Guest Editor(s)
Dr. Wanlun Ma
Email: wma@swin.edu.au
Affiliation: Department of Computing Technologies, Swinburne University of Technology, Hawthorn, Australia
Homepage:
Research Interests: trustworthy and responsible AI; AI security; adversarial machine learning; security and reliability of AI agents; software security
Dr. Xiaogang Zhu
Email: xiaogang.zhu@adelaide.edu.au
Affiliation: School of Computer Science and Information Technology, Adelaide University, Adelaide, Australia
Homepage:
Research Interests: software testing and fuzzing; vulnerability detection; software, system, and IoT security; AI for software security
Summary
Artificial intelligence - from machine learning models to large language models (LLMs) and autonomous agents - is now deeply embedded in software systems, communication networks, and critical infrastructure, creating both a rapidly expanding attack surface and powerful new capabilities for cyber defense. Securing AI-enabled systems has therefore become one of the most pressing challenges in computing.
This Special Issue focuses on the intersection of AI security, trustworthy AI, software security, software engineering, cybersecurity, and critical infrastructure protection from a computing perspective. It addresses both the security of AI - protecting models, LLMs, and agents against adversarial, poisoning, jailbreak, and supply-chain attacks - and AI for security - applying intelligent techniques to software testing, vulnerability detection, and cyber defense. We welcome original research, tools, benchmarks, empirical studies, and surveys that advance systematic approaches to building secure, trustworthy, and dependable AI-enabled software systems and infrastructure.
Suggested themes include, but are not limited to:
1) Security, robustness, and trustworthiness of machine learning, generative AI, and large language models;
2) Security and safety of LLM-based agents, agent frameworks, and AI/software supply chains;
3) Privacy-preserving machine learning and detection of deepfakes and AI-generated content;
4) AI-assisted software testing, fuzzing, vulnerability detection, and automated program repair;
5) Verification, validation, and assurance of AI-enabled software systems;
6) Intelligent cyber defense and resilience of networks, cyber-physical systems, IIoT, and critical infrastructure.
Keywords
AI security; trustworthy AI; large language models; AI agents; adversarial machine learning; software testing and assurance; fuzzing; vulnerability detection; cyber-physical systems; critical infrastructure protection
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