TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Xin, Wang, Yixu, Zhang, Jiaming, Wang, Ruofan, Yu, Jiaqi, Chen, Kai, Chen, Jingjing, Ma, Xingjun, Jiang, Yu-Gang |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models
by: Wang, Xin, et al.
Published: (2024)
by: Wang, Xin, et al.
Published: (2024)
Adversarial Prompt Tuning for Vision-Language Models
by: Zhang, Jiaming, et al.
Published: (2023)
by: Zhang, Jiaming, et al.
Published: (2023)
AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
by: Wang, Xin, et al.
Published: (2024)
by: Wang, Xin, et al.
Published: (2024)
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models
by: Zhang, Jiaming, et al.
Published: (2025)
by: Zhang, Jiaming, et al.
Published: (2025)
FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models
by: Zhai, Kun, et al.
Published: (2025)
by: Zhai, Kun, et al.
Published: (2025)
Adversarial Prompt Distillation for Vision-Language Models
by: Luo, Lin, et al.
Published: (2024)
by: Luo, Lin, et al.
Published: (2024)
RedDiffuser: Auditing Multimodal Safety Failures in Vision-Language Models via Reinforced Diffusion
by: Wang, Ruofan, et al.
Published: (2025)
by: Wang, Ruofan, et al.
Published: (2025)
IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves
by: Wang, Ruofan, et al.
Published: (2024)
by: Wang, Ruofan, et al.
Published: (2024)
DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models
by: Sun, Ye, et al.
Published: (2026)
by: Sun, Ye, et al.
Published: (2026)
BackdoorVLM: A Benchmark for Backdoor Attacks on Vision-Language Models
by: Li, Juncheng, et al.
Published: (2025)
by: Li, Juncheng, et al.
Published: (2025)
EnJa: Ensemble Jailbreak on Large Language Models
by: Zhang, Jiahao, et al.
Published: (2024)
by: Zhang, Jiahao, et al.
Published: (2024)
White-box Multimodal Jailbreaks Against Large Vision-Language Models
by: Wang, Ruofan, et al.
Published: (2024)
by: Wang, Ruofan, et al.
Published: (2024)
SentGuard: Sentence-Level Streaming Guardrails for Large Language Models
by: Yu, Jiaqi, et al.
Published: (2026)
by: Yu, Jiaqi, et al.
Published: (2026)
BadPatch: Diffusion-Based Generation of Physical Adversarial Patches
by: Wang, Zhixiang, et al.
Published: (2024)
by: Wang, Zhixiang, et al.
Published: (2024)
JailBound: Jailbreaking Internal Safety Boundaries of Vision-Language Models
by: Song, Jiaxin, et al.
Published: (2025)
by: Song, Jiaxin, et al.
Published: (2025)
Efficient Test-Time Prompt Tuning for Vision-Language Models
by: Zhu, Yuhan, et al.
Published: (2024)
by: Zhu, Yuhan, et al.
Published: (2024)
StolenLoRA: Exploring LoRA Extraction Attacks via Synthetic Data
by: Wang, Yixu, et al.
Published: (2025)
by: Wang, Yixu, et al.
Published: (2025)
Simulated Ensemble Attack: Transferring Jailbreaks Across Fine-tuned Vision-Language Models
by: Wang, Ruofan, et al.
Published: (2025)
by: Wang, Ruofan, et al.
Published: (2025)
Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs
by: Chen, Yunhao, et al.
Published: (2025)
by: Chen, Yunhao, et al.
Published: (2025)
FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models
by: Wang, Xin, et al.
Published: (2025)
by: Wang, Xin, et al.
Published: (2025)
AIM: Additional Image Guided Generation of Transferable Adversarial Attacks
by: Li, Teng, et al.
Published: (2025)
by: Li, Teng, et al.
Published: (2025)
TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking
by: Cheng, Yu, et al.
Published: (2026)
by: Cheng, Yu, et al.
Published: (2026)
R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning
by: Sheng, Lijun, et al.
Published: (2025)
by: Sheng, Lijun, et al.
Published: (2025)
Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers
by: Zheng, Weijie, et al.
Published: (2024)
by: Zheng, Weijie, et al.
Published: (2024)
MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models
by: Zhao, Shiji, et al.
Published: (2025)
by: Zhao, Shiji, et al.
Published: (2025)
AttackVLA: Benchmarking Adversarial and Backdoor Attacks on Vision-Language-Action Models
by: Li, Jiayu, et al.
Published: (2025)
by: Li, Jiayu, et al.
Published: (2025)
SafeVid: Toward Safety Aligned Video Large Multimodal Models
by: Wang, Yixu, et al.
Published: (2025)
by: Wang, Yixu, et al.
Published: (2025)
Towards Context-Invariant Safety Alignment for Large Language Models
by: Wang, Yixu, et al.
Published: (2026)
by: Wang, Yixu, et al.
Published: (2026)
ACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization
by: Huang, Yixu, et al.
Published: (2026)
by: Huang, Yixu, et al.
Published: (2026)
Expose Before You Defend: Unifying and Enhancing Backdoor Defenses via Exposed Models
by: Li, Yige, et al.
Published: (2024)
by: Li, Yige, et al.
Published: (2024)
WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
by: Zi, Bojia, et al.
Published: (2021)
by: Zi, Bojia, et al.
Published: (2021)
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
by: Luo, Jun, et al.
Published: (2024)
by: Luo, Jun, et al.
Published: (2024)
Doubly Debiased Test-Time Prompt Tuning for Vision-Language Models
by: Song, Fei, et al.
Published: (2025)
by: Song, Fei, et al.
Published: (2025)
LeakyCLIP: Extracting Training Data from CLIP
by: Chen, Yunhao, et al.
Published: (2025)
by: Chen, Yunhao, et al.
Published: (2025)
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection
by: Lei, Tianwu, et al.
Published: (2024)
by: Lei, Tianwu, et al.
Published: (2024)
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning
by: Gou, Yunhao, et al.
Published: (2023)
by: Gou, Yunhao, et al.
Published: (2023)
HoneypotNet: Backdoor Attacks Against Model Extraction
by: Wang, Yixu, et al.
Published: (2025)
by: Wang, Yixu, et al.
Published: (2025)
MoPD: Mixture-of-Prompts Distillation for Vision-Language Models
by: Chen, Yang, et al.
Published: (2024)
by: Chen, Yang, et al.
Published: (2024)
AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models
by: Wang, Yixu, et al.
Published: (2025)
by: Wang, Yixu, et al.
Published: (2025)
Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters
by: Yu, Jiazuo, et al.
Published: (2024)
by: Yu, Jiazuo, et al.
Published: (2024)
Similar Items
-
TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models
by: Wang, Xin, et al.
Published: (2024) -
Adversarial Prompt Tuning for Vision-Language Models
by: Zhang, Jiaming, et al.
Published: (2023) -
AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
by: Wang, Xin, et al.
Published: (2024) -
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models
by: Zhang, Jiaming, et al.
Published: (2025) -
FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models
by: Zhai, Kun, et al.
Published: (2025)