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Record W4414079220 · doi:10.1587/transfun.2025eap1069

Key-Recovery Attack on 5-Round AES with Multiple-of-8 Property

2025· article· en· W4414079220 on OpenAlexaff
Hanbeom Shin, Sunyeop Kim, Byoungjin Seok, Dongjae Lee, Deukjo Hong, Jaechul Sung, Seokhie Hong

Bibliographic record

VenueIEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2025
Typearticle
Languageen
FieldComputer Science
TopicPhysical Unclonable Functions (PUFs) and Hardware Security
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsProperty (philosophy)Slide attackCorrelation attackCryptanalysisBlock cipherCryptography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.279
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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