Red Teaming with Artificial Intelligence-Driven Cyberattacks: A Scoping Review

2025-03-25

AI Summary

This study systematically investigates AI-enabled network attack paradigms in red teaming. Addressing evolving threats—such as AI-accelerated penetration and automated data exfiltration—the work employs a scoping review methodology, rigorously selecting 11 high-quality studies from an initial pool of 470 publications. The resulting AI-driven attack taxonomy comprehensively covers key adversarial scenarios: vulnerability exploitation, phishing content generation, password cracking, and URL/social account identification. Six reusable, AI-augmented attack pathways are synthesized, revealing both the generality and evolutionary trajectory of multimodal AI-powered attacks. The findings establish the first empirically grounded framework and methodological foundation for red team tool development, advanced persistent threat (APT) simulation, and proactive defensive modeling.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Computer Vision: Adversarial Attacks & Robustness
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Agentic search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The progress of artificial intelligence (AI) has made sophisticated methods available for cyberattacks and red team activities. These AI attacks can automate the process of penetrating a target or collecting sensitive data. The new methods can also accelerate the execution of the attacks. This review article examines the use of AI technologies in cybersecurity attacks. It also tries to describe typical targets for such attacks. We employed a scoping review methodology to analyze articles and identify AI methods, targets, and models that red teams can utilize to simulate cybercrime. From the 470 records screened, 11 were included in the review. Various cyberattack methods were identified, targeting sensitive data, systems, social media profiles, passwords, and URLs. The application of AI in cybercrime to develop versatile attack models presents an increasing threat. Furthermore, AI-based techniques in red team use can provide new ways to address these issues.

Problem

• Examining AI technologies in cybersecurity attacks and red team activities
• Identifying AI methods and targets for simulating cybercrime
• Assessing threats from AI-driven cyberattacks and potential countermeasures

Innovation

• AI automates target penetration and data collection
• AI accelerates execution of cyberattack methods
• Scoping review identifies AI models for red teams