1 citations · 1 across the 8 of their papers we have counts for
10 papers
Understanding Implicit Trust Errors in Core Carrier Networks through Multi-Agent Flaw Discovery and Analysis
Ziyu Lin, Ziting Wang, Xinfeng Li +2
Cellular core networks (CNs) are critical infrastructure, yet their internal security model has historically relied on physical isolation: interfaces between core components often…
"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems
Xinfeng Li, Shenyu Dai, Kelong Zheng +4
Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare. However, this deepening trust introduces a…
The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis
Peiran Wang, Xinfeng Li, Chong Xiang +5
The evolution of Large Language Models (LLMs) has resulted in a paradigm shift towards autonomous agents, necessitating robust security against Prompt Injection (PI) vulnerabilitie…
DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization
Lionel Z. Wang, Yusheng Zhao, Jiabin Luo +6
The deployment of Machine-Generated Text (MGT) detection systems necessitates processing sensitive user data, creating a fundamental conflict between authorship verification and pr…
CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding
Hanjun Luo, Chiming Ni, Jiaheng Wen +9
LLM-powered coding agents are reshaping the development paradigm. However, existing evaluation systems, neither traditional tests for humans nor benchmarks for LLMs, fail to captur…
EmoRAG: Evaluating RAG Robustness to Symbolic Perturbations
Xinyun Zhou, Xinfeng Li, Yinan Peng +9
Retrieval-Augmented Generation (RAG) systems are increasingly central to robust AI, enhancing large language model (LLM) faithfulness by incorporating external knowledge. However,…
Mitigating Over-Refusal in Aligned Large Language Models via Inference-Time Activation Energy
Eric Hanchen Jiang, Weixuan Ou, Run Liu +8
Safety alignment of large language models currently faces a central challenge: existing alignment techniques often prioritize mitigating responses to harmful prompts at the expense…
A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory
Qianshan Wei, Tengchao Yang, Yaochen Wang +7
Large Language Model (LLM) agents use memory to learn from past interactions, enabling autonomous planning and decision-making in complex environments. However, this reliance on me…
The Man Behind the Sound: Demystifying Audio Private Attribute Profiling via Multimodal Large Language Model Agents
Lixu Wang, Kaixiang Yao, Xinfeng Li +4
Our research uncovers a novel privacy risk associated with multimodal large language models (MLLMs): the ability to infer sensitive personal attributes from audio data -- a techniq…
AudioTrust: Benchmarking the Multifaceted Trustworthiness of Audio Large Language Models
Kai Li, Can Shen, Yile Liu +31
The rapid development and widespread adoption of Audio Large Language Models (ALLMs) demand rigorous evaluation of their trustworthiness. However, existing evaluation frameworks ar…