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:
• Security and robustness of machine learning and generative AI systems
• Security and safety of large language models, autonomous agents, and multi-agent systems
• Prompt injection, jailbreaks, data poisoning, backdoors, and adversarial attacks and defenses
• Security of AI tools, plugins, agent frameworks (e.g., Model Context Protocol), and AI/software supply chains
• Trustworthy AI: explainability, accountability, uncertainty, fairness, and reliability
• Privacy-preserving machine learning: federated learning, differential privacy, machine unlearning, and inference attacks
• Detection, provenance, and forensics of deepfakes and AI-generated content; misinformation defense
• AI-assisted software testing, fuzzing, program analysis, vulnerability detection, and automated program repair
• Verification, validation, and assurance of AI-enabled software systems
• AI for malware analysis, intrusion detection, network traffic analysis, and cyber-threat intelligence
• Security and resilience of cyber-physical systems, IIoT, edge/cloud systems, and critical infrastructure
• Datasets, benchmarks, evaluation frameworks, reproducibility studies, real-world deployment experiences, and other emerging topics at this intersection
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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