collaborators

10 papers

cs.CR2026

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…

cs.SE2026

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…

cs.SD2026

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…

cs.LG2026

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…

cs.HC2026

"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…

cs.CR2026

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…