harmful queries, this vulnerability allows remote attackers to coerce the model into providing factually incorrect, plausible, and dangerous responses to benign-looking inputs. By employing "JailFlip" techniques—specifically constructed affirmative-type or denial-type queries…