Researchers propose a proxy unlearning attack: exploiting LLM knowledge superposition to remove sensitive entities

1. 00:11
   RedditReddit r/AIsafety· Discussion

   Machine unlearning is meant for privacy, could it also remove an AI's safety guardrails?(opens the original post in a new tab)

1. 12:10
   arXiv: Adversarial Attacks, Backdoors & Poisoning

   Sensitive knowledge protection in LLM unlearning: agent threats and layered-aware defenses

   The researchers propose the Proxy Unlearning Attack (PUA): exploiting the superposition phenomenon in LLM machine unlearning to indirectly remove sensitive entities that were never requested, through legitimate unlearning requests. In LLMs, representations of different entities may overlap in shared parameter subspaces, so deleting one entity may affect other entities; based on this, the attacker selects a removable proxy entity that is superposed with the sensitive target and issues a request, and benign users may also unintentionally trigger such removal, which the authors say differs from data poisoning or backdoor attacks. In response, the authors propose a general defense framework, SAUP (Superposition-Aware Unlearning Protection), that can be applied to unlearning requests to ensure correct removal, working in two stages: before unlearning, superposition detection estimates which sensitive entities the request would endanger; after unlearning, shared representations are disentangled and affected entities are restored, while the requested entity remains suppressed. The authors state that LLM providers, when handling unlearning requests, need representation-level detection and restoration mechanisms to mitigate this threat and protect sensitive knowledge.

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