Hello tor-dev! My name is Kevin and I'm a PhD student at NYU. Recently I've been working on creating a "Tor Friendliness Scanner" (TFS), or a scanner that will measure what features of a given website are broken (non-functional) when accessed on the Tor Browser (TB), along with actionable suggestions to improve it. In order to do this, we first must get an approximation of ground-truth data of how a given website should work. We then need to compare it to how the website works on the TB to determine any changes. To generate a method of determining ground-truth, we decided to modify* the Firefox (FF) browser to log all of the steps of the creation of the Content Tree (also called the DOM tree), and to log the execution of all _javascript_ functions (currently underway). We then will apply these changes to the TB as well, and run a scan of popular Web sites using the modified FF and the modified TB on all three of the TB security slider settings. We will then compare the resulting logs to determine where the tree creation processes differed* and why. These differences could potentially help us illuminate two things:
As far as I have considered, this method seems to capture a lot,
but it's far from complete. For one thing, it obviously won't
detect any difference that's spawned from user interaction or
input (such as a script launched by an OnClick event). However, it
does seem to make automation of scanning for Tor Friendliness
possible, and can allow for wide-scale use. We have moved ahead with development (though have not yet
finished it) and are (hopefully) very close to a working
prototype. I was wondering if there was feedback on this method,
or if anyone can consider an angle we have not that would either
make the TFS more robust, easier to create, or both. Thanks for your time and consideration! Kevin *Note 1: Unfortunately we cannot just rely on _javascript_ for
examining the content tree, since this needs to work on all 3
security settings of the TB's security slider, and the "safest"
setting deactivates _javascript_ by default on all Web pages. -- Kevin Gallagher Ph.D. Candidate Center For Cybersecurity NYU Tandon School of Engineering Key Fingerprint: D02B 25CB 0F7D E276 06C3 BF08 53E4 C50F 8247 4861 |
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