4 papers
Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing
Qinghua Mao, Xi Lin, Jinze Gu +3
Large language models (LLMs) increasingly rely on knowledge editing to support knowledge-intensive reasoning, but this flexibility also introduces critical safety risks: adversarie…
DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis
Siyuan Li, Aodu Wulianghai, Guangyan Li +5
The rapid advancement of large language models (LLMs) presents new security challenges, particularly in detecting machine-generated text used for misinformation, impersonation, and…
CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks
Siyuan Li, Zehao Liu, Xi Lin +6
As Large Language Models (LLMs) are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety concerns, especially those evolvi…
Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization
Yuliang Chen, Xi Lin, Jun Wu +5
Federated Domain Generalization (FDG) aims to collaboratively train a global model across distributed clients that can generalize well on unseen domains. However, existing FDG meth…