Online Shift Detection and Conformal Adaptation for Deployed Safety Classifiers
Reasoning models deployed as safety monitors exhibit a systematic vulnerability: reasoning-token budget starvation. Adversarial inputs require $3.3\times$ more reasoning tokens than benign inputs to produce valid safety scores ($T_{50,\text{adv}}{=}154$ vs. $T_{50,\text{benign}}{=}46$ for o3), so low-budget deployments silently starve the monitor on exactly the inputs it must catch. This compounds the central failure mode: gradient-based evasion remains the residual threat - template jailbreaks fail at 99%, but GCG-optimized suffixes flip encoder decisions reliably. We systematize a canary construction - score-disagreement monitoring between a targeted and un-targeted classifier - and qua...
| Authors | Jun Wen Leong |
|---|---|
| Primary category | cs.LG |
| Published | 10 June 2026 |
| Last updated at source | 10 August 2026 |
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