# Measurement challenges in AI catastrophic risk governance and safety frameworks

arXiv: https://arxiv.org/abs/2410.00608 · HTML: https://mlsanity.com/paper/2410.00608/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2024.

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

- [Quantifying CBRN Risk in Frontier Models](https://mlsanity.com/paper/2510.21133/) (2025)
- [Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers](https://mlsanity.com/paper/2609.08789/) (2026)

## Abstract (arXiv)

Safety frameworks represent a significant development in AI governance: they are the first type of publicly shared catastrophic risk management framework developed by major AI companies and focus specifically on AI scaling decisions. I identify six critical measurement challenges in their implementation and propose three policy recommendations to improve their validity and reliability.