8 papers · 1 filter
BackdoorVLM: A Benchmark for Backdoor Attacks on Vision-Language Models
Juncheng Li, Yige Li, Hanxun Huang +5
Backdoor attacks undermine the reliability and trustworthiness of machine learning systems by injecting hidden behaviors that can be maliciously activated at inference time. While…
AutoBackdoor: Automating Backdoor Attacks via LLM Agents
Yige Li, Zhe Li, Wei Zhao +4
Backdoor attacks pose a serious threat to the secure deployment of large language models (LLMs), enabling adversaries to implant hidden behaviors triggered by specific inputs. Howe…
Imperceptible Jailbreaking against Large Language Models
Kuofeng Gao, Yiming Li, Chao Du +4
Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are generally assumed to require visi…
Simulated Ensemble Attack: Transferring Jailbreaks Across Fine-tuned Vision-Language Models
Ruofan Wang, Xin Wang, Yang Yao +3
The widespread practice of fine-tuning open-source Vision-Language Models (VLMs) raises a critical security concern: jailbreak vulnerabilities in base models may persist in downstr…
LeakyCLIP: Extracting Training Data from CLIP
Yunhao Chen, Shujie Wang, Xin Wang +3
Understanding the memorization and privacy leakage risks in Contrastive Language--Image Pretraining (CLIP) is critical for ensuring the security of multimodal models. Recent studie…
Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints
Zhenyun Yin, Shujie Wang, Xuhong Wang +2
Improving the reliability of large language models (LLMs) is critical for deploying them in real-world scenarios. In this paper, we propose \textbf{Deliberative Searcher}, the firs…