Automated Safety Evaluations Across 20 Large Language Models: The Aymara LLM Risk and Responsibility Matrix

• URL: http://arxiv.org/abs/2507.14719v1
• Date: Sat, 19 Jul 2025 18:49:16 GMT
• Title: Automated Safety Evaluations Across 20 Large Language Models: The Aymara LLM Risk and Responsibility Matrix
• Authors: Juan Manuel Contreras,
• Abstract summary: Aymara AI is a programmatic platform for generating and administering customized, policy-grounded safety evaluations.<n>It transforms natural-language safety policies into adversarial prompts and scores model responses using an AI-based rater validated against human judgments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) become increasingly integrated into real-world applications, scalable and rigorous safety evaluation is essential. This paper introduces Aymara AI, a programmatic platform for generating and administering customized, policy-grounded safety evaluations. Aymara AI transforms natural-language safety policies into adversarial prompts and scores model responses using an AI-based rater validated against human judgments. We demonstrate its capabilities through the Aymara LLM Risk and Responsibility Matrix, which evaluates 20 commercially available LLMs across 10 real-world safety domains. Results reveal wide performance disparities, with mean safety scores ranging from 86.2% to 52.4%. While models performed well in well-established safety domains such as Misinformation (mean = 95.7%), they consistently failed in more complex or underspecified domains, notably Privacy & Impersonation (mean = 24.3%). Analyses of Variance confirmed that safety scores differed significantly across both models and domains (p < .05). These findings underscore the inconsistent and context-dependent nature of LLM safety and highlight the need for scalable, customizable tools like Aymara AI to support responsible AI development and oversight.

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