5 citations · 10 across the 19 of their papers we have counts for
9 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…
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…
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…
Evaluation Faking: Unveiling Observer Effects in Safety Evaluation of Frontier AI Systems
Yihe Fan, Wenqi Zhang, Xudong Pan +1
As foundation models grow increasingly more intelligent, reliable and trustworthy safety evaluation becomes more indispensable than ever. However, an important question arises: Whe…
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…
Bare Minimum Mitigations for Autonomous AI Development
Joshua Clymer, Isabella Duan, Chris Cundy +10
Artificial intelligence (AI) is advancing rapidly, with the potential for significantly automating AI research and development itself in the near future. In 2024, international sci…
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…
Large language model-powered AI systems achieve self-replication with no human intervention
Xudong Pan, Jiarun Dai, Yihe Fan +3
Self-replication with no human intervention is broadly recognized as one of the principal red lines associated with frontier AI systems. While leading corporations such as OpenAI a…