# Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases

arXiv: https://arxiv.org/abs/2603.08760 · HTML: https://mlsanity.com/paper/2603.08760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 73 references that are in the map.
- First posted on arXiv 8 Mar 2026.

## Builds on (most cited first, 24 of 24)

- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Training Large Language Models to Reason in a Continuous Latent Space](https://mlsanity.com/paper/2412.06769/) (2024)
- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Deliberative Alignment: Reasoning Enables Safer Language Models](https://mlsanity.com/paper/2412.16339/) (2024)
- [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023)
- [Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming](https://mlsanity.com/paper/2501.18837/) (2025)
- [Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks](https://mlsanity.com/paper/2401.17263/) (2024)
- [Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems](https://mlsanity.com/paper/2405.06624/) (2024)
- [Stress Testing Deliberative Alignment for Anti-Scheming Training](https://mlsanity.com/paper/2509.15541/) (2025)
- [Safety Cases: How to Justify the Safety of Advanced AI Systems](https://mlsanity.com/paper/2403.10462/) (2024)
- [A sketch of an AI control safety case](https://mlsanity.com/paper/2501.17315/) (2025)
- [Towards evaluations-based safety cases for AI scheming](https://mlsanity.com/paper/2411.03336/) (2024)
- [How to evaluate control measures for LLM agents? A trajectory from today to superintelligence](https://mlsanity.com/paper/2504.05259/) (2025)
- [Safety case template for frontier AI: A cyber inability argument](https://mlsanity.com/paper/2411.08088/) (2024)
- [System Safety and Artificial Intelligence](https://mlsanity.com/paper/2202.09292/) (2022)
- [An alignment safety case sketch based on debate](https://mlsanity.com/paper/2505.03989/) (2025)
- [The BIG Argument for AI Safety Cases](https://mlsanity.com/paper/2503.11705/) (2025)
- [Ethical Assurance: A practical approach to the responsible design, development, and deployment of data-driven technologies](https://mlsanity.com/paper/2110.05164/) (2021)
- [Safety Cases: A Scalable Approach to Frontier AI Safety](https://mlsanity.com/paper/2503.04744/) (2025)
- [An Example Safety Case for Safeguards Against Misuse](https://mlsanity.com/paper/2505.18003/) (2025)
- [AI Companies Should Report Pre- and Post-Mitigation Safety Evaluations](https://mlsanity.com/paper/2503.17388/) (2025)
- [What Is AI Safety? What Do We Want It to Be?](https://mlsanity.com/paper/2505.02313/) (2025)
- [Assessing confidence in frontier AI safety cases](https://mlsanity.com/paper/2502.05791/) (2025)
- [AI threats to national security can be countered through an incident regime](https://mlsanity.com/paper/2503.19887/) (2025)

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

- [I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts](https://mlsanity.com/paper/2606.14327/) (2026)

## Abstract (arXiv)

This paper contributes to the nascent debate around safety cases for frontier AI systems. Safety cases are structured, defensible arguments that a system is acceptably safe to deploy in a given context. Historically, they have been used in safety-critical industries, such as aerospace, nuclear or automotive. As a result, safety cases for frontier AI have risen in prominence, both in the safety policies of leading frontier developers and in international research agendas proposed by leaders in generative AI, such as the Singapore Consensus on Global AI Safety Research Priorities and the International AI Safety Report. This paper appraises this work. We note that research conducted within the alignment community which draws explicitly on lessons from the assurance community has significant limitations. We therefore aim to rethink existing approaches to alignment safety cases. We offer lessons from existing methodologies within safety assurance and outline the limitations involved in the alignment community's current approach. Building on this foundation, we present a case study for a safety case focused on Deceptive Alignment and CBRN capabilities, drawing on existing, theoretical safety case "sketches" created by the alignment safety case community. Overall, we contribute holistic insights from the field of safety assurance via rigorous theory and methodologies that have been applied in safety-critical contexts. We do so in order to create a better foundational framework for robust, defensible and useful safety case methodologies which can help to assure the safety of frontier AI systems.