Work

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

Year: 2026

Type: conference-paper

Abstract: Mixture-of-Experts (MoE) architectures enable scalable and efficient large language models (LLMs) by selectively activating expert subnetworks through a routing mechanism.However, this adaptive design introduces a new attack surface: specific experts become disproportionately correlated with certain tokens (e.g., end-of-sequence), allowing adversaries to manipulate model behavior via lightweight perturbations.In this work, we present Groundhog Bit-Flip Attack (GBFA), the first bitflip-based Denial-of-Wallet availability attack against MoE-based LLMs.By identifying and flipping routing-layer bits associated with related expert activations, we demonstrate that GBFA substantially extends the decoding token usage across three different LLM modes: conversational, reasoning, and agentic tasks, while largely preserving semantic fidelity.Across four main real-world MoE-based LLMs, manually deactivating on average fewer than 4 experts drives average output inflation to 5912%, with the majority of test samples reaching max tokens.These results reveal a robustness vulnerability of MoE architectures to bit flip, and highlight the potential of GBFA as an availability attack against LLMs.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/2026.emnlp-main.1799