{"total":35,"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":"2403.14774","title":"Few-Shot Adversarial Prompt Learning on Vision-Language Models","created":"2024-03-21","cited_by_count":39,"area":"mllm"},{"id":"2504.11195","title":"R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning","created":"2025-04-15","cited_by_count":33,"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":"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":"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":"2411.02669","title":"Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack","created":"2024-11-04","cited_by_count":18,"area":"mllm"},{"id":"2510.24038","title":"Enhancing CLIP Robustness via Cross-Modality Alignment","created":"2025-10-28","cited_by_count":15,"area":"mllm"},{"id":"2511.17448","title":"MMT-ARD: Multimodal Multi-Teacher Adversarial Distillation for Robust Vision-Language Models","created":"2025-11-21","cited_by_count":11,"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":"2412.12722","title":"Defending LVLMs Against Vision Attacks through Partial-Perception Supervision","created":"2024-12-17","cited_by_count":7,"area":"mllm"},{"id":"2411.15244","title":"Adversarial Prompt Distillation for Vision-Language Models","created":"2024-11-22","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":"2503.01263","title":"Generalizable Prompt Learning of CLIP: A Brief Overview","created":"2025-03-03","cited_by_count":3,"area":"mllm"},{"id":"2510.22785","title":"Self-Calibrated Consistency can Fight Back for Adversarial Robustness in Vision-Language Models","created":"2025-10-26","cited_by_count":3,"area":"mllm"},{"id":"2512.16523","title":"TTP: Test-Time Padding for Adversarial Detection and Robust Adaptation on Vision-Language Models","created":"2025-12-18","cited_by_count":2,"area":"mllm"},{"id":"2601.19210","title":"Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP","created":"2026-01-27","cited_by_count":2,"area":"mllm"},{"id":"2502.19269","title":"Neural Antidote: Class-Wise Prompt Tuning for Purifying Backdoors in CLIP","created":"2025-02-26","cited_by_count":1,"area":"mllm"},{"id":"2509.06992","title":"FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models","created":"2025-09-03","cited_by_count":1,"area":"mllm"},{"id":"2601.12443","title":"Adversarial Defense in Vision-Language Models: An Overview","created":"2026-01-18","cited_by_count":1,"area":"mllm"},{"id":"2603.26984","title":"A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models","created":"2026-03-27","cited_by_count":1,"area":"mllm"},{"id":"2603.29410","title":"AGFT: Alignment-Guided Fine-Tuning for Zero-Shot Adversarial Robustness of Vision-Language Models","created":"2026-03-31","cited_by_count":1,"area":"mllm"},{"id":"2604.11576","title":"Finetune Like You Pretrain: Boosting Zero-shot Adversarial Robustness in Vision-language Models","created":"2026-04-13","cited_by_count":1,"area":"mllm"},{"id":"2604.18867","title":"Hierarchically Robust Zero-shot Vision-language Models","created":"2026-04-20","cited_by_count":1,"area":"mllm"},{"id":"2606.06938","title":"When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness","created":"2026-06-05","cited_by_count":1,"area":"mllm"},{"id":"2606.18839","title":"Semantic Robustness Certification for Vision-Language Models","created":"2026-06-17","cited_by_count":1,"area":"mllm"},{"id":"2505.17509","title":"MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models","created":"2025-05-23","cited_by_count":0,"area":"mllm"},{"id":"2603.12799","title":"What Makes VLMs Robust? Towards Reconciling Robustness and Accuracy in Vision-Language Models","created":"2026-03-13","cited_by_count":0,"area":"mllm"},{"id":"2605.15584","title":"AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models","created":"2026-05-15","cited_by_count":0,"area":"mllm"},{"id":"2605.17577","title":"TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models","created":"2026-05-17","cited_by_count":0,"area":"mllm"},{"id":"2605.25922","title":"Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models","created":"2026-05-25","cited_by_count":0,"area":"mllm"},{"id":"2606.06943","title":"SS-TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models","created":"2026-06-05","cited_by_count":0,"area":"mllm"},{"id":"2606.23132","title":"T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models","created":"2026-06-22","cited_by_count":0,"area":"mllm"},{"id":"2607.14737","title":"GeoDetect: Geometric Adversarial Detection for VLPs","created":"2026-07-16","cited_by_count":0,"area":"mllm"},{"id":"2609.00788","title":"Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain","created":"2026-09-01","cited_by_count":0,"area":"mllm"}],"next_cursor":null}