Removing Information Content Does Not Certify Tamper Resistance in Open-Weight Models

Abstract:Does removing harmful information make open-weight models resistant to fine-tuning attacks? We show that mutual information at release alone cannot universally certify slow recovery. Function-preserving reparameterizations leave information unchanged while altering gradient-descent geometry, so an invariant certificate is bounded by the fastest reachable parameterization. We apply this principle to weight--data mutual information under training-data filtering and label--representation mutual information under capability removal. Training order can change recovery time at fixed weight--data information, while exact representation-level independence can preserve the entire parameter Jacobian. An explicit construction has both information quantities equal to zero and recovers in one gradient step. Controlled experiments illustrate order-dependent recovery and parameterization-dependent attack speed. These results identify the missing requirement for certification: constraints on attack dynamics beyond mutual information at release.

Machine Learning (cs.LG); Cryptography and Security (cs.CR)

arXiv:2610.09004 [cs.LG]

(or arXiv:2610.09004v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.09004 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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