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On the Impossible Safety of Large AI Models

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arxiv 2209.15259 v2 pith:FQSZTPOQ submitted 2022-09-30 cs.LG cs.AIcs.CR

El-Mahdi El-Mhamdi , Sadegh Farhadkhani , Rachid Guerraoui , Nirupam Gupta , Lê-Nguyên Hoang , Rafael Pinot , Sébastien Rouault , John Stephan This is my paper · ORCID
classification cs.LGcs.AIcs.CR
keywords largemodelslaimslearningmachinesecurityaccuracyaccurate
verification ladder T0 review T1 audit T2 compute T3 formal
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Large AI Models (LAIMs), of which large language models are the most prominent recent example, showcase some impressive performance. However they have been empirically found to pose serious security issues. This paper systematizes our knowledge about the fundamental impossibility of building arbitrarily accurate and secure machine learning models. More precisely, we identify key challenging features of many of today's machine learning settings. Namely, high accuracy seems to require memorizing large training datasets, which are often user-generated and highly heterogeneous, with both sensitive information and fake users. We then survey statistical lower bounds that, we argue, constitute a compelling case against the possibility of designing high-accuracy LAIMs with strong security guarantees.

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Cited by 5 Pith papers

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