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Record W1982621550 · doi:10.1145/2523649.2523650

Uncovering access control weaknesses and flaws with security-discordant software clones

2013· article· en· W1982621550 on OpenAlexafffund
François Gauthier, Thierry Lavoie, Ettore Merlo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
Keywordsclone (Java method)Computer scienceAccess controlSoftware security assurancePerspective (graphical)Code (set theory)SoftwareStrengths and weaknessesComputer securityInformation securityProgramming languageArtificial intelligenceBiologySecurity serviceSet (abstract data type)Psychology

Abstract

fetched live from OpenAlex

Software clone detection techniques identify fragments of code that share some level of syntactic similarity. In this study, we investigate security-sensitive clone clusters: clusters of syntactically similar fragments of code that are protected by some privileges. From a security perspective, security-sensitive clone clusters can help reason about the implemented security model: given syntactically similar fragments of code, it is expected that they are protected by similar privileges. We hypothesize that clones that violate this assumption, defined as security-discordant clones, are likely to reveal weaknesses and flaws in access control models.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.591
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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 designObservational
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

Citations22
Published2013
Admission routes2
Has abstractyes

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