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

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.

Key Contributions

Systematic synthesis of automated/algorithmic red teaming methodologies across the AI security literature
Comparison of tools, benefits, and limitations of automated versus manual red teaming approaches
Identification of current research gaps and future directions for scalable AI security evaluation

🛡️ Threat Analysis

Input Manipulation Attack

Algorithmic red teaming for AI applications directly involves automated adversarial input generation — systematically crafting inputs to elicit misclassification or unsafe model behavior at inference time.