EvoCrawl: Exploring Web Application Code and State using Evolutionary Search
Bibliographic record
Abstract
As more critical services move onto the web, it has become increasingly important to detect and address vulnerabilities in web applications.These vulnerabilities only occur under specific conditions: when 1) the vulnerable code is executed and 2) the web application is in the required state.If the application is not in the required state, then even if the vulnerable code is executed, the vulnerability may not be triggered.Previous work naively explores the application state by filling every field and triggering every JavaScript event before submitting HTML forms.However, this simplistic approach can fail to satisfy constraints between the web page elements, as well as input format constraints.To address this, we present EvoCrawl, a web crawler that uses evolutionary search to efficiently find different sequences of web interactions.EvoCrawl finds sequences that can successfully submit inputs to web applications and thus explore more code and server-side states than previous approaches.To assess the benefits of EvoCrawl, we evaluate it against three stateof-the-art vulnerability scanners on ten web applications.We find that EvoCrawl achieves better code coverage due to its ability to execute code that can only be executed when the application is in a particular state.On average, EvoCrawl achieves a 59% increase in code coverage and successfully submits HTML forms 5× more frequently than the next best tool.By integrating IDOR and XSS vulnerability scanners, we used EvoCrawl to find eight zero-day IDOR and XSS vulnerabilities in WordPress, HotCRP, Kanboard, ImpressCMS, and GitLab.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".