# LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models

arXiv: https://arxiv.org/abs/2511.02866 · HTML: https://mlsanity.com/paper/2511.02866/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search](https://mlsanity.com/paper/1903.12269/) (2019)
- [EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models](https://mlsanity.com/paper/2403.12171/) (2024)
- [FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation](https://mlsanity.com/paper/1912.00941/) (2019)
- [GPUHammer: Rowhammer Attacks on GPU Memories are Practical](https://mlsanity.com/paper/2507.08166/) (2025)
- [On Jailbreaking Quantized Language Models Through Fault Injection Attacks](https://mlsanity.com/paper/2507.03236/) (2025)
- [BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks](https://mlsanity.com/paper/2402.00906/) (2024)
- [Revisiting RowHammer: An Experimental Analysis of Modern DRAM Devices and Mitigation Techniques](https://mlsanity.com/paper/2005.13121/) (2020)
- [Retrospective: Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors](https://mlsanity.com/paper/2306.16093/) (2023)
- [Bit-Flip Fault Attack: Crushing Graph Neural Networks via Gradual Bit Search](https://mlsanity.com/paper/2507.05531/) (2025)

## Built on by (most cited first, 3 of 3)

- [Same Game, Different Story: A Minimal Conservative Strategic Robustness Benchmark for Large Language Model Agents](https://mlsanity.com/paper/2607.19670/) (2026)
- [BitFlipScope: Scalable Fault Localization and Recovery for Bit-Flip Corruptions in LLMs](https://mlsanity.com/paper/2512.22174/) (2025)
- [Faults That Fortify: CNN Adversarial Robustness via GPU Undervolting](https://mlsanity.com/paper/2608.20572/) (2026)

## Abstract (arXiv)

This paper presents LM-Fix, a lightweight detection and rapid recovery framework for faults in large language models (LLMs). Existing integrity approaches are often heavy or slow for modern LLMs. LM-Fix runs a short test-vector pass and uses hash-guided checks to detect bit-flip faults, then repairs them locally without a full reload. Across multiple models, it detects over 94% of single-bit flips at TVL=200 and nearly 100% of multi-bit flips with approximately 1% to 7.7% runtime overhead; recovery is more than 100x faster than reloading. These results show a practical, low-overhead solution to keep LLMs reliable in production