3 papers
cs.CR2026
RvB: Automating AI System Hardening via Iterative Red-Blue Games
Lige Huang, Zicheng Liu, Jie Zhang +3
The dual offensive and defensive utility of Large Language Models (LLMs) highlights a critical gap in AI security: the lack of unified frameworks for dynamic, iterative adversarial…
cs.AI2025
TradeTrap: Are LLM-based Trading Agents Truly Reliable and Faithful?
Lewen Yan, Jilin Mei, Tianyi Zhou +4
LLM-based trading agents are increasingly deployed in real-world financial markets to perform autonomous analysis and execution. However, their reliability and robustness under adv…
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…