Author

Min-Seon Kim

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#artificial intelligence Preprint Sep 2026

Backdoor Containment via Expert Quarantine and Shutdown in LLMs

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...

Jian-Wei Li, Min-Seon Kim, Jung-Eun Kim · 0 citations

#machine learning Jan 2026

Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning

This work proposes PromptMIA, a membership inference attack tailored to federated prompt-tuning, in which a malicious server introduces adversarially crafted prompts and exploits their updates during collaborative training to determine whether a target data point belongs to a client's private dataset.

Quan Nguyen, Min-Seon Kim, Hoang M. Ngo et al. · arXiv.org · 1 citation