LLWRA: Large Language Models Weight Replacement Attack

Abstract

The enormous size of large language models (LLMs) makes storing the entire model in on-chip memory implausible. The LLM application requires an external off-chip memory to store model weights, exposing them to memory fault injection attacks. In this work, we introduce a novel approach to compromise the performance of LLMs by exploiting a novel fault injection mechanism that introduces targeted bit-flips in page frame numbers of main memory. In the context of main memory, each weight block consists of a set of weights stored at a specific address. As a result, a single bit-flip in the page frame number can replace a target weight block with a new replacement weight block, disrupting the memory translation. However, the algorithmic challenge of creating a formal attack lies in the fact that random weight replacement faults fail to produce detrimental effects on model performance. In this work, we propose LLWRA which effectively utilizes weight replacement fault injection to degrade the intelligence of state-of-the-art LLMs for the first time. Additionally, we present the ReBlock search algorithm, which efficiently identifies a set of vulnerable target and replacement weight blocks. We evaluate our approach, LLWRA, across three distinct attack objectives: untargeted classification, targeted classification, and untargeted causal modeling. Experimental results demonstrate that LLWRA requires fewer than five attack optimization rounds to reduce classification accuracy to a random guess level and fewer than nine iterations to reduce the causal model into a random generator, making our attack the most lethal weight manipulation attack against LLMs.

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