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arXiv · 2505.18003

An Example Safety Case for Safeguards Against Misuse

Joshua Clymer·Jonah Weinbaum·Robert Kirk·Kimberly Mai·Selena Zhang·Xander Davies
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Abstract

Existing evaluations of AI misuse safeguards provide a patchwork of evidence that is often difficult to connect to real-world decisions. To bridge this gap, we describe an end-to-end argument (a "safety case") that misuse safeguards reduce the risk posed by an AI assistant to low levels. We first describe how a hypothetical developer red teams safeguards, estimating the effort required to evade them. Then, the developer plugs this estimate into a quantitative "uplift model" to determine how much barriers introduced by safeguards dissuade misuse (https://www.aimisusemodel.com/). This procedure provides a continuous signal of risk during deployment that helps the developer rapidly respond to emerging threats. Finally, we describe how to tie these components together into a simple safety case. Our work provides one concrete path -- though not the only path -- to rigorously justifying AI misuse risks are low.

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Joshua Clymer, Jonah Weinbaum, Robert Kirk, Kimberly Mai, Selena Zhang, Xander Davies. 2025-05-23. An Example Safety Case for Safeguards Against Misuse. https://arxiv.org/abs/2505.18003

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