The Tokens Remember: When Tokenization Bypasses Knowledge Editing and Unlearning
Open-weight LLMs give downstream users control over the inference stack, but this flexibility can undermine post-release guarantees that sensitive knowledge has been modified or removed. Model editing and machine unlearning are used to modify or remove targeted knowledge without retraining models from scratch. However, existing security evaluations of these techniques face two critical limitations. First, they typically require access to either the original pre-edit/unlearning model or auxiliary classifiers to detect modifications or reconstruct pre-edit behavior. Second, they evaluate modifications under the canonical tokenization of an input, implicitly treating tokenization as a benign...
| Authors | Manit Baser, Aditya Nawal, Dinil Mon Divakaran et al. |
|---|---|
| Primary category | cs.CR |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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