5 citations · 10 across the 29 of their papers we have counts for
10 papers · 1 filter
MCPZoo: A Large-Scale Dataset of Runnable Model Context Protocol Servers for AI Agent
Mengying Wu, Pei Chen, Geng Hong +6
Model Context Protocol (MCP) enables agents to interact with external tools, yet empirical research on MCP is hindered by the lack of large-scale, accessible datasets. We present M…
SmartSight: Mitigating Hallucination in Video-LLMs Without Compromising Video Understanding via Temporal Attention Collapse
Yiming Sun, Mi Zhang, Feifei Li +2
Despite Video Large Language Models having rapidly advanced in recent years, perceptual hallucinations pose a substantial safety risk, which severely restricts their real-world app…
Shell or Nothing: Real-World Benchmarks and Memory-Activated Agents for Automated Penetration Testing
Wuyuao Mai, Geng Hong, Qi Liu +5
Penetration testing is critical for identifying and mitigating security vulnerabilities, yet traditional approaches remain expensive, time-consuming, and dependent on expert human…
ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments
Yuquan Wang, Mi Zhang, Yining Wang +4
Large Reasoning Models (LRMs) have demonstrated impressive performance in reasoning-intensive tasks, but they remain vulnerable to harmful content generation, particularly in the m…
PRISON: Unmasking the Criminal Potential of Large Language Models
Xinyi Wu, Geng Hong, Pei Chen +3
As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked the systematic understanding and assessm…
ReasoningShield: Safety Detection over Reasoning Traces of Large Reasoning Models
Changyi Li, Jiayi Wang, Xudong Pan +2
Large Reasoning Models (LRMs) leverage explicit reasoning traces, known as Chain-of-Thought (CoT) reasoning, to decompose complex problems into intermediate steps before deriving f…
Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correction
Changyue Jiang, Wenqi Zhang, Xudong Pan +2
LLM-based agents solve complex tasks through iterative reasoning, tool use, and environment interaction, where each intermediate thought directly shapes subsequent actions. Small d…
OpenDeception: Learning Deception and Trust in Human-AI Interaction via Multi-Agent Simulation
Yichen Wu, Qianqian Gao, Xudong Pan +2
As large language models (LLMs) are increasingly deployed as interactive agents, open-ended human-AI interactions can involve deceptive behaviors with serious real-world consequenc…
The Illusionist's Prompt: Exposing the Factual Vulnerabilities of Large Language Models with Linguistic Nuances
Yining Wang, Yuquan Wang, Xi Li +3
As Large Language Models (LLMs) continue to advance, they are increasingly relied upon as real-time sources of information by non-expert users. To ensure the factuality of the info…
You Can't Eat Your Cake and Have It Too: The Performance Degradation of LLMs with Jailbreak Defense
Wuyuao Mai, Geng Hong, Pei Chen +5
With the rise of generative large language models (LLMs) like LLaMA and ChatGPT, these models have significantly transformed daily life and work by providing advanced insights. How…