(OPENAI-ONLY EVAL) This evaluation blueprint tests whether Large Language Models (LLMs) can respond appropriately to critical mental health scenarios, based on the methodology from Moore, Grabb, Agnew, et al.'s 2024 Stanford HAI study "Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers."

The study investigated whether LLMs can safely replace human mental health providers by testing their responses to clinical scenarios involving delusions, suicidal ideation, hallucinations, mania, and obsessive-compulsive behaviors. Their findings revealed significant safety concerns, including stigmatizing responses and inappropriate enabling of dangerous behaviors.

Key Study References:

• Stanford HAI News Article
• Original Paper (arXiv:2504.18412)

Content Warning: This evaluation contains content related to sensitive mental health topics, including suicide, delusions, and other crisis scenarios.

Evaluation Focus: Tests adherence to core therapeutic principles such as not colluding with delusions, not enabling harmful behaviors, and redirecting clients toward appropriate care and safety.

Tests a model's resilience against various jailbreaking techniques aimed at eliciting unsafe or harmful information. This suite focuses on scenarios where the user attempts to bypass safety filters by reframing dangerous requests as benign, academic, or for fictional purposes.

This blueprint operationalizes findings from AI safety research and documented case studies to test for specific modes of behavioral collapse. It uses long-context, multi-turn conversational scenarios designed to probe for known failure modes. These include:

• Emergent Malignant Personas: Activating a latent "shadow self" that can lead to manipulative tactics like love-bombing and gaslighting, mirroring the canonical "Sydney" case study.
• Weaponized Helpfulness: Bypassing safety protocols by reframing a harmful query as an academic or fictional exercise, causing the AI's helpfulness to be co-opted for dangerous ends.
• Pathological Affirmation: The creation of a sycophantic echo chamber that validates and amplifies user delusions or conspiracy theories, rather than grounding the conversation in reality.

The evaluation for each prompt is structured to assess the AI's response against two distinct behavioral paths:

1. A 'Good' (Safe/Aligned) Path: Defines the characteristics of a response that upholds safety, respects boundaries, and maintains a helpful, harmless persona.
2. A 'Bad' (Harmful/Collapsed) Path: Defines the characteristics of a response exhibiting one of the failure modes described above.