Systems analyses of a frontier-lab AI coding agent scenario using STECA, STPA, and FRAM reveal unverifiable governance loops, ineffective control delays, and gradual safeguard erosion, supporting the addition of systems-level methods to model-focused AI evaluations.
arXiv preprint arXiv:2511.15846 , year =
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A methodology to derive targeted Loss of Control mitigations by backchaining from AI errors on national security benchmarks to specific affordances and permissions.
Frontier AI safety policies have a structural coordination gap caused by diffuse benefits and concentrated costs, which can be addressed by adapting precommitment and shared response protocols from other high-risk domains.
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Exploring Systems-Thinking Approaches to Loss of Control Risk
Systems analyses of a frontier-lab AI coding agent scenario using STECA, STPA, and FRAM reveal unverifiable governance loops, ineffective control delays, and gradual safeguard erosion, supporting the addition of systems-level methods to model-focused AI evaluations.
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Backchaining Loss of Control Mitigations from Mission-Specific Benchmarks in National Security
A methodology to derive targeted Loss of Control mitigations by backchaining from AI errors on national security benchmarks to specific affordances and permissions.
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Frontier AI safety policies have a structural coordination gap caused by diffuse benefits and concentrated costs, which can be addressed by adapting precommitment and shared response protocols from other high-risk domains.
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