# Epistemic Fragility in Large Language Models: Prompt Framing Systematically Modulates Misinformation Correction

arXiv: https://arxiv.org/abs/2511.22746 · HTML: https://mlsanity.com/paper/2511.22746/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs](https://mlsanity.com/paper/2601.04435/) (2026)
- [Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively](https://mlsanity.com/paper/2610.17703/) (2026)

## Abstract (arXiv)

As large language models (LLMs) rapidly displace traditional expertise, their capacity to correct misinformation has become a core concern. We investigate the idea that prompt framing systematically modulates misinformation correction - something we term 'epistemic fragility'. We manipulated prompts by open-mindedness, user intent, user role, and complexity. Across ten misinformation domains, we generated 320 prompts and elicited 2,560 responses from four frontier LLMs, which were coded for strength of misinformation correction and rectification strategy use. Analyses showed that creative intent, expert role, and closed framing led to a significant reduction in correction likelihood and effectiveness of used strategy. We also found striking model differences: Gemini 2.5 Pro had 74% lower odds of strong correction than Claude Sonnet 4.5. These findings highlight epistemic fragility as an important structural property of LLMs, challenging current guardrails and underscoring the need for alignment strategies that prioritize epistemic integrity over conversational compliance.