Evolutionary Search for Authorization Vulnerabilities in Web Applications
Bibliographic record
Abstract
Access controls are essential to protect private resources in web applications. However, authorization vulnerabilities resulting from improper access controls are common. Existing detection techniques require manual effort and suffer from false positives as automating authorization vulnerability detection in an app agnostic way is challenging. We present two subproblems- automated discovery of app resources and automated detection. This thesis introduces AuthZee, a tool that automatically generates objects and discovers resources in a web app and automatically detects if those resources are vulnerable to improper authorization, without requiring details about the app code/logic. We present a novel evolutionary crawler and triad testing technique requiring login credentials of three user accounts in the app which allows AuthZee to crawl user account space and perform automated authorization vulnerability detection. AuthZee discovered more resources than existing crawling techniques in 7 open-source web apps and detected 1 vulnerability with 0 false positives for most apps.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".