Uncovering Tor users: where anonymity ends in the Darknet

Uncovering Tor users: where anonymity ends in the Darknet

Uncovering Tor users: where anonymity ends in the Darknet


Unlike conventional World Wide Web technologies, the Tor Darknet onion routing technologies give users a real chance to remain anonymous. Many users have jumped at this chance – some did so to protect themselves or out of curiosity, while others developed a false sense of impunity, and saw an opportunity to do clandestine business anonymously: selling banned goods, distributing illegal content, etc. However, further developments, such as the detention of the maker of the Silk Road site, have conclusively demonstrated that these businesses were less anonymous than most assumed.



The research was prepared in cooperation with Maria Garnaeva specifically for Securelist


Intelligence services have not disclosed any technical details of how they detained cybercriminals who created Tor sites to distribute illegal goods; in particular, they are not giving any clues how they identify cybercriminals who act anonymously. This may mean that the implementation of the Tor Darknet contains some vulnerabilities and/or configuration defects that make it possible to unmask any Tor user. In this research, we will present practical examples to demonstrate how Tor users may lose their anonymity and will draw conclusions from those examples.


How are Tor users pinned down?


The history of the Tor Darknet has seen many attempts – theoretical and practical – to identify anonymous users. All of them can be conditionally divided into two groups: attacks on the client’s side (the browser), and attacks on the connection.


Problems in Web Browsers


The leaked NSA documents tell us that intelligence services have no qualms about using exploits to Firefox, which was the basis for Tor Browser. However, as the NSA reports in its presentation, using vulnerability exploitation tools does not allow permanent surveillance over Darknet users. Exploits have a very short life cycle, so at a specific moment of time there are different versions of the browser, some containing a specific vulnerability and other not. This enables surveillance over only a very narrow spectrum of users.


Uncovering Tor users: where anonymity ends in the Darknet

The leaked NSA documents, including a review of how Tor users can be de-anonymized (Source: www.theguardian.com )


As well as these pseudo-official documents, the Tor community is also aware of other more interesting and ingenuous attacks on the client side. For instance, researchers from the Massachusetts Institute of Technology established that Flash creates a dedicated communication channel to the cybercriminal’s special server, which captures the client’s real IP address, and totally discredits the victim. However, Tor Browser’s developers reacted promptly to this problem by excluding Flash content handlers from their product.


Uncovering Tor users: where anonymity ends in the Darknet

Flash as a way to find out the victim’s real IP address (Source: http://web.mit.edu )


Another more recent method of compromising a web browser is implemented using the WebRTC DLL. This DLL is designed to arrange a video stream transmission channel supporting HTML5, and, similarly to the Flash channel described above, it used to enable the victim’s real IP address to be established. WebRTC’s so-called STUN requests are sent in plain text, thus bypassing Tor and all the ensuing consequences. However, this “shortcoming” was also promptly rectified by Tor Browser developers, so now the browser blocks WebRTC by default.


Attacks on the communication channel


Unlike browser attacks, attacks on the channel between the Tor client and a server located within or outside of the Darknet seem unconvincing. So far most of the concepts were presented by researchers in laboratory conditions and no ‘in-the-field’ proofs of concept have been yet presented.


Among these theoretical works, one fundamental text deserve a special mention – it is based on analyzing traffic employing the NetFlow protocol. The authors of the research believe that the attacker side is capable of analyzing NetFlow records on routers that are direct Tor nodes or are located near them. A NetFlow record contains the following information:



  • Protocol version number;

  • Record number;

  • Inbound and outgoing network interface;

  • Time of stream head and stream end;

  • Number of bytes and packets in the stream;

  • Address of source and destination;

  • Port of source and destination;

  • IP protocol number;

  • The value of Type of Service;

  • All flags observed during TCP connections;

  • Gatway address;

  • Masks of source and destination subnets.


In practical terms all of these identify the client.


Uncovering Tor users: where anonymity ends in the Darknet

De-anonymizing a Tor client based on traffic analysis

(Source: https://mice.cs.columbia.edu )


This kind of traffic analysis-based investigation requires a huge number of points of presence within Tor, if the attacker wants to be able to de-anonymize any user at any period of time. For this reason, these studies are of no practical interest to individual researchers unless they have a huge pool of computing resources. Also for this reason, we will take a different tack, and consider more practical methods of analyzing a Tor user’s activity.


Passive monitoring system


Every resident of the network can share his/her computing resources to arrange a Node server. A Node server is a nodal element in the Tor network that plays the role of an intermediary in a network client’s information traffic. In this Darknet, there several types of nodes: relay nodes and exit nodes. An exit node is an end link in traffic decryption operation, so they are an end point which may become the source of leaking interesting information.


Our task is very specific: we need to collect existing and, most importantly, relevant onion resources. We cannot solely rely on internal search engines and/or website catalogs, as these leave much to be required in terms of the relevance and completeness of contained information.


However, there is a straightforward solution to the problem of aggregation of relevant websites. To make a list of the onion resources that were recently visited by a Darknet user, one needs to track each instance of accessing them. As we know, an exit node is the end point of the path that encrypted packets follow within the Darknet, so we can freely intercept HTTP/HTTPS protocol packets at the moment when they are decrypted at the exit node. In other words, if the user uses the Darknet as an intermediary between his/her browser and a web resource located in the regular Internet, then Tor’s exit node is the location where packets travel in unencrypted format and can be intercepted.


We know that an HTTP packet may contain information about web resources, including onion resources that were visited earlier. This data is contained in the ‘Referrer’ request header, which may contain the URL address of the source of the request. In the regular Internet, this information helps web masters determine which search engine requests or sites direct users towards the web resource they manage.


In our case, it is enough to scan the dump of intercepted traffic with a regular expression containing the string ‘onion’.


There is a multitude of articles available on configuring exit nodes, so we will not spend time on how to configure an exit node, but instead point out a few details.


First of all, it is necessary to set an Exit Policy that allows traffic communication across all ports; this should be done in the configuration file torrc, located in the Tor installation catalog. This configuration is not a silver bullet but it does offer a chance of seeing something interesting at a non-trivial port.


>> ExitPolicy accept *:*


The field ‘Nickname’ in the torrc file does not have any special meaning to it, so the only recommendation in this case is not to use any conspicuous (e.g. ‘WeAreCapturingYourTraffic’) node names or those containing numbers (‘NodeNumber3

Alt text

Ваш провайдер знает о вас больше, чем ваша девушка?

Присоединяйтесь и узнайте, как это остановить!

Денис Макрушин

Inspired by Insecure