Fast Detection of Access Control Vulnerabilities in PHP Applications
Bibliographic record
Abstract
Access control vulnerabilities in web applications are on the rise. In its 2010 "Top 10 Most Critical Web Applications Security Risks", the OWASP reported that the prevalence of access control vulnerabilities in web applications increased compared to 2007. However, in contrast to SQL injection and cross-site scripting flaws, access control vulnerabilities comparatively received much less attention from the research community. This paper presents ACMA (Access Control Model Analyzer), a model checking-based tool for the detection of access control vulnerabilities in PHP applications. The core of ACMA uses a lightweight model checker to detect the privileges that are enforced at each statement of an application. Based on this information, ACMA can detect several types of access control vulnerabilities: from forced browsing vulnerabilities to faulty access controls. We show how, when compared to the state of the art, ACMA achieves advantageously comparable results with accelerations up to 890 times faster. Moreover, contrary to the state of the art, ACMA scales up to medium-large applications with large access control models, as shown by the analysis of Moodle, a 400,000+ LOC application counting more than 200 distinct privileges. Results show that ACMA is fast, precise and scalable making it a practical tool for the detection of access control vulnerabilities in real-world applications. A discussion about further extensions to ACMA is also presented.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".