BitFlipScope: Scalable Fault Localization and Recovery for Bit-Flip Corruptions in LLMs

TL;DR

BitFlipScope tackles the challenge of bit-flip faults in large language models by delivering a dual-path fault-localization framework: Differential fault localization uses a clean reference to pinpoint faults, while Self-Referential fault localization relies on residual-scaling sensitivity to detect abnormalities without a reference. The methodology provides a hierarchical localization pipeline (block, layer, weight/bit) and lightweight recovery that avoids retraining; in the differential setting, targeted parameter replacement fully restores accuracy, and in the self-referential setting, inference-time attenuation recovers a majority of the degraded performance. Experiments on LLaMA 3.x models with 8-bit quantization demonstrate accurate localization across blocks and layers, substantial robustness across model sizes, and practical recovery achieving over 80% restoration without fine-tuning. Collectively, BitFlipScope advances trustworthy, fault-resilient LLM deployment by enabling scalable, software-only localization and mitigation in hardware-prone or adversarial environments.

Abstract

Large Language Models (LLMs) deployed in practical and safety-critical settings are increasingly susceptible to bit-flip faults caused by hardware degradation, cosmic radiation, or deliberate fault-injection attacks such as Rowhammer. These faults silently corrupt internal parameters and can lead to unpredictable or dangerous model behavior. Localizing these corruptions is essential: without identifying the affected region, it is impossible to diagnose the source of degradation, apply targeted corrective measures, or restore model functionality without resorting to costly fine-tuning or full retraining. This work introduces BitFlipScope, a scalable, software-based framework for identifying fault-affected regions within transformer architectures under two deployment scenarios. When a clean reference model is available, BitFlipScope performs differential analysis of outputs, hidden states, and internal activations for detecting anomalous behavior indicative of corruption to pinpoint or localize faults. When no reference model exists, it uses residual-path perturbation and loss-sensitivity profiling to infer the fault-impacted region directly from the corrupted model. In both settings, the framework not only enables effective fault diagnosis but also supports lightweight performance recovery without fine-tuning, offering a practical path to restoring corrupted models. Together, these capabilities make BitFlipScope an important step toward trustworthy, fault-resilient LLM deployment in hardware-prone and adversarial environments.

Figures (8)

• Figure 1: Overview of the BitFlipScope framework for detecting and mitigating bit-flip faults in LLMs. (a) A single bit-flip arising from hardware faults or attack corrupts a transformer block and degrades the model’s output. (b) Fault localization is performed using two approaches: self-referential analysis (left), which identifies abnormal loss sensitivity under residual scaling, and differential analysis (right), which detects hidden-state divergence using a clean reference model. (c) Once the faulty block is identified, lightweight recovery mechanisms reduce or correct its influence, enabling performance restoration without fine-tuning.
• Figure 2: Loss change $\Delta\mathrm{Loss}$ across a broad range of scaling values $\alpha \in [0.2,1.8]$ for a representative block. Diagnostic sensitivity is highest in the interval $[0.6,1.4]$, which motivates the selection of scaling values used in subsequent experiments.
• Figure 3: Hidden-state comparison between clean and bit-flipped models for block-level localization.
• Figure 4: Cosine similarity of attention vs. MLP layer activations within the faulty block. The corrupted layer mlp in this case exhibits a drop in similarity.
• Figure 5: Heatmaps of $\Delta\mathrm{Loss}$ for LLaMA 3.2 3B across the four injected faults. The corrupted block in each case shows a pronounced asymmetric loss pattern under scaling.