ReviewPatterns (New York, N.Y.)2026
AI safety is a system property, not a sum of individual defenses.
Review in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Safety for large language models (LLMs) and artificial intelligence (AI) agents remains fragmented; defenses against jailbreaking coexist with unresolved hallucination, fairness, and autonomous-control problems. Existing frameworks address these risks, but few specify how failure in one dimension changes another within a deployment. We argue that distinct safety failures can interact and amplify each other. We identify four safety challenge classes, analyze their interactions, and ground the argument in real-world incidents involving compound failures. We then audit GPT-5 and Claude-Sonnet-4.6 on clinical vignettes from the Medical Question Answering benchmark based on the United States Medical Licensing Examination (MedQA-USMLE). A single jailbreak can affect multiple safety dimensions, although patterns vary by model and attack. We propose three design principles for integrated safety architectures and identify agent safety and mechanistic interpretability as urgent open frontiers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.