most citedLLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CR2026

Beyond Direct Access: Resource Hijacking in LLM Agents

Puyu Zeng, Qibing Ren

Large language model agents are increasingly connected to high-value resources such as computing infrastructure, credentials, usage budgets, identities, private knowledge, communic…

cs.MA2026

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…

cs.CL20261 cited

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…

cs.AI2025

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…

cs.AI2025

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…