UnifiedAttack: Evaluating the Safety of Large Multimodal Models in Synergistic Harmful Image-Text Generation

arXiv:2610.00341 · cs.CR, cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CR, cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/bingjunluo/UnifiedAttack

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 37× in this paper · explained in 2 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Consistency — 10× in this paper · explained in 3 episodes
• Cognitive Planning Injection — 7× in this paper
• In-Context Reskinning — 6× in this paper
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Attack Success Rate — 4× in this paper
• Actionability — 3× in this paper · explained in 3 episodes
• Attack Success Rate (ASR) — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Adversarial scenarios — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes

Sources

• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• Emerging Properties in Unified Multimodal Pretraining
• SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
• HoliSafe: Holistic Safety Benchmarking and Modeling for Vision-Language Model
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
• Visual Instruction Tuning
• Chameleon: Mixed-Modal Early-Fusion Foundation Models
• Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
• Emu3: Next-Token Prediction is All You Need
• T2I-RiskyPrompt: A Benchmark for Safety Evaluation, Attack, and Defense on Text-to-Image Model

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