Red Teaming Clinical AI: Adversarial Safety and Escalation-Failure Analysis

Benchmark-based evaluation of clinical AI systems provides a baseline measure of performance but does not guarantee robustness under adversarial or out-of-distribution conditions. Red teaming—systematic adversarial probing—reveals failure modes invisible in standard evaluation. This paper describes methodology for red teaming clinical AI systems: identifying classes of inputs designed to trigger failures, analyzing error patterns, and categorizing failures by severity (benign, clinically relevant, catastrophic). I introduce an escalation-failure taxonomy that distinguishes between failures that remain bounded (confined to the immediate case) and those that escalate (damage trust, lead to downstream errors, or prevent human override). I detail red-teaming strategies: adversarial case construction (edge cases, ambiguous presentations, synthetic anomalies), input manipulation (data quality degradation, missing information), and jailbreak attempts (prompts designed to bypass safety guidelines). Results from red-teaming sessions on a clinical AI system illustrate how adversarial analysis reveals gaps in standard benchmarks. Red teaming should be integrated into clinical AI safety evaluation as a complement to benchmark testing, enabling earlier detection of risks before deployment.