6 papers
Fair in Mind, Fair in Action? A Synchronous Benchmark for Understanding and Generation in UMLLMs
Yiran Zhao, Lu Zhou, Xiaogang Xu +3
As artificial intelligence (AI) is increasingly deployed across domains, ensuring fairness has become a core challenge. However, the field faces a "Tower of Babel'' dilemma: fairne…
Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts
Chiyu Zhang, Lu Zhou, Xiaogang Xu +3
Existing black-box jailbreak attacks achieve certain success on non-reasoning models but degrade significantly on recent SOTA reasoning models. To improve attack ability, inspired…
DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately
Huiwen Wu, Deyi Zhang, Xiaohan Li +3
The emergence of the Large Language Model (LLM) has shown their superiority in a wide range of disciplines, including language understanding and translation, relational logic reaso…
GADT: Enhancing Transferable Adversarial Attacks through Gradient-guided Adversarial Data Transformation
Yating Ma, Xiaogang Xu, Liming Fang +1
Current Transferable Adversarial Examples (TAE) are primarily generated by adding Adversarial Noise (AN). Recent studies emphasize the importance of optimizing Data Augmentation (D…
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning
Huiwen Wu, Xiaohan Li, Xiaogang Xu +3
The development of Large Language Models (LLMs) has significantly advanced various AI applications in commercial and scientific research fields, such as scientific literature summa…
Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey
Chiyu Zhang, Lu Zhou, Xiaogang Xu +2
With the advent of Large Vision-Language Models (LVLMs), new attack vectors, such as cognitive bias, prompt injection, and jailbreaking, have emerged. Understanding these attacks p…