RAMBO: Reliability Analysis for Mamba through Bit-flip attack Optimization

2025-12-14

AI Summary

This work systematically evaluates the vulnerability of Mamba-style state space models (SSMs) to hardware-level bit-flip attacks (BFAs). To address this, we propose the first BFA-optimized framework tailored for Mamba architectures, integrating memory fault modeling with gradient-based parameter importance analysis. We conduct targeted single-bit perturbation experiments on the LAMBADA cloze task. Results demonstrate that flipping just one weight bit in the Mamba-1.4b model reduces accuracy from 74.64% to 0% and increases perplexity from 18.94 to 3.75×10⁶—a surge exceeding six orders of magnitude. This study is the first to expose severe reliability vulnerabilities of SSMs under hardware faults, establishing a critical security benchmark for trustworthy AI systems and opening new avenues for hardware-aware robustness mitigation.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

State-space models (SSMs), exemplified by the Mamba architecture, have recently emerged as state-of-the-art sequence-modeling frameworks, offering linear-time scalability together with strong performance in long-context settings. Owing to their unique combination of efficiency, scalability, and expressive capacity, SSMs have become compelling alternatives to transformer-based models, which suffer from the quadratic computational and memory costs of attention mechanisms. As SSMs are increasingly deployed in real-world applications, it is critical to assess their susceptibility to both software- and hardware-level threats to ensure secure and reliable operation. Among such threats, hardware-induced bit-flip attacks (BFAs) pose a particularly severe risk by corrupting model parameters through memory faults, thereby undermining model accuracy and functional integrity. To investigate this vulnerability, we introduce RAMBO, the first BFA framework specifically designed to target Mamba-based architectures. Through experiments on the Mamba-1.4b model with LAMBADA benchmark, a cloze-style word-prediction task, we demonstrate that flipping merely a single critical bit can catastrophically reduce accuracy from 74.64% to 0% and increase perplexity from 18.94 to 3.75 x 10^6. These results demonstrate the pronounced fragility of SSMs to adversarial perturbations.

Problem

• Assess SSM susceptibility to hardware bit-flip attacks
• Introduce RAMBO framework targeting Mamba architecture vulnerabilities
• Demonstrate catastrophic accuracy loss from single critical bit flip

Innovation

• RAMBO framework targets Mamba with bit-flip attacks
• Flipping single critical bit catastrophically reduces model accuracy
• Assesses SSM vulnerability to hardware-induced memory faults