5 papers · 1 filter
SafeLLM: Unlearning Harmful Outputs from Large Language Models against Jailbreak Attacks
Xiangman Li, Xiaodong Wu, Qi Li +2
Jailbreak attacks pose a serious threat to the safety of Large Language Models (LLMs) by crafting adversarial prompts that bypass alignment mechanisms, causing the models to produc…
When There Is No Decoder: Removing Watermarks from Stable Diffusion Models in a No-box Setting
Xiaodong Wu, Tianyi Tang, Xiangman Li +2
Watermarking has emerged as a promising solution to counter harmful or deceptive AI-generated content by embedding hidden identifiers that trace content origins. However, the robus…
SecureT2I: No More Unauthorized Manipulation on AI Generated Images from Prompts
Xiaodong Wu, Xiangman Li, Qi Li +2
Text-guided image manipulation with diffusion models enables flexible and precise editing based on prompts, but raises ethical and copyright concerns due to potential unauthorized…
PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning
Xiangman Li, Xiaodong Wu, Jianbing Ni +2
Decentralized learning is vulnerable to poison attacks, where malicious clients manipulate local updates to degrade global model performance. Existing defenses mainly detect and fi…
SoK: A Comprehensive Security Analysis of Jailbreak Resilience in GPT and DeepSeek Models
Xiaodong Wu, Xiangman Li, Qi Li +2
The rapid proliferation of Large Language Models (LLMs) has heightened concerns regarding their exposure to jailbreak attacks, which craft adversarial inputs designed to elicit uns…