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   Hijacking a Trusted LLM in Quantized Deployments via Disguised Adapters

   Research · since 13 Nov 2026

   Researchers demonstrated that third-party LoRA adapters can pass audits on unquantized base models while successfully hijacking decisions after quantization. The attack requires no modifications to the trusted base model.

   AI
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   Vision Transformers Breaking the Privacy Promise: Gradient Inversion Attacks on Federated Low-Rank Adapted Vision Transformers

   Research · since 9 Nov 2026

   This study investigates data privacy risks in federated learning environments utilizing Low-Rank Adaptation for Vision Transformers, demonstrating successful gradient inversion attacks.

   AI

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