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20232026
most citedJailGuard: A Universal Detection Framework for LLM Prompt-based Attacks

9 citations · 16 across the 18 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

eess.IV2026

Reliability Testing of Medical Model Performance under Distributed Deployment

Yifei Wang, Xiaohan Zhang, Youtao Ding +4

Distributed inference has become an indispensable part of deploying medical models under practical latency, memory, and throughput constraints. Although modern frameworks improve s…

cs.SE2026

Not as Sweet by Another Name: An Empirical Study of Format Robustness in LLM Document Workflows

Xiaoyu Zhang, Xianyun Cheng, Tianlin Li +3

LLM-driven software systems are rapidly evolving from plain-text conversations to document-centric end-to-end workflows, where the same semantic content can be delivered in diverse…

cs.CR2026

Trusted Weights, Treacherous Optimizations? Optimization-Triggered Backdoor Attacks on LLMs

Yifei Wang, Tianlin Li, Xiaohan Zhang +3

Inference optimization aims to minimize the latency and resource consumption of LLM inference while preserving output quality, making large-scale deployment practical and cost-effe…

cs.SE2026

Weaponizing the Commons: A Taxonomy and Detection Framework of Abuse on GitHub

Yuli Cheng, Xiaoyu Zhang, Jiongchi Yu +3

GitHub plays a critical role in modern software supply chains, making its security an important research concern. Existing studies have primarily focused on CI/CD automation, colla…

cs.SE2026

Where Agent Frameworks Fall Short: Examining Functional Challenges and Usability Concerns

Xinxue Zhu, Yanzhou Mu, Xiaoyu Zhang +5

Large language model (LLM) agents are increasingly built on agent frameworks that provide reusable abstractions for workflow orchestration, state management, tool integration, and…

cs.CR2026

False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models

Weipeng Jiang, Xiaoyu Zhang, Juan Zhai +3

Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs) remain largely unexplored. In this pap…