Research seriesL3data science
Feature dictionaries, sparse autoencoders, and activation probes are making model internals partially legible at runtime — and that raises a defensive question: can reading the internals detect deception, jailbreak states, or unsafe behavior before it reaches the output? This paper treats interpretability as a monitoring control rather than a research curiosity, and asks the harder question every detector must answer: what are its false-negative limits under an adaptive adversary? The organizing contribution is a Probe Assurance Ledger that separates 'evidence' from 'guarantee' for each internal-signal detector. Product-agnostic, grounded in the primary probing and feature-learning literature, and tied back to the AI-agent stack every time.
Model internals are becoming partially legible at runtime, and this piece asks the security question: what does that legibility actually buy a defender — and what does it not?
A probe can read a security-relevant state from a model's activations — but turning that read into a trustworthy detector is a data-science problem, not a demo.
A model's internals may reveal when it is lying — but the moment the model can optimize against that signal, the detector's guarantee starts to dissolve.
Reading a model's internals at inference time is a real signal — and a real bill; this piece treats internal monitoring as a systems problem with a cost-coverage frontier.
Interpretability's value as a security control is decided by its limits, so the discipline is to turn those limits into a doctrine: use it to raise the alarm, never to clear the model.