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October 8, 2025Open Access

Keeping an Eye on LLM Unlearning: The Hidden Risk and Remedy

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Malicious users can exploit vulnerabilities in unlearning techniques, risking utility degradation for benign users.
A Stealthy Attack demonstrates how common benign tokens can trigger unintended behaviors in unlearned models.
Scope-aware unlearning aims to localize forgetting effects, enhancing model robustness against malicious requests.
Extensive experiments confirm the effectiveness of the proposed unlearning techniques and their defense.

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This research reveals vulnerabilities in unlearning techniques for large language models, suggesting scope-aware unlearning as a remedy.

Ren et al. (2025) studied this question.

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