Current state of client-side extensions aimed at protecting against CSRF-like attacks
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
For over a decade now, cross-site request forgery (CSRF) has been persistently named one of the OWASP's top 10 Web vulnerabilities. Recently, a variant of CSRF - named cross-site framing attack (CSFA) - has also been identified. Both attacks are very simple to implement/execute while resulting in potentially devastating consequences for the victim. What distinguishes the two attacks is their ultimate objective. CSRF generally aims to simulate the user/victim action on an authenticated site, thereby causing damage to the victim's security and/or privacy. CSFA, on the other hand, could target both authenticated and non-authenticated Web sites, and generally aims to harm the victim's reputation. To date, a number of client- and server-side mechanisms of protection against CSRF and CSFA have been proposed. Unfortunately, the implementation of these mechanisms is neither regulated nor mandated by the Web industry. Hence, often times, the user's best bet against CSRF and CSFA is general vigilance and/or the use of protective client-side extensions. The aim of our work was to survey the current state of Chrome-based extensions that claim to protect against CSRF (and CSFA). The results of our study have shown that, out of the five identified extensions that fall into this category, none of the extensions are effective in blocking all examined variants of CSRF and CSFA. The extensions examined do not only fail to provide comprehensive protection against CSRF and CSFA, but also exhibit a number of other deficiencies, and therefore cannot be recommended as effective anti-SRF and CSFA tools.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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