5 papers
AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models
Jiacheng Liang, Tanqiu Jiang, Yuhui Wang +3
This paper presents AutoRAN, the first framework to automate the hijacking of internal safety reasoning in large reasoning models (LRMs). At its core, AutoRAN pioneers an execution…
Dynamic Token Reweighting for Robust Vision-Language Models
Tanqiu Jiang, Jiacheng Liang, Rongyi Zhu +3
Large vision-language models (VLMs) are highly vulnerable to multimodal jailbreak attacks that exploit visual-textual interactions to bypass safety guardrails. In this paper, we pr…
Enhancing Medical Large Vision-Language Models via Alignment Distillation
Aofei Chang, Ting Wang, Fenglong Ma
Medical Large Vision-Language Models (Med-LVLMs) have shown promising results in clinical applications, but often suffer from hallucinated outputs due to misaligned visual understa…
MEDMKG: Benchmarking Medical Knowledge Exploitation with Multimodal Knowledge Graph
Xiaochen Wang, Yuan Zhong, Lingwei Zhang +3
Medical deep learning models depend heavily on domain-specific knowledge to perform well on knowledge-intensive clinical tasks. Prior work has primarily leveraged unimodal knowledg…
RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models
Tanqiu Jiang, Changjiang Li, Fenglong Ma +1
Differentially private diffusion models (DPDMs) harness the remarkable generative capabilities of diffusion models while enforcing differential privacy (DP) for sensitive data. How…