2 citations · 3 across the 7 of their papers we have counts for
8 papers
LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics
Quanyan Zhu
Large language models (LLMs) increasingly mediate strategic interactions through natural language, making semantic control a critical element of communication and deception. This p…
Understanding Censorship in Large Language Models: From Mechanisms to Governance
Quanyan Zhu
Large language models (LLMs) increasingly mediate access to information, yet their responses are shaped by training-data curation, alignment procedures, provider policies, inferenc…
Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations
Tao Li, Quanyan Zhu
Cybersecurity is being fundamentally reshaped by foundation-model-based artificial intelligence. Large language models now enable autonomous planning, tool orchestration, and strat…
Cyber Resilience in Next-Generation Networks: Threat Landscape, Theoretical Foundations, and Design Paradigms
Junaid Farooq, Quanyan Zhu
The evolution of networked systems, driven by innovations in software-defined networking (SDN), network function virtualization (NFV), open radio access networks (O-RAN), and cloud…
Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats
Quanyan Zhu
Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual respon…
Toward a Dynamic Stackelberg Game-Theoretic Framework for Agentic AI Defense Against LLM Jailbreaking
Zhengye Han, Quanyan Zhu
This paper proposes a game theoretic framework that models the interaction between prompt engineers and large language models (LLMs) as a two player extensive form game coupled wit…
Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning
Lili Zhang, Quanyan Zhu, Herman Ray +1
Network threat detection has been challenging due to the complexities of attack activities and the limitation of historical threat data to learn from. To help enhance the existing…
Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View
Xuan Liu, Jie Zhang, Haoyang Shang +3
Large language models (LLMs) have been shown to face hallucination issues due to the data they trained on often containing human bias; whether this is reflected in the decision-mak…