{"total":21,"rows":[{"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":"2502.05206","title":"Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety","created":"2025-02-02","cited_by_count":67,"area":"mllm"},{"id":"2502.14881","title":"A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations","created":"2025-02-14","cited_by_count":67,"area":"mllm"},{"id":"2406.07230","title":"Needle In A Multimodal Haystack","created":"2024-06-11","cited_by_count":58,"area":"mllm"},{"id":"2507.19024","title":"A Survey of Multimodal Hallucination Evaluation and Detection","created":"2025-07-25","cited_by_count":27,"area":"mllm"},{"id":"2406.14194","title":"VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model","created":"2024-06-20","cited_by_count":26,"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":"2503.13962","title":"Survey of Adversarial Robustness in Multimodal Large Language Models","created":"2025-03-18","cited_by_count":16,"area":"mllm"},{"id":"2502.08779","title":"BBQ-V: Benchmarking Visual Stereotype Bias in Large Multimodal Models","created":"2025-02-12","cited_by_count":11,"area":"mllm"},{"id":"2406.17115","title":"Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models","created":"2024-06-24","cited_by_count":8,"area":"mllm"},{"id":"2504.16727","title":"Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward","created":"2025-04-23","cited_by_count":7,"area":"mllm"},{"id":"2407.05758","title":"Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports","created":"2024-07-08","cited_by_count":6,"area":"mllm"},{"id":"2509.18425","title":"Losing the Plot: How VLM responses degrade on imperfect charts","created":"2025-09-22","cited_by_count":5,"area":"mllm"},{"id":"2501.12266","title":"CBVLM: Training-free Explainable Concept-based Large Vision Language Models for Medical Image Classification","created":"2025-01-21","cited_by_count":4,"area":"mllm"},{"id":"2604.06950","title":"Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation","created":"2026-04-08","cited_by_count":4,"area":"mllm"},{"id":"2510.11178","title":"BLEnD-Vis: Benchmarking Multimodal Cultural Understanding in Vision Language Models","created":"2025-10-13","cited_by_count":3,"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":"2507.22398","title":"On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations","created":"2025-07-30","cited_by_count":2,"area":"mllm"},{"id":"2605.05045","title":"When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise","created":"2026-05-06","cited_by_count":0,"area":"mllm"},{"id":"2606.24388","title":"PHANTOM: A Large-Scale Dataset of Multimodal Adversarial Attacks for Vision-Language Models","created":"2026-06-23","cited_by_count":0,"area":"mllm"},{"id":"2608.07535","title":"Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards","created":"2026-07-27","cited_by_count":0,"area":"mllm"}],"next_cursor":null}