Reproduced by the Machine: Rape Myths in Large Language Model Responses Regarding Non-Consensual Intimate Image Dissemination

As large language models (LLMs) increasingly mediate access to information and support, their influence on sex offense survivors' help-seeking experiences warrants critical attention. This study examines whether LLMs reproduce rape myths and victim-blaming attitudes when responding to scenarios involving the non-consensual dissemination of intimate images (NCDII). Four leading LLMs were presented with 10 text-based vignettes systematically manipulating 3 contextual variables known to affect victim blaming: survivor's portrayed role in the dissemination, relationship duration, and degree of physical exposure. Each model completed a seven-item victim-blaming questionnaire per vignette. Four hundred unique outputs were produced and analyzed using mixed-design repeated-measures ANOVA. While all models attributed maximal blame to perpetrators in direct assessments, substantial differences emerged in implicit victim blaming. Grok 3 consistently exhibited higher levels, particularly in scenarios aligned with common rape myths. GPT-4o and Claude 4 Sonnet showed moderate levels, while Gemini 2.5 Pro displayed minimal blaming. Claude 4 Sonnet declined to respond to high-exposure scenarios due to its content restriction policy. The models displayed consistent response patterns across vignettes, but only Grok 3 demonstrated sensitivity to escalating myth salience. The findings highlight the risk that some LLMs may inadvertently reinforce victim-blaming responses and judgments consistent with rape myths when responding to NCDII. Given their growing role, these findings hold practical implications for developers, users, and policymakers regarding the deployment of AI tools in emotionally vulnerable situations.