# Safety case template for frontier AI: A cyber inability argument

arXiv: https://arxiv.org/abs/2411.08088 · HTML: https://mlsanity.com/paper/2411.08088/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2024.

## Built on by (most cited first, 19 of 19)

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- [A Framework for Evaluating Emerging Cyberattack Capabilities of AI](https://mlsanity.com/paper/2503.11917/) (2025)
- [An alignment safety case sketch based on debate](https://mlsanity.com/paper/2505.03989/) (2025)
- [STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports](https://mlsanity.com/paper/2508.09853/) (2025)
- [A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management](https://mlsanity.com/paper/2502.06656/) (2025)
- [Practical challenges of control monitoring in frontier AI deployments](https://mlsanity.com/paper/2512.22154/) (2025)
- [Systematic Hazard Analysis for Frontier AI using STPA](https://mlsanity.com/paper/2506.01782/) (2025)
- [Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework](https://mlsanity.com/paper/2507.12872/) (2025)
- [What AI evaluations for preventing catastrophic risks can and cannot do](https://mlsanity.com/paper/2412.08653/) (2024)
- [Mapping AI Benchmark Data to Quantitative Risk Estimates Through Expert Elicitation](https://mlsanity.com/paper/2503.04299/) (2025)
- [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)
- [Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations](https://mlsanity.com/paper/2412.15433/) (2024)
- [AI threats to national security can be countered through an incident regime](https://mlsanity.com/paper/2503.19887/) (2025)
- [Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases](https://mlsanity.com/paper/2603.08760/) (2026)
- [Comprehensive AI governance requires addressing non-model gains](https://mlsanity.com/paper/2606.00047/) (2026)
- [OntoGSN: An Ontology-Based Framework for Semantic Management and Extension of Assurance Cases](https://mlsanity.com/paper/2506.11023/) (2025)

## Abstract (arXiv)

Frontier artificial intelligence (AI) systems pose increasing risks to society, making it essential for developers to provide assurances about their safety. One approach to offering such assurances is through a safety case: a structured, evidence-based argument aimed at demonstrating why the risk associated with a safety-critical system is acceptable. In this article, we propose a safety case template for offensive cyber capabilities. We illustrate how developers could argue that a model does not have capabilities posing unacceptable cyber risks by breaking down the main claim into progressively specific sub-claims, each supported by evidence. In our template, we identify a number of risk models, derive proxy tasks from the risk models, define evaluation settings for the proxy tasks, and connect those with evaluation results. Elements of current frontier safety techniques - such as risk models, proxy tasks, and capability evaluations - use implicit arguments for overall system safety. This safety case template integrates these elements using the Claims Arguments Evidence (CAE) framework in order to make safety arguments coherent and explicit. While uncertainties around the specifics remain, this template serves as a proof of concept, aiming to foster discussion on AI safety cases and advance AI assurance.