Current state of client-side extensions aimed at protecting against CSRF-like attacks
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.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".