Language Model Security Database

The paper describes a specific black-box jailbreak evaluation, BVS, in which fragmented visual content is mixed with neutral imagery and paired with reconstruction-oriented text so harmful intent is only recomposed during multimodal reasoning. The authors report that this can bypass input and output safety assumptions in image-generating MLLMs. A safe defensive reproduction should use synthetic, non-harmful stand-ins for prohibited concepts, test whether fragmented cross-modal inputs are…

Multimodal Large Language Models (MLLMs) exhibit a vulnerability to "Reasoning-based Multi-Image Attacks," where safety guardrails are bypassed by distributing harmful intent across multiple images (2–4 inputs). Unlike single-image jailbreaks that rely on visual obfuscation, this vulnerability exploits the model's reasoning capabilities. By presenting images that share a specific relationship (e.g., Temporal Jump, Spatial Juxtaposition, or Causality), an attacker can compel the model to infer…

Code-generation Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) are vulnerable to directed misuse for the generation of misleading data visualizations. This vulnerability, described as the "ChartAttack" framework, allows an attacker to prompt the model to manipulate chart annotation code (e.g., JSON specifications for Matplotlib or Vega-Lite) to apply specific "misleaders"—design choices that distort data interpretation without altering the underlying data values. By…

Text-to-Image (T2I) models and their associated safety filters are vulnerable to MacPrompt, a black-box jailbreak technique that exploits cross-lingual embedding alignments. Attackers can bypass input text filters, latent representation filters, and model-level concept removal defenses by replacing sensitive keywords with "macaronic" substitutes. These substitutes are constructed by extracting and recombining character-level substrings from translations of the target word across multiple…

Point-based 3D Vision-Language Models (VLMs), specifically PointLLM and GPT4Point, are vulnerable to white-box, gradient-based adversarial attacks. The vulnerability exists in the model's processing of 3D point cloud data, where an attacker can optimize imperceptible geometric perturbations ($\delta$) on the input point cloud ($x$) to manipulate the model's textual output. The paper identifies two specific attack vectors: 1. Vision Attack: Directly perturbs the high-dimensional visual token…

A vulnerability in the prompt-side safety filters of GPT-based Text-to-Image (T2I) systems allows attackers to bypass restrictions on Politically Sensitive Content (PSC). By utilizing a technique called Identity-Preserving Descriptive Mapping (IPDM) combined with Geopolitically Distal Translation, an attacker can obfuscate explicit political entities into neutral descriptive phrases translated across multiple low-resource languages. This induces semantic fragmentation, preventing the safety…

Multi-modal Large Language Models (MLLMs) are vulnerable to a multi-turn jailbreaking attack that leverages typographic visual prompts combined with conversational context drifting. The vulnerability exists because MLLMs establish trust and context during initial benign interactions, shifting the model's latent representation toward helpfulness and compromising its ability to detect malicious intent in subsequent turns. The attack vector utilizes an image where a harmful request is…

A "Gamified Adversarial Multimodal Breakout via Instructional Traps" (GAMBIT) vulnerability exists in the safety alignment mechanisms of Multimodal Large Language Models (MLLMs), specifically those employing Chain-of-Thought (CoT) reasoning. The vulnerability exploits the finite cognitive resource budget of the model by inducing "cognitive overload" through a high-stakes, gamified context. The attack functions by decomposing a harmful query into a visual puzzle (e.g., a shuffled grid of image…

Large Vision-Language Models (LVLMs), specifically InstructBLIP, LLaVA, and MiniGPT-4, are susceptible to a black-box adversarial jailbreak vulnerability via Zeroth-Order Simultaneous Perturbation Stochastic Approximation (ZO-SPSA). An attacker can generate adversarial images with imperceptible perturbations that, when paired with harmful text prompts, bypass the model's safety alignment mechanisms (such as RLHF). Unlike traditional white-box attacks, this method does not require access to…

Commercial Multimodal Large Language Model (MLLM) integrated systems are vulnerable to a "Dual Steganography" jailbreak paradigm (referred to as Odysseus). The vulnerability arises from the reliance of safety filters on the assumption that malicious content must be explicitly visible in the input or output modalities (text or image). Attackers can bypass these filters by encoding malicious queries into binary matrices and embedding them into benign-looking images using steganographic encoders…