论文标题
攻击，防御和工具：促进强大的AI/ML系统的框架
Attacks, Defenses, And Tools: A Framework To Facilitate Robust AI/ML Systems
论文作者
论文摘要
软件系统越来越依赖人工智能（AI）和机器学习（ML）组件。 AI技术在各种应用领域的新兴人气吸引了恶意演员和对手。因此，支持AI-Software系统的开发人员需要考虑这些系统可能容易受到影响的各种新颖的网络攻击和漏洞。本文介绍了一个框架，以表征与AI-Systems相关的攻击和弱点，并提供缓解技术和防御策略。该框架旨在支持软件设计人员在开发支持AI-Software，了解此类系统的攻击表面以及开发对与ML相关的各种新兴攻击方面的产品中采取积极措施。开发的框架涵盖了广泛的攻击，缓解技术以及防御和进攻工具。在本文中，我们演示了框架架构及其主要组成部分，描述其属性，并讨论这项研究的长期目标。
Software systems are increasingly relying on Artificial Intelligence (AI) and Machine Learning (ML) components. The emerging popularity of AI techniques in various application domains attracts malicious actors and adversaries. Therefore, the developers of AI-enabled software systems need to take into account various novel cyber-attacks and vulnerabilities that these systems may be susceptible to. This paper presents a framework to characterize attacks and weaknesses associated with AI-enabled systems and provide mitigation techniques and defense strategies. This framework aims to support software designers in taking proactive measures in developing AI-enabled software, understanding the attack surface of such systems, and developing products that are resilient to various emerging attacks associated with ML. The developed framework covers a broad spectrum of attacks, mitigation techniques, and defensive and offensive tools. In this paper, we demonstrate the framework architecture and its major components, describe their attributes, and discuss the long-term goals of this research.
