Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models
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arXiv
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2025
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| author | Pan, Jiazhen Jian, Bailiang Hager, Paul Zhang, Yundi Liu, Che Jungmann, Friedrike Li, Hongwei Bran You, Chenyu Wu, Junde Zhu, Jiayuan Liu, Fenglin Liu, Yuyuan Bubeck, Niklas Wachinger, Christian Chen Chen Gong, Zhenyu Ouyang, Cheng Kaissis, Georgios Wiestler, Benedikt Rueckert, Daniel |
| author_facet | Pan, Jiazhen Jian, Bailiang Hager, Paul Zhang, Yundi Liu, Che Jungmann, Friedrike Li, Hongwei Bran You, Chenyu Wu, Junde Zhu, Jiayuan Liu, Fenglin Liu, Yuyuan Bubeck, Niklas Wachinger, Christian Chen Chen Gong, Zhenyu Ouyang, Cheng Kaissis, Georgios Wiestler, Benedikt Rueckert, Daniel |
| contents | Ensuring the safety and reliability of large language models (LLMs) in clinical practice is critical to prevent patient harm. However, LLMs are advancing so rapidly that static benchmarks quickly become obsolete or prone to overfitting, yielding a misleading picture of model trustworthiness. Here we introduce a Dynamic, Automatic, and Systematic (DAS) red-teaming framework that continuously stress-tests LLMs across four safety-critical axes: robustness, privacy, bias/fairness, and hallucination. Validated against board-certified clinicians with high concordance, a suite of adversarial agents autonomously mutates clinical test cases to uncover vulnerabilities in real time. Applying DAS to 15 proprietary and open-source LLMs revealed a profound gap between high static benchmark performance and low dynamic reliability - the ``Benchmarking Gap''. Despite median MedQA accuracy exceeding 80\%, 94\% of previously correct answers failed our dynamic robustness tests. Crucially, this brittleness generalized to the realistic, open-ended HealthBench dataset, where top-tier models exhibited failure rates exceeding 70\% and stark shifts in model rankings across evaluations, suggesting that high scores on established static benchmarks may reflect superficial memorization. We observed similarly high failure rates across other domains: privacy leaks were elicited in 86\% of scenarios, cognitive-bias priming altered clinical recommendations in 81\% of fairness tests, and we identified hallucination rates exceeding 74\% in widely used models. By converting medical LLM safety analysis from a static checklist into a dynamic stress-test, DAS provides a foundational, scalable, and living platform to surface the latent risks that must be addressed before the next generation of medical AI can be safely deployed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models Pan, Jiazhen Jian, Bailiang Hager, Paul Zhang, Yundi Liu, Che Jungmann, Friedrike Li, Hongwei Bran You, Chenyu Wu, Junde Zhu, Jiayuan Liu, Fenglin Liu, Yuyuan Bubeck, Niklas Wachinger, Christian Chen Chen Gong, Zhenyu Ouyang, Cheng Kaissis, Georgios Wiestler, Benedikt Rueckert, Daniel Machine Learning Ensuring the safety and reliability of large language models (LLMs) in clinical practice is critical to prevent patient harm. However, LLMs are advancing so rapidly that static benchmarks quickly become obsolete or prone to overfitting, yielding a misleading picture of model trustworthiness. Here we introduce a Dynamic, Automatic, and Systematic (DAS) red-teaming framework that continuously stress-tests LLMs across four safety-critical axes: robustness, privacy, bias/fairness, and hallucination. Validated against board-certified clinicians with high concordance, a suite of adversarial agents autonomously mutates clinical test cases to uncover vulnerabilities in real time. Applying DAS to 15 proprietary and open-source LLMs revealed a profound gap between high static benchmark performance and low dynamic reliability - the ``Benchmarking Gap''. Despite median MedQA accuracy exceeding 80\%, 94\% of previously correct answers failed our dynamic robustness tests. Crucially, this brittleness generalized to the realistic, open-ended HealthBench dataset, where top-tier models exhibited failure rates exceeding 70\% and stark shifts in model rankings across evaluations, suggesting that high scores on established static benchmarks may reflect superficial memorization. We observed similarly high failure rates across other domains: privacy leaks were elicited in 86\% of scenarios, cognitive-bias priming altered clinical recommendations in 81\% of fairness tests, and we identified hallucination rates exceeding 74\% in widely used models. By converting medical LLM safety analysis from a static checklist into a dynamic stress-test, DAS provides a foundational, scalable, and living platform to surface the latent risks that must be addressed before the next generation of medical AI can be safely deployed. |
| title | Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.00923 |