Backdoor Containment via Expert Quarantine and Shutdown in LLMs
Researchers propose isolating and deactivating compromised expert modules in Mixture-of-Experts LLMs at inference time, offering an architectural defence against backdoor triggers without full retraining.
Summary written by editorial AI · Source link below
arXiv:2610.00663v2 Announce Type: replace-cross Abstract: Backdoored large language models (LLMs) can behave normally on benign inputs while producing attacker-specified outputs under hidden triggers. Existing defenses span four stages---prior-training, in-training, post-training, and inference-time---and share one of two underlying strategies: either suppress backdoor learning (by filtering poisoned data or interrupting its acquisition during optimization) or learn, then purify (by repairing m
Editorial Analysis
As MoE architectures become mainstream, the ability to quarantine backdoored experts at inference time could reduce the blast radius of supply-chain compromises in LLM deployments.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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