Key-Recovery Attack on 5-Round AES with Multiple-of-8 Property
Bibliographic record
Abstract
At EUROCRYPT 2017, Grassi et al. proposed the multiple-of-8 property for 5-round AES, which states that the number of pairs in a certain input-output subspace, referred to as right pairs, is always a multiple of 8. However, no key-recovery attack has been proposed that utilizes this property until now. In this paper, we identify a new aspect of the multiple-of-8 property: when the number of right pairs is exactly eight, these eight pairs all have the same difference from after the 1st round SubBytes to before the 4th round SubBytes. Based on this observation, we propose a new key-recovery attack on 5-round AES. Our attack requires data and time complexities of 232.6 chosen plaintexts and 5-round AES encryptions, and a memory complexity of 231 128-bit blocks to recover a 32-bit subkey with a success probability of 50.5%. Although it is not the best attack on 5-round AES, it is notable as the first key-recovery attack that utilizes the multiple-of-8 property. We validate our observation through experiments and demonstrate its applicability to other ciphers with SPN structures, beyond AES.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".