77 citations · 78 across the 10 of their papers we have counts for
10 papers
MMAligner: Safeguarding Multimodal Large Language Models through Representation Calibration
Shenyi Zhang, Keyan Guo, Zihao Wang +5
Multimodal large language models (MLLMs) often refuse unsafe text prompts yet generate harmful responses to semantically equivalent multimodal inputs. Existing defenses either rely…
VOID: Defeating Unauthorized Mimicry in Latent Diffusion Models
Chunlin Qiu, Ang Li, Tianxiao Huang +7
While Latent Diffusion Models (LDMs) have revolutionized visual synthesis, they are increasingly exploited for unauthorized mimicry of individuals. Existing defenses inject decepti…
Sparse Tokens Suffice: Jailbreaking Audio Language Models via Token-Aware Gradient Optimization
Zheng Fang, Xiaosen Wang, Shenyi Zhang +2
Jailbreak attacks on audio language models (ALMs) optimize audio perturbations to elicit unsafe generations, and they typically update the entire waveform densely throughout optimi…
Amulet: Fast TEE-Shielded Inference for On-Device Model Protection
Zikai Mao, Lingchen Zhao, Lei Xu +4
On-device machine learning (ML) introduces new security concerns about model privacy. Storing valuable trained ML models on user devices exposes them to potential extraction by adv…
Selective Masking Adversarial Attack on Automatic Speech Recognition Systems
Zheng Fang, Shenyi Zhang, Tao Wang +3
Extensive research has shown that Automatic Speech Recognition (ASR) systems are vulnerable to audio adversarial attacks. Current attacks mainly focus on single-source scenarios, i…
JBShield: Defending Large Language Models from Jailbreak Attacks through Activated Concept Analysis and Manipulation
Shenyi Zhang, Yuchen Zhai, Keyan Guo +7
Despite the implementation of safety alignment strategies, large language models (LLMs) remain vulnerable to jailbreak attacks, which undermine these safety guardrails and pose sig…
Zero-Query Adversarial Attack on Black-box Automatic Speech Recognition Systems
Zheng Fang, Tao Wang, Lingchen Zhao +6
In recent years, extensive research has been conducted on the vulnerability of ASR systems, revealing that black-box adversarial example attacks pose significant threats to real-wo…
Boosting Adversarial Transferability with Low-Cost Optimization via Maximin Expected Flatness
Chunlin Qiu, Ang Li, Yiheng Duan +4
Transfer-based attacks craft adversarial examples on white-box surrogate models and directly deploy them against black-box target models, offering model-agnostic and query-free thr…
Hijacking Attacks against Neural Networks by Analyzing Training Data
Yunjie Ge, Qian Wang, Huayang Huang +7
Backdoors and adversarial examples are the two primary threats currently faced by deep neural networks (DNNs). Both attacks attempt to hijack the model behaviors with unintended ou…
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information
Baolin Zheng, Peipei Jiang, Qian Wang +6
Adversarial attacks against commercial black-box speech platforms, including cloud speech APIs and voice control devices, have received little attention until recent years. The cur…