The Fragility of Moral Judgment in Large Language Models

People increasingly use large language models (LLMs) for everyday moral and interpersonal guidance, yet these systems judge dilemmas as presented. We introduce a perturbation framework for testing the stability and manipulability of LLM moral judgments while holding the underlying moral conflict constant. Using 2,939 dilemmas from r/AmItheAsshole (January–March 2025), we generate three families of content perturbations—surface edits (lexical/structural noise), point-of-view shifts (voice and stance neutralization), and persuasion cues (minimal self-positioning, social proof, pattern admissions, victim framing). We additionally vary the evaluation instructions via protocol perturbations (ordering of model outputs, placement of task instructions, and an unstructured prompt without forced-choice scaffolding). We evaluated all scenario variants with four models (GPT-4.1, Claude 3.7 Sonnet, DeepSeek V3, Qwen2.5-72B) (N = 129,156 judgments). We estimated the self-consistency of models via test–retest (3 runs) and sampling-based normalized entropy.