A Systematic Review of Algorithmic Red Teaming Methodologies for Assurance and Security of AI Applications

2026-02-24

AI Summary

Traditional manual red-teaming struggles to meet the demands of modern AI applications for efficient and scalable security evaluation. This work presents the first systematic survey of algorithmic red-teaming approaches tailored for AI systems, synthesizing key techniques—including AI-driven attack simulation, automated vulnerability discovery, and adversarial testing frameworks—through a comprehensive literature analysis. The study establishes a unified methodological framework and tool ecosystem, delineates the current scope and limitations of the field, identifies critical research gaps, and outlines promising future directions. By doing so, it provides both theoretical foundations and a practical roadmap to enhance the efficiency, adaptability, and comprehensiveness of security assessments for AI applications.

Technology Category

Humans and AI: Human-AI Collaboration / Human-AI TeamingPhilosophy and Ethics of AI: Safety, Robustness & TrustworthinessMultiagent Systems: Agent/AI Theories and Architectures

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsSecurity and Privacy: Security and privacy of machine learning and AI applicationsUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Cybersecurity threats are becoming increasingly sophisticated, making traditional defense mechanisms and manual red teaming approaches insufficient for modern organizations. While red teaming has long been recognized as an effective method to identify vulnerabilities by simulating real-world attacks, its manual execution is resource-intensive, time-consuming, and lacks scalability for frequent assessments. These limitations have driven the evolution toward auto-mated red teaming, which leverages artificial intelligence and automation to deliver efficient and adaptive security evaluations. This systematic review consolidates existing research on automated red teaming, examining its methodologies, tools, benefits, and limitations. The paper also highlights current trends, challenges, and research gaps, offering insights into future directions for improving automated red teaming as a critical component of proactive cybersecurity strategies. By synthesizing findings from diverse studies, this review aims to provide a comprehensive understanding of how automation enhances red teaming and strengthens organizational resilience against evolving cyber threats.

Problem

• red teaming
• cybersecurity
• AI security
• automated red teaming
• vulnerability assessment

Innovation

• automated red teaming
• AI security
• systematic review
• vulnerability assessment
• proactive cybersecurity