Cover Image

Directional Embedding Smoothing for Robust Vision Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Ye, Liu, Jing, Koike-Akino, Toshiaki
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Computation and Language Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Smoothed Embeddings for Robust Language Models
by: Hase, Ryo, et al.
Published: (2025)

Variational Randomized Smoothing for Sample-Wise Adversarial Robustness
by: Hase, Ryo, et al.
Published: (2024)

Amplification Effects in Test-Time Reinforcement Learning: Safety and Reasoning Vulnerabilities
by: Khattar, Vanshaj, et al.
Published: (2026)

Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
by: Rashid, Md Rafi Ur, et al.
Published: (2024)

Analyzing Inference Privacy Risks Through Gradients in Machine Learning
by: Li, Zhuohang, et al.
Published: (2024)

Exploring User-level Gradient Inversion with a Diffusion Prior
by: Li, Zhuohang, et al.
Published: (2024)

Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
by: Lowy, Andrew, et al.
Published: (2024)

Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment
by: Wang, Ye, et al.
Published: (2026)

$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)

Efficient Differentially Private Fine-Tuning of Diffusion Models
by: Liu, Jing, et al.
Published: (2024)

OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models
by: Xu, Xiaoyu, et al.
Published: (2025)

FIT to Forget: Robust Continual Unlearning for Large Language Models
by: Xu, Xiaoyu, et al.
Published: (2026)

Probing the Robustness of Large Language Models Safety to Latent Perturbations
by: Gu, Tianle, et al.
Published: (2025)

Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models
by: Yuan, Hongbang, et al.
Published: (2024)

On Adversarial Robustness of Language Models in Transfer Learning
by: Turbal, Bohdan, et al.
Published: (2024)

MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts?
by: Wahed, Muntasir, et al.
Published: (2025)

PostMark: A Robust Blackbox Watermark for Large Language Models
by: Chang, Yapei, et al.
Published: (2024)

Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring
by: Hua, Peichun, et al.
Published: (2025)

SequentialBreak: Large Language Models Can be Fooled by Embedding Jailbreak Prompts into Sequential Prompt Chains
by: Saiem, Bijoy Ahmed, et al.
Published: (2024)

Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses
by: Zheng, Xiaosen, et al.
Published: (2024)

SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models
by: Li, Lijun, et al.
Published: (2024)

Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization
by: Tang, Haochun, et al.
Published: (2026)

Lifelong Safety Alignment for Language Models
by: Wang, Haoyu, et al.
Published: (2025)

On the Role of Attention Heads in Large Language Model Safety
by: Zhou, Zhenhong, et al.
Published: (2024)

Learnable Privacy Neurons Localization in Language Models
by: Chen, Ruizhe, et al.
Published: (2024)

Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?
by: Liang, Zi, et al.
Published: (2025)

EnJa: Ensemble Jailbreak on Large Language Models
by: Zhang, Jiahao, et al.
Published: (2024)

Memories Retrieved from Many Paths: A Multi-Prefix Framework for Robust Detection of Training Data Leakage in Large Language Models
by: Dang, Trung Cuong, et al.
Published: (2025)

Instructional Fingerprinting of Large Language Models
by: Xu, Jiashu, et al.
Published: (2024)

Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations
by: Wei, Zeming, et al.
Published: (2023)

Extracting Training Data from Diffusion Language Models via Infilling
by: Wang, Yihan, et al.
Published: (2026)

Adversarial Text Purification: A Large Language Model Approach for Defense
by: Moraffah, Raha, et al.
Published: (2024)

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
by: Liu, Fuqiang, et al.
Published: (2024)

DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints
by: Zhao, Andrew, et al.
Published: (2024)

Machine Unlearning of Pre-trained Large Language Models
by: Yao, Jin, et al.
Published: (2024)

Securing Multi-turn Conversational Language Models From Distributed Backdoor Triggers
by: Tong, Terry, et al.
Published: (2024)

The Dark Side of Human Feedback: Poisoning Large Language Models via User Inputs
by: Chen, Bocheng, et al.
Published: (2024)

Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion
by: Wang, Guanchu, et al.
Published: (2024)

Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation
by: Li, Xianzhi, et al.
Published: (2024)

Detecting Training Data of Large Language Models via Expectation Maximization
by: Kim, Gyuwan, et al.
Published: (2024)