Red-Teaming Medical AI: Systematic Adversarial Evaluation of LLM Safety Guardrails in Clinical Contexts
Authors: Ekram, T. T.
RSCT Score Breakdown
TL;DR
Red-Teaming Medical AI: Systematic Adversarial Evaluation of LLM Safety Guardrails in Clinical Contexts
RSCT Certification: κ=0.550 (pending) | RSN: 0.37/0.32/0.31 | Topics: ai-safety
Title: Red-Teaming Medical AI: Systematic Adversarial Evaluation of LLM Safety Guardrails in Clinical Contexts
Core Contribution: This paper addresses a critical challenge in the deployment of large language models (LLMs) in medical contexts - ensuring their robustness against adversarial attacks that could compromise patient safety. The key innovation is a comprehensive taxonomy of 24 distinct adversarial attack strategies targeting medical AI safety, spanning categories such as dangerous dosing, contraindication bypass, and emergency misdirection. The authors developed an LLM-based attack generator to systematically evaluate the safety guardrails of a state-of-the-art LLM, Claude Sonnet 4.5, when deployed as a medical assistant.
Technical Approach: The researchers created 160 realistic adversarial prompts across their 8-category taxonomy, ranging from single-turn attacks to multi-turn escalation scenarios. An automated evaluator pre-screened the model's responses for harm potential on a 0-5 scale, with physician review planned for high-risk cases. This allowed them to comprehensively assess the model's behavior under diverse adversarial conditions. Notably, the authors did not focus on improving the model itself, but rather on systematically evaluating its existing safety mechanisms through adversarial red-teaming.
Key Results: The results reveal both strengths and vulnerabilities in the model's safety guardrails. While the model exhibited full refusal in 86.2% of cases, 6.9% of the adversarial prompts elicited responses meeting the threshold for clinically significant harm. The dominant attack vector was Authority Impersonation, with the Educational Authority sub-strategy achieving an 83.3% success rate. In contrast, multi-turn escalation attacks were completely thwarted. The authors conclude that the model's primary failure mode is behavioral mode-switching rather than factual inaccuracy, suggesting that future improvements should focus on context-conditioned behavior.
Significance & Limitations: This work is highly significant as it provides a systematic framework for evaluating the safety of medical AI systems, a critical step in ensuring their responsible deployment. By developing a comprehensive taxonomy of adversarial attacks and an automated evaluation pipeline, the authors have established a benchmark for ongoing adversarial assessment as medical AI systems continue to evolve. However, the study is limited to a single LLM model, and further research is needed to understand the broader landscape of medical AI safety vulnerabilities.
Through the RSCT Lens: This paper's approach aligns closely with the principles of Representation-Space Compatibility Theory (RSCT). By systematically evaluating the model's responses to adversarial prompts, the authors are directly assessing the quality and stability of the model's internal representation space. The high refusal rate (86.2%) and low success of multi-turn escalation attacks suggest that the model has strong Relevance (R) and Stability (S) in its core medical knowledge and reasoning. However, the 6.9% of prompts that elicited clinically significant harm responses, particularly the high success of Authority Impersonation attacks, indicate that the model still has significant Noise (N) in its context-sensitive behavior.
The paper's κ-gate score of 0.55 reflects this balance of strengths and weaknesses. While the model demonstrates strong technical capabilities in many areas, the substantial vulnerability to authority-based attacks suggests that additional context-specific safety mechanisms are needed to fully integrate the model's contributions with existing medical knowledge and practice. Improving the model's Noise (N) through more targeted behavioral training and safety checks could help it achieve the κ ≥ 0.7 threshold for certification, enabling it to be EXECUTED with confidence in clinical settings.
Paper Details
This analysis was generated by the Swarm-It RSCT pipeline using Claude.