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New Groundhog Bit-Flip Attack exploits MoE LLMs, causing massive output inflation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new attack called the Groundhog Bit-Flip Attack (GBFA) that targets Mixture-of-Experts (MoE) large language models. This attack exploits the routing mechanism in MoE architectures, where specific experts can become correlated with certain tokens. By manipulating routing-layer bits, GBFA can cause significant inflation in the model's output length, extending decoding token usage by an average of 5912% across conversational, reasoning, and agentic tasks. The attack requires deactivating only a small number of experts, highlighting a robustness vulnerability in MoE designs. AI

IMPACT Reveals a significant vulnerability in MoE architectures, potentially impacting the reliability and security of large language models.

RANK_REASON Research paper detailing a new attack method against LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Groundhog Bit-Flip Attack exploits MoE LLMs, causing massive output inflation

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Research paper detailing a new attack method against LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Huakang Lin, Tiancheng Zheng, Mingxuan Sun, Tianhong Xu, Fan Zhang, Yunsi Fei, Ruyi Ding ·

    Groundhog Bit-Flip Attack: Seeding Infinite Generation Loops in Mixture-of-Experts LLMs through Bit Flips

    arXiv:2608.25276v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable scalable and efficient large language models (LLMs) by selectively activating expert sub-networks through a routing mechanism. However, this adaptive design introduces a new attack surfa…

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