cs.GTOct 19, 2026

Fundamental Impossibility Results in AI Safety: Trading Off between Desiderata

Authors: Ida Mattsson, Ratip Emin Berker, Emanuel Tewolde, Ioannis Anagnostides, Kiriaki Fragkia, Xinyu Liu, Alexander Heckett, Jiayuan Liu, 

Organizations: Carnegie Mellon University · Bar-Ilan University · Czech Technical University · University of Pittsburgh

Abstract

Is there a fundamental tradeoff between desiderata for safe AI systems, such as harmlessness and helpfulness? We answer by formalizing the interaction between AI systems and users as a game with three players: a defender and an attacker that are computationally or informationally bounded, and an AI that provides public advice and is less constrained. Our main result shows that there is a fundamental tradeoff in providing advice that helps the defender while advice remains public. We also consider whether the information that the AI shares can be manipulated to benefit only the defender and conclude with a discussion of how we can use these results in designing safer AI systems.

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