1 citations · 1 across the 2 of their papers we have counts for
5 papers
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
Zhengyi Li, Yakai Wang, Kang Yang +6
For Transformer models, cryptographically secure inference ensures that the client learns only the final output, while the server learns nothing about the client's input. However,…
Rethinking and Exploring String-Based Malware Family Classification in the Era of LLMs and RAG
Yufan Chen, Daoyuan Wu, Juantao Zhong +7
Malware family classification aims to identify the specific family (e.g., GuLoader or BitRAT) a malware sample may belong to, in contrast to malware detection or sample classificat…
Detecting Various DeFi Price Manipulations with LLM Reasoning
Juantao Zhong, Daoyuan Wu, Ye Liu +4
DeFi (Decentralized Finance) is one of the most important applications of today's cryptocurrencies and smart contracts. It manages hundreds of billions in Total Value Locked (TVL)…
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner
Xunguang Wang, Daoyuan Wu, Zhenlan Ji +7
Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs) and has evolved into multiple categories: h…
LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper
Daoyuan Wu, Shuai Wang, Yang Liu +1
Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs). A considerable amount of research exists p…