A new weight quantization framework, Seed-Q, is introduced to compress large language model weights while considering their sensitivity. This approach uses Linear Feedback Shift Register (LFSR)-based weight generation with non-uniform bit allocation, eliminating the need for side-information and metadata. Experiments show that Seed-Q achieves better compression efficiency and security against bit-flip attacks compared to existing methods.