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.

翻译：在现实及安全关键场景中部署的大语言模型（LLM）日益面临由硬件退化、宇宙辐射或Rowhammer等蓄意故障注入攻击引发的比特翻转故障威胁。这些故障会静默破坏内部参数，可能导致不可预测或危险的行为。关键问题在于必须定位这些破坏区域：若无法识别受影响区域，就无法诊断退化根源、实施针对性纠正措施或在无需代价高昂的微调或完整重训练前提下恢复模型功能。本文提出BitFlipScope，这是一种基于软件的可扩展框架，用于在两种部署场景下识别Transformer架构中的故障影响区域。当存在干净参考模型时，BitFlipScope通过对输出、隐藏状态及内部激活值的差异分析，检测表明参数破坏的异常行为以精确定位故障；当无参考模型时，它利用残差路径扰动与损失敏感性分析，直接从受损模型中推断故障影响区域。在这两种场景下，该框架不仅能实现高效故障诊断，还支持无需微调的轻量级性能恢复，为修复受损模型提供了实用路径。这些能力使BitFlipScope成为在硬件脆弱及对抗环境中实现可信、抗故障LLM部署的重要里程碑。