The poor usability of OpenLDAP Access Control Lists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it