1 citations · 1 across the 5 of their papers we have counts for
7 papers
MemEvoBench: Benchmarking Safety Risks from Memory Misevolution in LLM Agents
Weiwei Xie, Shaoxiong Guo, Fan Zhang +5
Equipping Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks. Specifically, contaminated or bia…
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…
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…
One RL to See Them All: Visual Triple Unified Reinforcement Learning
Yan Ma, Linge Du, Xuyang Shen +7
Reinforcement learning (RL) is becoming an important direction for post-training vision-language models (VLMs), but public training methodologies for unified multimodal RL remain m…
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…