3 citations · 4 across the 7 of their papers we have counts for
8 papers
Financial Fragility in Societies of LLM Agents: Coordination Failures and Stabilizing Mechanisms
Zhenhao Fu, Ruipeng Xu, Qibing Ren
Individually protective decisions can produce avoidable collective failures. As large language model (LLM) agents take on greater roles in financial decision-making, financial AI s…
Beyond Direct Access: Resource Hijacking in LLM Agents
Puyu Zeng, Qibing Ren, Mingang Chen +1
Large language model agents are increasingly connected to high-value resources, including external APIs, GPUs and servers, and workflows such as deployment and approval. Existing a…
When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms
Qibing Ren, Zhijie Zheng, Jiaxuan Guo +3
In this work, we study the risks of collective financial fraud in large-scale multi-agent systems powered by large language model (LLM) agents. We investigate whether agents can co…
Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report
Shanghai AI Lab, :, Xiaoyang Chen +35
To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligence (AI) models, this report presents a comprehensive assessment of their frontier…
When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems
Qibing Ren, Sitao Xie, Longxuan Wei +4
Recent large-scale events like election fraud and financial scams have shown how harmful coordinated efforts by human groups can be. With the rise of autonomous AI systems, there i…
LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts
Qibing Ren, Hao Li, Dongrui Liu +7
Safety concerns in large language models (LLMs) have gained significant attention due to their exposure to potentially harmful data during pre-training. In this paper, we identify…
CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion
Qibing Ren, Chang Gao, Jing Shao +4
The rapid advancement of Large Language Models (LLMs) has brought about remarkable generative capabilities but also raised concerns about their potential misuse. While strategies l…
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Chaochao Lu, Chen Qian, Guodong Zheng +33
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide ga…