# How Much of Your Data Can Suck? Thresholds for Domain Performance and Emergent Misalignment in LLMs

arXiv: https://arxiv.org/abs/2509.19325 · HTML: https://mlsanity.com/paper/2509.19325/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 13 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://mlsanity.com/paper/2310.03693/) (2023)
- [Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs](https://mlsanity.com/paper/2502.17424/) (2025)
- [Re-Emergent Misalignment: How Narrow Fine-Tuning Erodes Safety Alignment in LLMs](https://mlsanity.com/paper/2507.03662/) (2025)

## Built on by (most cited first, 1 of 1)

- [Correct, Don't Delete: Mitigating Emergent Misalignment with Corrective Supervision](https://mlsanity.com/paper/2609.37624/) (2026)

## Abstract (arXiv)

This paper investigates the impact of incorrect data on the performance and safety of large language models (LLMs), specifically gpt-4o, during supervised fine-tuning (SFT). Although LLMs become increasingly vital across broad domains like finance, coding, law, and health, fine-tuning on incorrect data can lead to "emergent misalignment," producing harmful or deceptive outputs unrelated to the intended task. We evaluate gpt-4o models fine-tuned with varying ratios (10% to 90% correct) of both obviously and subtly incorrect data across four domains: coding, finance, health, and legal. Our findings show that even modest amounts of incorrect data (10-25%) dramatically degrade domain performance and not moral alignment. A clear threshold of at least 50% correct data is needed for models to consistently recover strong performance, though they rarely match the robustness and safety of the base model, which exhibits near-perfect alignment and zero dangerous completions out-of-the-box. This research emphasizes that the cost of incorrect data is heavy, highlighting the critical need for extremely high-quality data curation or, alternatively, leveraging robust base models without unnecessary fine-tuning for high-stakes applications.