Adversarial Machine Learning: Attack Vectors, Defences, and Robustness

Abstract

Background. Adversarial machine learning has progressed from a marginal concern within machine learning research into a first-order discipline for the secure deployment of artificial intelligence systems in regulated and operational environments. The contemporary threat landscape encompasses evasion at inference time, data poisoning across training pipelines, model extraction and inference attacks against deployed systems, and a defence ecosystem whose claimed robustness frequently fails to gene

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@misc{tanveer2026adversarial,
  title = {{Adversarial Machine Learning: Attack Vectors, Defences, and Robustness}},
  author = {Rizwan Tanveer},
  year = {2026},
  doi = {10.2139/ssrn.6696178},
  url = {https://doi.org/10.2139/ssrn.6696178}
}