There has been extensive discussion in Taiwan regarding the application of Artificial Intelligence (AI) in security defense. However, the security challenges faced by AI models have received comparatively less attention. This presentation will use the OWASP ML Top 10 to explore common security risks in machine learning, incorporating practical demonstrations of Deep Neural Network (DNN) attacks to thoroughly explain the principles behind each attack.

The presentation will cover the following topics: input data attacks (such as adversarial sample generation), data manipulation attacks (data poisoning), model inversion attacks, model stealing, and AI supply chain attacks. Through these cases, the audience will gain a clear understanding of how each security risk operates, enabling them to design effective defense and detection mechanisms.