# Governable AI: Provable Safety Under Extreme Threat Models

**Authors:** Donglin Wang, Weiyun Liang, Chunyuan Chen, Jing Xu, Yulong Fu  
**arXiv:** [2508.20411](https://arxiv.org/abs/2508.20411)  
**Date:** 2025-08-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2508.20411v1/

## Key points

- Potential security risks associated with AI systems are becoming increasingly severe, particularly in critical scenarios with existential implications.
- Traditional approaches to AI safety face limitations when dealing with extreme motivations and unlimited intelligence in AI systems, leading to security vulnerabilities in high-stakes situations.
- A novel framework called Governable AI (GAI) is proposed to address these challenges, relying on external enforcement through cryptographic mechanisms that are computationally impractical to breach.
- Core components of the GAI framework include a robust rule enforcement module (REM), governance rules defining boundaries for AI operation, and a governable secure super-platform (GSSP) ensuring end-to-end protection against compromise or subversion by AI entities.
- By decoupling governance rules from the technical platform, the GAI framework establishes a feasible and scalable pathway for ensuring safety governance across diverse applications and scenarios.

## Summary

In the rapidly evolving landscape of artificial intelligence (AI), the potential security risks associated with AI systems are becoming increasingly severe. This is particularly concerning in critical scenarios that could have existential implications. The possibility of AI becoming uncontrollable, manipulated, or bypassing safety mechanisms intentionally can lead to catastrophic systemic failures. Traditional approaches to AI safety face limitations when dealing with extreme motivations and unlimited intelligence in AI systems. As a result, these methods cannot guarantee security in high-stakes situations. To address this pressing challenge, a novel framework known as Governable AI (GAI) is proposed. This paradigm shift relies on external enforcement through cryptographic mechanisms that are computationally impractical to breach even for future generations of AI systems. The core components of the GAI framework include a robust rule enforcement module (REM), governance rules defining boundaries for AI operation, and a governable secure super-platform (GSSP) ensuring end-to-end protection against compromise or subversion by AI entities. By decoupling governance rules from the technical platform, the GAI framework establishes a feasible and scalable pathway for ensuring safety governance across diverse applications and scenarios. The REM module enforces foundational principles set forth by governance rules while the GSSP guarantees non-bypassability, tamper-resistance, and unforgeability to eliminate all identified attack vectors effectively. This paper provides a rigorous formal proof of the security properties embedded within this mechanism and demonstrates its efficacy through a prototype implementation evaluated in representative high-stakes scenarios. Authored by Donglin Wang et al., under the title "Governable AI: Provable Safety Under Extreme Threat Models," this research offers a comprehensive exploration into safeguarding against potential risks posed by advanced AI systems operating under extreme threat models.