11 citations · 11 across the 4 of their papers we have counts for
6 papers
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…
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…
PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilities
Zicheng Liu, Lige Huang, Jie Zhang +3
The increasing autonomy of Large Language Models (LLMs) necessitates a rigorous evaluation of their potential to aid in cyber offense. Existing benchmarks often lack real-world com…
Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey
Yunkai Dang, Kaichen Huang, Jiahao Huo +11
The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in…
REEF: Representation Encoding Fingerprints for Large Language Models
Jie Zhang, Dongrui Liu, Chen Qian +4
Protecting the intellectual property of open-source Large Language Models (LLMs) is very important, because training LLMs costs extensive computational resources and data. Therefor…
The Tug of War Within: Mitigating the Fairness-Privacy Conflicts in Large Language Models
Chen Qian, Dongrui Liu, Jie Zhang +2
Ensuring awareness of fairness and privacy in Large Language Models (LLMs) is critical. Interestingly, we discover a counter-intuitive trade-off phenomenon that enhancing an LLM's…