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This paper proposes a pragmatic, cybersecurity-inspired framework for monitoring behavioral indicators that could signal the progression of AI systems toward catastrophic risks. By defining clear metrics and thresholds across multiple capability dimensions, it equips researchers and policymakers with evidence-based protocols to detect rogue AI advancement early. The work bridges technical monitoring with national security methodologies to address existential threats in a structured manner.
Reducing Catastrophic Risk from AI with Systematic Monitoring and Evaluation of Rogue AI Progression
arXiv:2609.03189v1 Announce Type: cross Abstract: This article presents a structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artificial intelligence systems. We adopt a pragmatic approach, inspired by established methodologies in cybersecurity and national security. By establishing clear metrics, indicators, and thresholds across multiple dimensions of AI capability and behavior, this framework enables researchers and policymakers to implement evidence-based monitoring protocols.
Authors:T. Bauer, W.P. Kegelmeyer, E. Begoli, A. Sadovnik, T. Emerson, C. Corley, N. Generous, J. Moore, B. Bartoldson, R. Goldhan, M. Goldman, M. Greaves, M.J.D. Vermeer, B. MacLennan, D. Schulker, N. VanHoudnos, J. Bansemer, Y. Bengio
Abstract:This article presents a structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artificial intelligence systems. We adopt a pragmatic approach, inspired by established methodologies in cybersecurity and national security. By establishing clear metrics, indicators, and thresholds across multiple dimensions of AI capability and behavior, this framework enables researchers and policymakers to implement evidence-based monitoring protocols.
Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2609.03189 [cs.CY]
(or arXiv:2609.03189v1 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2609.03189 arXiv-issued DOI via DataCite (pending registration)
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