3 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
Towards Privacy-Preserving LLM Inference via Covariant Obfuscation (Technical Report)
Yu Lin, Qizhi Zhang, Wenqiang Ruan +6
The rapid development of large language models (LLMs) has driven the widespread adoption of cloud-based LLM inference services, while also bringing prominent privacy risks associat…
CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing
Yifan Zhou, Tianshi Xu, Jue Hong +2
Private large language model (LLM) inference based on cryptographic primitives offers a promising path towards privacy-preserving deep learning. However, existing frameworks only s…
Black-box Optimization of LLM Outputs by Asking for Directions
Jie Zhang, Meng Ding, Yang Liu +2
We present a novel approach for attacking black-box large language models (LLMs) by exploiting their ability to express confidence in natural language. Existing black-box attacks r…
AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection
Peiran Wang, Yang Liu, Yunfei Lu +6
Large Language Model (LLM) agents offer a powerful new paradigm for solving various problems by combining natural language reasoning with the execution of external tools. However,…