arXiv:2606.23884v1 Announce Type: cross 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 depre
The increasing deployment of LLMs for sensitive applications like mental health is making the inconsistencies and inadequacies of their safety safeguards explicitly clear.
The widespread use of inadequately safeguarded LLMs in mental health applications poses significant risks to individuals and could lead to severe ethical and regulatory backlashes, undermining trust in AI.
The understanding that safeguards for LLMs are highly inconsistent and fail for many critical conditions beyond suicide and self-harm, requiring a more comprehensive and robust approach to AI safety.
- · AI Safety Researchers
- · Ethical AI Developers
- · AI Governance Consultancies
- · LLM Providers with Lax Safeguards
- · Early Adopters of LLM-based Mental Health Tools
- · Unregulated AI Health Tech
Increased scrutiny and potential regulation on the application of LLMs in sensitive domains like healthcare.
Development of more sophisticated, domain-specific AI safety frameworks and evaluation methodologies.
A public perception shift leading to greater demand for transparency and accountability from AI developers for harmful outputs.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI