LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models
Bibliographic record
Abstract
Bit-flip attacks threaten the reliability and security of Language Models (LMs) by altering internal parameters and compromising output integrity. Recent studies show that flipping only a few bits in model parameters can bypass safety mechanisms and jailbreak the model. Existing detection approaches for DNNs and CNNs are not suitable for LMs, as the massive number of parameters significantly increases timing and memory overhead for software-based methods and chip area overhead for hardware-based methods. In this work, we present LM-Fix, a lightweight LM-driven detection and recovery framework that leverages the model's own capabilities to identify and recover faults. Our method detects bit-flips by generating a single output token from a predefined test vector and auditing the output tensor of a target layer against stored reference data. The same mechanism enables rapid recovery without reloading the entire model. Experiments across various models show that LM-Fix detects more than 94% of single-bit flips and nearly 100% of multi-bit flips, with very low computational overhead$(\approx 1 \%- 7.7 {\%}$at TVL$=200$across models). Recovery achieves more than$100 \times$speedup compared to full-model reload, which is critical in edge devices. LM-Fix can handle bit-flips affecting any part of the model's computation, including memory, cache, and arithmetic operations. Evaluation against recent LM-specific bit-flip attacks confirms its robustness and practical value for real-world deployment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".