{"total":164,"rows":[{"id":"2310.03744","title":"Improved Baselines with Visual Instruction Tuning","created":"2023-10-05","cited_by_count":4418,"area":"mllm"},{"id":"2306.13394","title":"MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models","created":"2023-06-23","cited_by_count":1652,"area":"mllm"},{"id":"2306.13549","title":"A Survey on Multimodal Large Language Models","created":"2023-06-23","cited_by_count":909,"area":"mllm"},{"id":"2311.05608","title":"FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts","created":"2023-11-09","cited_by_count":398,"area":"mllm"},{"id":"2307.14539","title":"Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models","created":"2023-07-26","cited_by_count":273,"area":"mllm"},{"id":"2309.11751","title":"How Robust is Google's Bard to Adversarial Image Attacks?","created":"2023-09-21","cited_by_count":183,"area":"mllm"},{"id":"2402.08567","title":"Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast","created":"2024-02-13","cited_by_count":153,"area":"mllm"},{"id":"2403.09792","title":"Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models","created":"2024-03-14","cited_by_count":136,"area":"mllm"},{"id":"2402.12336","title":"Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models","created":"2024-02-19","cited_by_count":120,"area":"mllm"},{"id":"2406.04031","title":"Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt","created":"2024-06-06","cited_by_count":109,"area":"mllm"},{"id":"2503.02199","title":"Words or Vision: Do Vision-Language Models Have Blind Faith in Text?","created":"2025-03-04","cited_by_count":89,"area":"mllm"},{"id":"2406.12030","title":"SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model","created":"2024-06-17","cited_by_count":85,"area":"mllm"},{"id":"2407.07403","title":"A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends","created":"2024-07-10","cited_by_count":84,"area":"mllm"},{"id":"2411.15296","title":"MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs","created":"2024-11-22","cited_by_count":72,"area":"mllm"},{"id":"2407.01599","title":"JailbreakZoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-Language Models","created":"2024-06-26","cited_by_count":70,"area":"mllm"},{"id":"2404.07204","title":"BRAVE: Broadening the visual encoding of vision-language models","created":"2024-04-10","cited_by_count":69,"area":"mllm"},{"id":"2501.04931","title":"Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency","created":"2025-01-09","cited_by_count":51,"area":"mllm"},{"id":"2402.06659","title":"Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models","created":"2024-02-05","cited_by_count":50,"area":"mllm"},{"id":"2401.04350","title":"Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness","created":"2024-01-09","cited_by_count":48,"area":"mllm"},{"id":"2403.01849","title":"One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models","created":"2024-03-04","cited_by_count":48,"area":"mllm"},{"id":"2411.16721","title":"Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreaks","created":"2024-11-23","cited_by_count":48,"area":"mllm"},{"id":"2410.05346","title":"AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models","created":"2024-10-07","cited_by_count":46,"area":"mllm"},{"id":"2403.09766","title":"An Image Is Worth 1000 Lies: Adversarial Transferability across Prompts on Vision-Language Models","created":"2024-03-14","cited_by_count":45,"area":"mllm"},{"id":"2311.11261","title":"Adversarial Prompt Tuning for Vision-Language Models","created":"2023-11-19","cited_by_count":44,"area":"mllm"},{"id":"2504.14348","title":"Manipulating Multimodal Agents via Cross-Modal Prompt Injection","created":"2025-04-19","cited_by_count":44,"area":"mllm"},{"id":"2402.08577","title":"Test-Time Backdoor Attacks on Multimodal Large Language Models","created":"2024-02-13","cited_by_count":40,"area":"mllm"},{"id":"2411.15720","title":"Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks","created":"2024-11-24","cited_by_count":39,"area":"mllm"},{"id":"2404.10335","title":"Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models","created":"2024-04-16","cited_by_count":38,"area":"mllm"},{"id":"2410.10855","title":"Core Knowledge Deficits in Multi-Modal Language Models","created":"2024-10-06","cited_by_count":38,"area":"mllm"},{"id":"2503.10635","title":"A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1","created":"2025-03-13","cited_by_count":37,"area":"mllm"},{"id":"2502.10794","title":"Distraction is All You Need for Multimodal Large Language Model Jailbreaking","created":"2025-02-15","cited_by_count":36,"area":"mllm"},{"id":"2410.05474","title":"R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?","created":"2024-10-07","cited_by_count":30,"area":"mllm"},{"id":"2404.19287","title":"Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective","created":"2024-04-30","cited_by_count":28,"area":"mllm"},{"id":"2405.13581","title":"Safety Alignment for Vision Language Models","created":"2024-05-22","cited_by_count":28,"area":"mllm"},{"id":"2403.09346","title":"B-AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Black-box Adversarial Visual-Instructions","created":"2024-03-14","cited_by_count":26,"area":"mllm"},{"id":"2405.09981","title":"Adversarial Robustness for Visual Grounding of Multimodal Large Language Models","created":"2024-05-16","cited_by_count":23,"area":"mllm"},{"id":"2411.13136","title":"TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models","created":"2024-11-20","cited_by_count":23,"area":"mllm"},{"id":"2412.00114","title":"SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments","created":"2024-11-28","cited_by_count":22,"area":"mllm"},{"id":"2312.01886","title":"InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models","created":"2023-12-04","cited_by_count":21,"area":"mllm"},{"id":"2505.05528","title":"X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP","created":"2025-05-08","cited_by_count":21,"area":"mllm"},{"id":"2404.05264","title":"Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security","created":"2024-04-08","cited_by_count":19,"area":"mllm"},{"id":"2406.09250","title":"MirrorCheck: Efficient Adversarial Defense for Vision-Language Models","created":"2024-06-13","cited_by_count":19,"area":"mllm"},{"id":"2411.02708","title":"Exploring Response Uncertainty in MLLMs: An Empirical Evaluation under Misleading Scenarios","created":"2024-11-05","cited_by_count":19,"area":"mllm"},{"id":"2306.14895","title":"Large Multimodal Models: Notes on CVPR 2023 Tutorial","created":"2023-06-26","cited_by_count":18,"area":"mllm"},{"id":"2408.16769","title":"PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning","created":"2024-08-29","cited_by_count":17,"area":"mllm"},{"id":"2502.13095","title":"Understanding and Rectifying Safety Perception Distortion in VLMs","created":"2025-02-18","cited_by_count":17,"area":"mllm"},{"id":"2308.12636","title":"Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning","created":"2023-08-24","cited_by_count":16,"area":"mllm"},{"id":"2503.11832","title":"Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning","created":"2025-03-14","cited_by_count":16,"area":"mllm"},{"id":"2503.13962","title":"Survey of Adversarial Robustness in Multimodal Large Language Models","created":"2025-03-18","cited_by_count":16,"area":"mllm"},{"id":"2409.07353","title":"Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks","created":"2024-09-11","cited_by_count":15,"area":"mllm"},{"id":"2501.15269","title":"Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language Models with Only Attention Sink","created":"2025-01-25","cited_by_count":15,"area":"mllm"},{"id":"2511.14159","title":"MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs","created":"2025-11-18","cited_by_count":15,"area":"mllm"},{"id":"2404.16557","title":"Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples","created":"2024-04-25","cited_by_count":14,"area":"mllm"},{"id":"2502.18290","title":"Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models","created":"2025-02-25","cited_by_count":14,"area":"mllm"},{"id":"2603.06148","title":"VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models","created":"2026-03-06","cited_by_count":14,"area":"mllm"},{"id":"2410.01534","title":"Toward a Holistic Evaluation of Robustness in CLIP Models","created":"2024-10-02","cited_by_count":13,"area":"mllm"},{"id":"2307.12980","title":"A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models","created":"2023-07-24","cited_by_count":12,"area":"mllm"},{"id":"2310.02569","title":"ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks","created":"2023-10-04","cited_by_count":12,"area":"mllm"},{"id":"2407.14971","title":"Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust and Semantically-Rich Vision-Language Models","created":"2024-07-20","cited_by_count":12,"area":"mllm"},{"id":"2411.18000","title":"Exploring Visual Vulnerabilities via Multi-Loss Adversarial Search for Jailbreaking Vision-Language Models","created":"2024-11-27","cited_by_count":12,"area":"mllm"},{"id":"2505.19911","title":"Attention! Your Vision Language Model Could Be Maliciously Manipulated","created":"2025-05-26","cited_by_count":12,"area":"mllm"},{"id":"2412.08108","title":"Exploiting Vision Encoder Vulnerabilities for Universal Adversarial Perturbations on Large Vision-Language Models","created":"2024-12-11","cited_by_count":11,"area":"mllm"},{"id":"2509.21029","title":"FORCE: Transferable Visual Jailbreaking Attacks via Feature Over-Reliance CorrEction","created":"2025-09-25","cited_by_count":11,"area":"mllm"},{"id":"2505.01050","title":"Transferable Adversarial Attacks on Black-Box Vision-Language Models","created":"2025-05-02","cited_by_count":10,"area":"mllm"},{"id":"2505.19610","title":"JailBound: Jailbreaking Internal Safety Boundaries of Vision-Language Models","created":"2025-05-26","cited_by_count":10,"area":"mllm"},{"id":"2501.19164","title":"Poison as Cure: Visual Noise for Mitigating Object Hallucinations in LVMs","created":"2025-01-31","cited_by_count":9,"area":"mllm"},{"id":"2502.01576","title":"Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models","created":"2025-02-03","cited_by_count":9,"area":"mllm"},{"id":"2505.15130","title":"Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models","created":"2025-05-21","cited_by_count":9,"area":"mllm"},{"id":"2511.20223","title":"V-Attack: Targeting Disentangled Value Features for Controllable Adversarial Attacks on LVLMs","created":"2025-11-25","cited_by_count":9,"area":"mllm"},{"id":"2404.13425","title":"Enhancing Adversarial Robustness of Vision-Language Models through Low-Rank Adaptation","created":"2024-04-20","cited_by_count":8,"area":"mllm"},{"id":"2504.01735","title":"AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization","created":"2025-04-02","cited_by_count":8,"area":"mllm"},{"id":"2505.17440","title":"VEAttack: Downstream-agnostic Vision Encoder Attack against Large Vision Language Models","created":"2025-05-23","cited_by_count":8,"area":"mllm"},{"id":"2603.03637","title":"Image-based Prompt Injection: Hijacking Multimodal LLMs through Visually Embedded Adversarial Instructions","created":"2026-03-04","cited_by_count":8,"area":"mllm"},{"id":"2407.08970","title":"Self-interpreting Adversarial Images","created":"2024-07-12","cited_by_count":7,"area":"mllm"},{"id":"2412.12722","title":"Defending LVLMs Against Vision Attacks through Partial-Perception Supervision","created":"2024-12-17","cited_by_count":7,"area":"mllm"},{"id":"2407.09050","title":"Refusing Safe Prompts for Multi-modal Large Language Models","created":"2024-07-12","cited_by_count":6,"area":"mllm"},{"id":"2503.12874","title":"Evolution-based Region Adversarial Prompt Learning for Robustness Enhancement in Vision-Language Models","created":"2025-03-17","cited_by_count":6,"area":"mllm"},{"id":"2601.21531","title":"On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression","created":"2026-01-29","cited_by_count":6,"area":"mllm"},{"id":"2602.17645","title":"Pushing the Frontier of Black-Box LVLM Attacks via Fine-Grained Detail Targeting","created":"2026-02-19","cited_by_count":6,"area":"mllm"},{"id":"2407.11121","title":"Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques","created":"2024-07-15","cited_by_count":5,"area":"mllm"},{"id":"2412.03002","title":"AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?","created":"2024-12-04","cited_by_count":5,"area":"mllm"},{"id":"2502.16593","title":"Tracking the Copyright of Large Vision-Language Models through Parameter Learning Adversarial Images","created":"2025-02-23","cited_by_count":5,"area":"mllm"},{"id":"2508.13739","title":"Enhancing Targeted Adversarial Attacks on Large Vision-Language Models via Intermediate Projector","created":"2025-08-19","cited_by_count":5,"area":"mllm"},{"id":"2502.15678","title":"Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models","created":"2025-02-21","cited_by_count":4,"area":"mllm"},{"id":"2503.11750","title":"Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization","created":"2025-03-14","cited_by_count":4,"area":"mllm"},{"id":"2508.15370","title":"Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigation","created":"2025-08-21","cited_by_count":4,"area":"mllm"},{"id":"2509.25178","title":"GHOST: Hallucination-Inducing Image Generation for Multimodal LLMs","created":"2025-09-29","cited_by_count":4,"area":"mllm"},{"id":"2510.04257","title":"AgentTypo: Adaptive Typographic Prompt Injection Attacks against Black-box Multimodal Agents","created":"2025-10-05","cited_by_count":4,"area":"mllm"},{"id":"2603.27918","title":"Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey","created":"2026-03-30","cited_by_count":4,"area":"mllm"},{"id":"2504.01308","title":"Safeguarding Vision-Language Models: Mitigating Vulnerabilities to Gaussian Noise in Perturbation-based Attacks","created":"2025-04-02","cited_by_count":3,"area":"mllm"},{"id":"2506.15734","title":"The Safety Reminder: A Soft Prompt to Reactivate Delayed Safety Awareness in Vision-Language Models","created":"2025-06-15","cited_by_count":3,"area":"mllm"},{"id":"2511.19220","title":"Are Large Vision Language Models Truly Grounded in Medical Images? Evidence from Italian Clinical Visual Question Answering","created":"2025-11-24","cited_by_count":3,"area":"mllm"},{"id":"2512.02973","title":"Contextual Image Attack: How Visual Context Exposes Multimodal Safety Vulnerabilities","created":"2025-12-02","cited_by_count":3,"area":"mllm"},{"id":"2602.19418","title":"PA-Attack: Guiding Gray-Box Attacks on LVLM Vision Encoders with Prototypes and Attention","created":"2026-02-23","cited_by_count":3,"area":"mllm"},{"id":"2606.15608","title":"On the Adversarial Robustness of Multimodal LLM Judges","created":"2026-06-14","cited_by_count":3,"area":"mllm"},{"id":"2406.12321","title":"Automatic benchmarking of large multimodal models via iterative experiment programming","created":"2024-06-18","cited_by_count":2,"area":"mllm"},{"id":"2410.13030","title":"Sensitivity of Generative VLMs to Semantically and Lexically Altered Prompts","created":"2024-10-16","cited_by_count":2,"area":"mllm"},{"id":"2411.17794","title":"NEMO: Can Multimodal LLMs Identify Attribute-Modified Objects?","created":"2024-11-26","cited_by_count":2,"area":"mllm"},{"id":"2501.09039","title":"Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in Large Vision-Language Models","created":"2025-01-14","cited_by_count":2,"area":"mllm"},{"id":"2502.08079","title":"MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models","created":"2025-02-12","cited_by_count":2,"area":"mllm"}],"next_cursor":"100"}