5 papers
Overlooked Safety Vulnerability in LLMs: Malicious Intelligent Optimization Algorithm Request and its Jailbreak
Haoran Gu, Handing Wang, Yi Mei +2
The widespread deployment of large language models (LLMs) has raised growing concerns about their misuse risks and associated safety issues. While prior studies have examined the s…
Enhancing the Effectiveness and Durability of Backdoor Attacks in Federated Learning through Maximizing Task Distinction
Zhaoxin Wang, Handing Wang, Cong Tian +1
Federated learning allows multiple participants to collaboratively train a central model without sharing their private data. However, this distributed nature also exposes new attac…
Implicit Jailbreak Attacks via Cross-Modal Information Concealment on Vision-Language Models
Zhaoxin Wang, Handing Wang, Cong Tian +1
Multimodal large language models (MLLMs) enable powerful cross-modal reasoning capabilities. However, the expanded input space introduces new attack surfaces. Previous jailbreak at…
One Trigger Token Is Enough: A Defense Strategy for Balancing Safety and Usability in Large Language Models
Haoran Gu, Handing Wang, Yi Mei +2
Large Language Models (LLMs) have been extensively used across diverse domains, including virtual assistants, automated code generation, and scientific research. However, they rema…
ParetoHqD: Fast Offline Multiobjective Alignment of Large Language Models using Pareto High-quality Data
Haoran Gu, Handing Wang, Yi Mei +2
Aligning large language models with multiple human expectations and values is crucial for ensuring that they adequately serve a variety of user needs. To this end, offline multiobj…