Bounded Reachability & Jailbreak Detection via Contraction-Constrained State Space Models
Safety heads are lightweight classifiers attached to pretrained language models for flagging harmful inputs before generation. Their empirical detection performance has been studied, but their formal robustness properties remain largely unexplored. We ask when a State Space Model (SSM)-based safety head can be certified to produce the same prediction for all inputs within a bounded embedding-space perturbation. We prove that the answer turns on a single condition: the $l_\infty$ norm of the state transition matrix must satisfy $\norm{A}_\infty<1$ (the \emph{contraction condition}), which enables exact interval bound propagation (IBP) certification for linear time-invariant classifiers. Wh...
| Authors | Omanshu Thapliyal |
|---|---|
| Primary category | cs.AI |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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