Based on interviews with 28 organizations, we found that industry
practitioners are not equipped with tactical and strategic tools to protect,
detect and respond to attacks on their Machine Learning (ML) systems. We
leverage the insights from the interviews and we enumerate the gaps in
perspective in securing machine learning systems when viewed in the context of
traditional software security development. We write this paper from the
perspective of two personas: developers/ML engineers and security incident
responders who are tasked with securing ML systems as they are designed,
developed and deployed ML systems. The goal of this paper is to engage
researchers to revise and amend the Security Development Lifecycle for
industrial-grade software in the adversarial ML era.

通过对 28 个组织的采访，我们发现行业从业者缺乏保护、检测和响应机器学习系统攻击的战术和战略工具。此文章旨在从开发者 / ML 工程师和安全事件响应者的角度，阐述了机器学习系统在传统软件安全开发背景下的安全性视图差异，以期鼓励研究人员在对抗 ML 领域中修订和修改软件安全开发生命周期。