A Survey on Offensive AI Within Cybersecurity

TL;DR

This survey comprehensively reviews offensive AI techniques and their impacts across various domains, highlighting attack methods, implications, and future research directions in cybersecurity.

Contribution

It provides a thorough overview of offensive AI methods, including adversarial attacks and weaponized AI, with insights and case studies for cybersecurity research.

Findings

Offensive AI poses significant threats to digital infrastructure.

Adversarial machine learning is a key attack vector.

Case studies illustrate real-world implications.

Abstract

Artificial Intelligence (AI) has witnessed major growth and integration across various domains. As AI systems become increasingly prevalent, they also become targets for threat actors to manipulate their functionality for malicious purposes. This survey paper on offensive AI will comprehensively cover various aspects related to attacks against and using AI systems. It will delve into the impact of offensive AI practices on different domains, including consumer, enterprise, and public digital infrastructure. The paper will explore adversarial machine learning, attacks against AI models, infrastructure, and interfaces, along with offensive techniques like information gathering, social engineering, and weaponized AI. Additionally, it will discuss the consequences and implications of offensive AI, presenting case studies, insights, and avenues for further research.