# Safety cases for frontier AI

arXiv: https://arxiv.org/abs/2410.21572 · HTML: https://mlsanity.com/paper/2410.21572/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2024.

## Built on by (most cited first, 18 of 18)

- [Levels of Autonomy for AI Agents](https://mlsanity.com/paper/2506.12469/) (2025)
- [Evaluating Frontier Models for Stealth and Situational Awareness](https://mlsanity.com/paper/2505.01420/) (2025)
- [Evaluation-Driven Development and Operations of LLM Agents: A Process Model and Reference Architecture](https://mlsanity.com/paper/2411.13768/) (2024)
- [The Singapore Consensus on Global AI Safety Research Priorities](https://mlsanity.com/paper/2506.20702/) (2025)
- [STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports](https://mlsanity.com/paper/2508.09853/) (2025)
- [Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework](https://mlsanity.com/paper/2507.12872/) (2025)
- [Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems](https://mlsanity.com/paper/2606.28425/) (2026)
- [International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty](https://mlsanity.com/paper/2503.18956/) (2025)
- [An Example Safety Case for Safeguards Against Misuse](https://mlsanity.com/paper/2505.18003/) (2025)
- [Misalignment or misuse? The AGI alignment tradeoff](https://mlsanity.com/paper/2506.03755/) (2025)
- [LURE: Live-Usage Replay Evaluations for Reducing Evaluation Awareness](https://mlsanity.com/paper/2605.26438/) (2026)
- [Against racing to AGI: Cooperation, deterrence, and catastrophic risks](https://mlsanity.com/paper/2507.21839/) (2025)
- [Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands](https://mlsanity.com/paper/2605.15164/) (2026)
- [Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations](https://mlsanity.com/paper/2412.15433/) (2024)
- [Open Problems in AI Incident Governance](https://mlsanity.com/paper/2607.05163/) (2026)
- [Incentive Aware AI Regulations: A Credal Characterisation](https://mlsanity.com/paper/2603.05175/) (2026)
- [Questionnaire Responses Do not Capture the Safety of AI Agents](https://mlsanity.com/paper/2603.14417/) (2026)
- [Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech](https://mlsanity.com/paper/2607.05165/) (2026)

## Abstract (arXiv)

As frontier artificial intelligence (AI) systems become more capable, it becomes more important that developers can explain why their systems are sufficiently safe. One way to do so is via safety cases: reports that make a structured argument, supported by evidence, that a system is safe enough in a given operational context. Safety cases are already common in other safety-critical industries such as aviation and nuclear power. In this paper, we explain why they may also be a useful tool in frontier AI governance, both in industry self-regulation and government regulation. We then discuss the practicalities of safety cases, outlining how to produce a frontier AI safety case and discussing what still needs to happen before safety cases can substantially inform decisions.