3 papers
cs.AI2025
LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation
Huizhen Shu, Xuying Li, Zhuo Li
Achieving robust safety alignment in large language models (LLMs) while preserving their utility remains a fundamental challenge. Existing approaches often struggle to balance comp…
cs.CL2025
The Resurgence of GCG Adversarial Attacks on Large Language Models
Yuting Tan, Xuying Li, Zhuo Li +2
Gradient-based adversarial prompting, such as the Greedy Coordinate Gradient (GCG) algorithm, has emerged as a powerful method for jailbreaking large language models (LLMs). In thi…
cs.CL2025
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Huizhen Shu, Xuying Li, Qirui Wang +3
With the rapid proliferation of Natural Language Processing (NLP), especially Large Language Models (LLMs), generating adversarial examples to jailbreak LLMs remains a key challeng…