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Record W1491103366

Fixing races for fun and profit: how to use access(2)

2004· article· en· W1491103366 on OpenAlexaff
Drew Dean, Alan J. Hu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsExecutorComputer scienceProbabilistic logicComputer securityArtificial intelligenceEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

It is well known that it is insecure to use the access(2) system call in a setuid program to test for the ability of the program’s executor to access a file before opening said file. Although the access(2) call appears to have been designed exactly for this use, such use is vulnerable to a race condition. This race condition is a classic example of a time-of-check-to-time-of-use (TOCTTOU) problem. We prove the “folk theorem ” that no portable, deterministic solution exists without changes to the system call interface, we present a probabilistic solution, and we examine the effect of increasing CPU speeds on the exploitability of the attack.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0030.016
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.086
GPT teacher head0.320
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations37
Published2004
Admission routes1
Has abstractyes

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