A verified algorithm for detecting conflicts in XACML access control rules
Bibliographic record
Abstract
We describe the formalization of a correctness proof for a conflict detection algorithm for XACML (eXtensible Access Control Markup Language). XACML is a standardized declarative access control policy language that is increasingly used in industry. In practice it is common for rule sets to grow large, and contain unintended errors, often due to conflicting rules. A conflict occurs in a policy when one rule permits a request and another denies that same request. Such errors can lead to serious risks involving both allowing access to an unauthorized user as well as denying access to someone who needs it. Removing conflicts is thus an important aspect of debugging policies, and the use of a verified algorithm provides the highest assurance in a domain where security is important. In this paper, we focus on several complex XACML constructs, including time ranges and integer intervals, as well as ways to combine any number of functions using the boolean operators and, or, and not. The latter are the most complex, and add significant expressive power to the language. We propose an algorithm to find conflicts and then use the Coq Proof Assistant to prove the algorithm correct. We develop a library of tactics to help automate the proof.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".