AI and the Future of Cybersecurity: Why Openness Matters
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A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out...
Secure AI Systems Protecting Machine Learning Models from Emerging Cyber Threats
Securing AI systems is not a task any single discipline can accomplish alone; it requires sustained collaboration between machine learning researchers, cybersecurity professionals, and policymakers if AI technologies are to remain reliable, trustworthy, and resilient in adversarial environments.
Retrosynthesis of Synthetic Media for Explainable AI Provenance Forensics
A self-referential retrosynthesis framework for explainable AI provenance forensics under a fixed-generator setting that leverages a jointly optimized encoder-decoder pair to implement a self-embedding mechanism that enables round-trip consistency verification.
Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications
This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their...