The rapid advancement of frontier artificial intelligence (AI) systems has created an urgent need to secure the infrastructure on which these systems run. As AI advances, so do its associated threats: Not only could malicious actors target AI infrastructure to steal model weights or disrupt operations, but a misaligned AI system could exploit vulnerabilities in its own infrastructure to bypass safety monitors or exfiltrate itself.
To address this possibility, the authors surveyed 23 experts in early 2026 to determine whether formal methods—using mathematical techniques to reason about software behavior and, potentially, show that systems behave as specified—could meaningfully reduce risks associated with AI infrastructure vulnerabilities. The survey respondents were experts in the domains of formal methods, AI infrastructure, cybersecurity, software engineering, hardware architecture, and policy.
Using responses from the survey, the authors examined which components of the machine learning inference and training stacks are most amenable to formal verification, what security properties could be guaranteed, what development and adoption barriers exist, and how advances in AI-assisted formal methods might change the calculus. The authors offer preliminary recommendations and a starting point for a community-technical roadmap oriented toward frontier AI labs, the formal methods community, hardware vendors, government agencies, and the broader public.
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