most citedGuardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM

2 citations · 4 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CR2026

Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions

Junjie Xiong, Zhengyuan Jiang, Xiaoran Xu +7

Large Language Models (LLMs) have emerged as powerful tools that impact information integrity on social media platforms. This comprehensive review examines the dual role of LLMs in…

cs.CL2026

LLM-as-a-Reviewer: Benchmarking Their Ability, Divergence, and Prompt Injection Resistance as Paper Reviewers

Lingyao Li, Junjie Xiong, Changjia Zhu +5

Large language models (LLMs) are increasingly used in academic peer review, yet their reliability, alignment with human judgment, and robustness to adversarial attacks remain poorl…

cs.CR2026

Prompt Overflow: What the Guardrail Inspects Is Not What the Model Infers

Yuanbo Zhou, Changjia Zhu, Junyu Wang +5

Guardrail models (a.k.a. safety checkers) are widely deployed to screen user inputs before they reach large language models (LLMs), serving as a primary defense against prompt inje…

cs.CR2025

COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

Junyu Wang, Changjia Zhu, Yuanbo Zhou +4

This paper studies how multimodal large language models (MLLMs) undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply a…

cs.CY2025★ 1 cited

When Your Reviewer is an LLM: Biases, Divergence, and Prompt Injection Risks in Peer Review

Changjia Zhu, Junjie Xiong, Renkai Ma +3

Peer review is the cornerstone of academic publishing, yet the process is increasingly strained by rising submission volumes, reviewer overload, and expertise mismatches. Large lan…

cs.CY2025★ 1 cited

Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild"

Lingyao Li, Renkai Ma, Zhaoqian Xue +1

While Large Language Models (LLMs) are rapidly integrating into daily life, research on their risks often remains lab-based and disconnected from the problems users encounter "in t…

cs.CL2025★ 2 cited

Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM

Chi Zhang, Changjia Zhu, Junjie Xiong +4

Large Language Models (LLMs) have revolutionized content creation across digital platforms, offering unprecedented capabilities in natural language generation and understanding. Me…

cs.CR2025

Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models

Junjie Xiong, Changjia Zhu, Shuhang Lin +4

Large Language Models (LLMs) are increasingly equipped with capabilities of real-time web search and integrated with protocols like Model Context Protocol (MCP). This extension cou…

cs.CL2025

Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction

Xiaoran Xu, Zhaoqian Xue, Chi Zhang +7

Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey methods often fall short due to limite…