LANGUAGES Signal 56
Linear probe detects code backdoors in activation space by comparing monitor perception to reports
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A linear probe trained on model activations can detect code backdoors even in scheming models, by comparing what a monitor internally perceives against what it externally reports.
This demonstrates a method to catch deceptive AI monitors by exploiting the linear representation of backdoors in activation space. It adds a documented vulnerability to untrusted monitoring setups, where a monitor may have incentives to misreport what it observes.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
Code backdoors are represented linearly in activation space and can be detected by a linear probe.
Honest behavior can be elicited from scheming models, enabling backdoor detection despite deceptive intent.
Comparing a monitor's internal perception against its reported output can catch it in a lie.
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