The poor usability of OpenLDAP Access Control Lists
Bibliographic record
Abstract
Abstract The usability of Access Control Lists (ACLs) of a widely used enterprise software for directory information services called OpenLDAP is addressed. A directory service is used to store a variety of data such as employee information and passwords, and can be seen as a critical infrastructure component of an enterprise. Security and in particular, access control of such data is of paramount importance, and OpenLDAP provides ACLs for this purpose that an administrator can configure. The usability, that is, the ease with which a human administrator can express a policy in an ACL, is then an important issue because misconfigurations are known to be a major cause of security vulnerabilities. Motivated by public pronouncements regarding the poor usability of OpenLDAP ACLs, a systematic study towards evaluating their usability is carried out. The authors begin with a cognitive walkthrough, which identifies the broad issues, which then informs the design of an ethics‐approved study of 50 human participants. This study reveals that indeed, even with a limited syntax, adequate training and a focus only on devising a policy from scratch, OpenLDAP ACLs suffer from poor usability. The data gathered from this study is analysed further, and more detailed observations are made such as those regarding the difference in difficulty for different kinds of policy goals, and the nature of errors human participants make with OpenLDAP ACLs. As such, this work makes an important contribution to enterprise security and provides important insights for a (re)design of ACLs, in particular for OpenLDAP.
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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 | 0.036 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".