55 citations · 97 across the 29 of their papers we have counts for
11 papers · 1 filter
Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework
Zimo Ji, Daoyuan Wu, Wenyuan Jiang +5
Large Language Model (LLM)-based agent systems are increasingly deployed for complex real-world tasks but remain vulnerable to natural language-based attacks that exploit over-priv…
Taxonomy, Evaluation and Exploitation of IPI-Centric LLM Agent Defense Frameworks
Zimo Ji, Xunguang Wang, Zongjie Li +6
Large Language Model (LLM)-based agents with function-calling capabilities are increasingly deployed, but remain vulnerable to Indirect Prompt Injection (IPI) attacks that hijack t…
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…
Differentiation-Based Extraction of Proprietary Data from Fine-Tuned LLMs
Zongjie Li, Daoyuan Wu, Shuai Wang +1
The increasing demand for domain-specific and human-aligned Large Language Models (LLMs) has led to the widespread adoption of Supervised Fine-Tuning (SFT) techniques. SFT datasets…
IP Leakage Attacks Targeting LLM-Based Multi-Agent Systems
Liwen Wang, Wenxuan Wang, Shuai Wang +5
The rapid advancement of Large Language Models (LLMs) has led to the emergence of Multi-Agent Systems (MAS) to perform complex tasks through collaboration. However, the intricate n…
SoK: Evaluating Jailbreak Guardrails for Large Language Models
Xunguang Wang, Zhenlan Ji, Wenxuan Wang +3
Large Language Models (LLMs) have achieved remarkable progress, but their deployment has exposed critical vulnerabilities, particularly to jailbreak attacks that circumvent safety…
BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts
Qingyue Wang, Qi Pang, Xixun Lin +2
Mixture-of-Experts (MoE) have emerged as a powerful architecture for large language models (LLMs), enabling efficient scaling of model capacity while maintaining manageable computa…
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…
LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
Yuqiang Sun, Daoyuan Wu, Yue Xue +5
Large language models (LLMs) have demonstrated significant potential in various tasks, including those requiring human-level intelligence, such as vulnerability detection. However,…