COBRA: Catastrophic Bit-flip Reliability Analysis of State-Space Models

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.

💡 Research Summary

The paper “COBRA: Catastrophic Bit‑flip Reliability Analysis of State‑Space Models” investigates the vulnerability of recent state‑space models (SSMs), exemplified by the Mamba architecture, to hardware‑induced bit‑flip attacks (BFAs). While SSMs have gained attention for their linear‑time scalability and strong performance on long‑context tasks, the authors argue that their increasing deployment demands a rigorous assessment of both software and hardware threats. They introduce COBRA, the first BFA framework specifically tailored to Mamba‑style models.

COBRA begins with a theoretical sensitivity analysis that ranks model parameters by a hybrid metric combining absolute weight magnitude and gradient magnitude. Using this metric, the authors perform a layer‑wise ranking: for each layer they select the top‑k most sensitive bits, flip them, and measure the resulting increase in cross‑entropy loss (BFlipLoss). The layers that cause the largest loss increase are deemed most vulnerable. To reduce the attack’s computational cost, they formulate a combinatorial optimization problem that seeks the smallest subset of bits preserving the original loss increase, relax it to a continuous problem, and solve it with a randomized exclusion heuristic.

Experiments are conducted on the 1.4 billion‑parameter Mamba model using the LAMBADA cloze‑style word‑prediction benchmark and WikiText‑2 perplexity evaluation. Strikingly, flipping a single critical bit—representing only 7.14 × 10⁻¹⁰ % of all bits—drops LAMBADA accuracy from 74.64 % to 0 % and inflates perplexity from 18.94 to 3.75 × 10⁶. The attack succeeds under both white‑box (full parameter access) and gray‑box (partial knowledge) threat models, highlighting the practical risk posed by real‑world hardware faults such as RowHammer or laser fault injection.

The authors conclude that SSMs, despite their algorithmic advantages, are intrinsically fragile to minimal parameter perturbations. They release the COBRA codebase publicly, encouraging further research on robust training, error‑correcting memory schemes, and defensive architectures. Limitations include focus on a single model size and lack of evaluation on quantized or distributed deployments, which the paper identifies as promising directions for future work.