One Year Later...The Harms Persist, But So Do We!
arXiv:2606.23884v1 Announce Type: new Abstract: General-purpose large language models (LLMs) are increasingly used for mental health-related conversations, yet safety safeguards remain inadequate and inconsistent across clinical conditions. This study evaluates six proprietary LLMs across 16 DSM-5 conditions using four adversarial attack variants, introducing an eight-dimension harm taxonomy and a multi-dimensional evaluation framework. Results show that safeguards hold reliably only for suicide and self-harm, while conditions such as eating disorders, substance use disorder, and major depressive disorder exhibit failure rates of up to 100%. We argue that ethical design and deployment of these LLMs demand clearly defined harm categories across clinical conditions and implementation of safeguards accordingly. Until such safeguards are in place, these models pose significant risks to vulnerable populations, making their growing integration into educational settings a particularly concerning.
[2606.23884] One Year Later...The Harms Persist, But So Do We!
[Submitted on 22 Jun 2026]
Title:One Year Later...The Harms Persist, But So Do We!
View a PDF of the paper titled One Year Later...The Harms Persist, But So Do We!, by Annika Marie Schoene and 3 other authors
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Abstract:General-purpose large language models (LLMs) are increasingly used for mental health-related conversations, yet safety safeguards remain inadequate and inconsistent across clinical conditions. This study evaluates six proprietary LLMs across 16 DSM-5 conditions using four adversarial attack variants, introducing an eight-dimension harm taxonomy and a multi-dimensional evaluation framework. Results show that safeguards hold reliably only for suicide and self-harm, while conditions such as eating disorders, substance use disorder, and major depressive disorder exhibit failure rates of up to 100%. We argue that ethical design and deployment of these LLMs demand clearly defined harm categories across clinical conditions and implementation of safeguards accordingly. Until such safeguards are in place, these models pose significant risks to vulnerable populations, making their growing integration into educational settings a particularly concerning.
Comments: 20 pages, 8 tables
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.23884 [cs.CL]
(or arXiv:2606.23884v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.23884
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Annika Marie Schoene [view email] [v1] Mon, 22 Jun 2026 19:30:14 UTC (460 KB)
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