Practical elimination of external interaction vulnerabilities in web applications
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
External Interaction Vulnerabilities (EIVs) are currently the most common vulnerability for web applications. These vulnerabilities allow attackers to use vulnerable web applications as a vessel to transmit malicious code to external systems that interact with the web applications. The malicious code will modify the semantic content of the information sent to the external application. Current vulnerability detection approaches are black-box oriented and do not take advantage of the data flow information which is available in the source code. In this paper, we introduce a white-box approach called EIV analysis to eliminate web applications' vulnerabilities. This strategy allows investigators to accurately identify all inputs entering the web application and model the input as it reaches external systems acting as data sinks. The strategy is partially automated resulting in substantial effort savings when compared with common industrial approaches; while also providing superior performance in terms vulnerability detection. A case study using a commercial, currently deployed, mission-critical web application is presented to demonstrate the validity of these claims.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it