5 papers
Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment
Yuxi Li, Yi Liu, Yuekang Li +4
Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Current safety alignment techniques, how…
Efficient Detection of Toxic Prompts in Large Language Models
Yi Liu, Junzhe Yu, Huijia Sun +4
Large language models (LLMs) like ChatGPT and Gemini have significantly advanced natural language processing, enabling various applications such as chatbots and automated content g…
Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models
Junzhe Yu, Yi Liu, Huijia Sun +2
Large Language Models (LLMs) have significantly advanced text understanding and generation, becoming integral to applications across education, software development, healthcare, en…
MiniScope: Automated UI Exploration and Privacy Inconsistency Detection of MiniApps via Two-phase Iterative Hybrid Analysis
Shenao Wang, Yuekang Li, Kailong Wang +4
The advent of MiniApps, operating within larger SuperApps, has revolutionized user experiences by offering a wide range of services without the need for individual app downloads. H…
Self and Cross-Model Distillation for LLMs: Effective Methods for Refusal Pattern Alignment
Jie Li, Yi Liu, Chongyang Liu +4
Large Language Models (LLMs) like OpenAI's GPT series, Anthropic's Claude, and Meta's LLaMa have shown remarkable capabilities in text generation. However, their susceptibility to…