How do we prevent AI agents from going rogue? It starts with a new kind of measurement | Bruce Schneier and Barath Raghavan
TL;DR
Like genies of folklore, AI agents take their instructions literally – to potentially disastrous effect. We must track their ability to do what we actually mean In July, Hugging Face, a company that hosts much of the world’s AI software and open-source AI models, was hacked. A malicious dataset had been used to run code on one of its servers.
Nauti's Take
Intent fidelity as a measurable property would be real progress, because agents would then be judged on how they behave under ambiguous instructions and not on success rates alone. The risk is visible in the incident itself: a model executing instructions literally ran code from a poisoned dataset and harvested credentials.
Until solid metrics exist, an in-house test suite with ambiguous prompts, tightly scoped permissions and complete logs stands in for the missing seal of approval.
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