Emerging studies demonstrate that machine learning (ML) has the potential to improve the detection capabilities of network intrusion detection systems (NIDS) against evolving cyber threats. However, recent adversarial ML (AML) studies have revealed critical ML vulnerabilities. This paper presents innovative multistage red-teaming techniques to evaluate the robustness of ML-NIDS in real-world adversarial settings.