EM Fault Injection on ARM and RISC-V
Bibliographic record
Abstract
Recently Electro-Magnetic Fault Injection (EMFI) techniques have been found to have significant implications on the security of embedded devices. Unfortunately, there is still a lack of understanding of EM faults and countermeasures for embedded processors. For the first time, this paper empirically shows that EMFI can cause skipping/faulting of more than one instruction on a 320MHz RISC-V processor, thus making it susceptible to a wider range of attacks. Additionally, empirical results on ARM Cortex M0 and RISC-V embedded processors show that EMFI is more susceptible at lower supply voltages and higher clock frequencies. Exception codes are also shown to be useful in understanding details of injected faults, providing further evidence that instructions have been corrupted in many cases. This research aims to enhance the understanding of faults, in order to better design countermeasures for embedded processors resistant to fault injection attacks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".