自动化红队方法提升AI安全评估效率与适应性。
A Systematic Review of Algorithmic Red Teaming Methodologies for Assurance and Security of AI Applications
- 用AI和自动化替代人工红队，实现高效攻击模拟。
- 系统梳理现有方法，揭示其在漏洞发现上的优势与局限。
- 适合关注AI安全防护的工程师与研究人员参考。
随着网络攻击日益复杂，传统防御机制和人工红队方法已难以满足现代组织需求。红队通过模拟真实攻击识别漏洞，但人工执行耗时耗力且难以规模化。这一挑战推动了自动化红队的发展，利用人工智能与自动化技术实现高效、可适应的安全评估。本文系统综述了自动化红队的研究现状，涵盖其方法、工具、优势与不足，指出现有趋势、挑战及研究空白，并为未来改进提供方向。通过整合多篇研究，本综述旨在深入理解自动化如何增强红队能力，强化组织应对不断演进的网络威胁的韧性。
原文摘要 · Abstract (English)
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.
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