Artificial Intelligence Security: Foundations, Methods, and Applications

Dear Colleagues,

Artificial Intelligence Security has become one of the most active research areas with the rapid advancement of foundation models and large language models. While AI systems have demonstrated remarkable capabilities across a wide range of applications, they are increasingly exposed to security threats such as adversarial attacks, data poisoning, prompt injection, model stealing, privacy leakage, jailbreak attacks, and unreliable model behaviors. Developing secure, trustworthy, explainable, robust, and governable AI systems is therefore essential for the safe and responsible deployment of artificial intelligence. This session aims to bring together researchers and practitioners from academia and industry working on Artificial Intelligence Security. Topics of interest include the theoretical foundations of AI security, adversarial machine learning, large language model security, privacy-preserving machine learning, trustworthy AI, explainable AI, AI security evaluation, AI safety, AI governance, and AI ethics. The session also welcomes research on secure AI applications in cybersecurity, smart manufacturing, autonomous driving, healthcare, smart cities, and other safety-critical domains.

The topics include, but are not limited to, the following indicative list:

• Foundations and Key Technologies of Artificial Intelligence Security
• Security of Large Language Models and Generative AI
• Adversarial Attacks, Defenses, and Robust Machine Learning
• Privacy-Preserving Machine Learning and Federated Learning Security
• Trustworthy AI and Explainable AI
• AI Security Evaluation, Risk Analysis, and AI Red Teaming
• AI Fairness, Ethics, and Governance
• Applications of AI Security in Cybersecurity, Smart Manufacturing, Autonomous Driving, Healthcare, Smart Cities, and Other Safety-Critical Domains

Prof. Dr. Yongkai Fan
Dr. Xia Lei
Dr. Yikun Xu
Dr. Chang Xu
Dr. Jinglei Tan
Topic Editors