Securing Artificial Intelligence Against Attacks
A special issue of Future Internet (ISSN 1999-5903).
Deadline for manuscript submissions: 31 May 2026 | Viewed by 1393
Special Issue Editors
Interests: IT security; privacy; interactive machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, AI has started to permeate many fields and industries with affordable and scalable services. Expectations are high that this trend will continue for a while, providing new AI-based services to experts and the general public alike, but also bringing change to many classical approaches, which can be enhanced greatly with AI.
This, of course, opens up new attack surfaces and attack vectors, using AI as both a tool for attacks, but also as a target. The latter is especially important, as currently prominent AI technologies like LLMs and DNNs lack explainability and thus also transparency. Furthermore, many classical security approaches like penetration testing need to be adapted to this situation, as, e.g., translating the finding of a security-relevant error in an AI system into a fix is generally not possible for many currently employed algorithms. In addition, complex systems like neural networks might increase the attack surface quite drastically, requiring special treatment not only from a technical perspective, but also from a risk management one.
This Special Issue is dedicated to research results in the area of security for AI systems. It calls for cutting-edge contributions to fundamental theoretical research as well as its application in practice. This Special Issue covers, but is not limited to, the following topics:
- Attacks against AI systems, e.g., model and data poisoning and model extraction attacks;
- Privacy and AI;
- Trustworthy AI and trust in data-driven systems;
- Controllable AI;
- Secure and trustworthy data provisioning;
- Resilient data-driven systems;
- AI risk analysis and AI risk management;
- Implementing security requirements from the AI Act;
- Security testing AI, e.g., penetration testing;
- Proof of concepts for secure AI systems.
Prof. Dr. Peter Kieseberg
Prof. Dr. Jungwoo Ryoo
Guest Editors
Manuscript Submission Information
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Keywords
- AI
- trustworthy AI
- AI attacks
- attacking AI
- secure AI
- controllable AI
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