As we grow and operate on a number of continents, so does our dependence on a rock-solid IT infrastructure. We are expanding our repertoire to include a greater collection of Linux/Open Source/Windows and OS X products. With this, we are on the look-out for a rock star to wrangle control of our internal networks, external cloud infrastructure and help us us utilise technology in a way to make us even better.
Job Title: IT Network Packet Wrangling Penguin Master
Salary Range: Industry standard, commensurate with experience
Location: Johannesburg/Pretoria, South Africa
Taking inspiration from Vlad's post I've been playing around with alternate means of viewing traffic/data generated by Android apps.
The technique that has given me most joy is memory analysis. Each application on android is run in the Dalvik VM and is allocated it's own heap space. Android being android, free and open, numerous ways of dumping the contents of the application heap exist. There's even a method for it in the android.os.Debug library: android.os.Debug.dumpHprofData(String filename). You can also cause a heap dump by issuing the kill command:
kill -10 <pid number>
1.) Open DDMS in Eclipse and attach your device/emulator
* Set your DDMS "HPROF action" option to "Open in Eclipse" - this ensures that the dump file gets converted to standard java hprof format and not the Android version of hprof. This allows you to open the hpof file in any java memory viewer.
* To convert a android hprof file to java hprof use the hprof converter found in the android-sdk/platform-tools directory: hprof-conv <infile> <outfile>
2.) Dump hprof data
Once DDMS has done it's magic you'll have a window pop up with the memory contents for your viewing pleasure. You'll immediately see that the applications UI objects and other base classes are in the first part of the file. Scrolling through you will start seeing the values of variables stored in memory. To get to the interesting stuff we can use the command-line.
3.) strings and grep the .hprof file (easy stuff)
To demonstrate the usefulness of memory analysis lets look at two finance orientated apps.
The first application is a mobile wallet application that allows customers to easily pay for services without having to carry cash around. Typically one would do some static analysis of the application and then when it comes to dynamic analysis you would use a proxy such as Mallory or Burp to view the network traffic. In this case it wasn't possible to do this as the application employed certificate pinning and any attempt to man in the middle the connection caused the application to exit with a "no network connection" error.
So what does memory analysis have to do with network traffic? As it turns out, a lot. Below is a sample of the data extracted from memory:
And there we have it, the user login captured along with the username and password in the clear. Through some creative strings and grep we can extract a lot of very detailed information. This includes credit card information, user tokens and products being purchased. Despite not being able to alter data in the network stream, it is still easy to view what data is being sent, all this without worrying about intercepting traffic or decrypting the HTTPS stream.
A second example application examined was a banking app. After spending some time using the app and then doing a dump of the hprof, we used strings and grep (and some known data) we could easily see what is being stored in memory.
strings /tmp/android43208542802109.hprof | grep '92xxxxxx'
And there we go, a fully "decrypted" JSON response containing lots of interesting information. Grep'ing around yields other interesting values, though I haven't managed to find the login PIN yet (a good thing I guess).
Next step? Find a way to cause a memory dump in the banking app using another app on the phone, extract the necessary values and steal the banking session, profit.
Memory analysis provides an interesting alternate means of finding data within applications, as well as allowing analysts to decipher how the application operates. The benefits are numerous as the application "does all the work" and there is no need to intercept traffic or figure out the decryption routines used.
ASP.NET HttpHandlers are interesting components of a .NET web application when performing security assessments, mainly due to the fact they are the most exposed part of the application processing client requests in HttpContext level and at the same time, not yet part of the official ASP.NET framework.
As a result, data validation vulnerabilities in custom HttpHandlers can be exploited far easier than issues on the inner layer components. However, they are mostly overlooked during the web application tests for two reasons:
If you are using any of the Telerik components in your application, make sure to replace the "Telerik.Web.UI.dll" with the latest version (about 9MB!).
The Telerik UI control has a web-based charts feature, which stores rendered graphic files in a cache folder for performance reasons. It registers a custom HttpHandler in the web.config file, which processes the following GET request and displays the chart in the client browser:
http://site/ChartImage.axd?useSession=false&imageFormat=image/png&ImageName=[base64 encoded value]
The next step is to decompile the code of the ChartHttpHandler.ProcessRequest(HttpContext), which gives us:
Although, the ImageName query string parameter is encrypted using an AES algorithm to prevent tampering, the encryption key and initialization vector are embedded in the application's assembly (Telerik.Web.UI.dll) and can be used to construct malicious requests to download files from the remote server, as shown in the following figure:
Next time you are on an assessment, don't overlook the mundane and not-so-interesting parts of the application, as they can often provide you with an additional attack surface area.
We blogged a little while back about the Snoopy demonstration given at 44Con London. A similar talk was given at ZaCon in South Africa. Whilst we've been promising a release for a while now, we wanted to make sure all the components were functioning as expected and easy to use. After an army of hundreds had tested it (ok, just a few), you may now obtain a copy of Snoopy from here. Below are some instructions on getting it running (check out the README file from the installer for additional info).
Remind me what Snoopy is?
Snoopy is a distributed tracking, data interception, and profiling framework.
-Ubuntu 12.04 LTS 32bit online server
-One or more Linux based client devices with internet connectivity and a WiFi device supporting injection drivers. We'd recommend the Nokia N900.
-A copy of Maltego Radium
After obtaining a copy from github run the install.sh script. You will be prompted to enter a username to use for Snoopy (default is 'woodstock') and to supply your public IP address. This is depicted below:
This installation will take around 3-5 minutes. At the end of the installation you will be presented with a randomly generated password for the web interface login. Remember it. You may now run the server component with the command snoopy, and you will be presented with the server main menu, as depicted below.
Selecting the 'Manage drone configuration packs' menu option will allow you to create custom installation packs for all of your drone devices. You will be presented with download links for these packs, such that you can download the software to your drones.
From your drone device download and extract the file from given link. Run setup_linux.sh or setup_n900.sh depending on your drone.
All collected probe data gets uploaded to the Snoopy server every 30 seconds. All associated clients have their internet routed through the server over OpenVPN. If you so desire, you can explore the MySQL database 'snoopy' to see this raw data. Graphical data exploration is more fun though.
In the Snoopy server menu select 'Configure server options' > 'List Maltego transform URLs'. This will give URLs to download Maltego Snoopy entities and machines, as well as a list of TDS transform URLs. You will need to download and add the entities and machines to your local Maltego installation, and add the transform URLs to your Maltego TDS account (https://cetas.paterva.com/tds). This is depicted below.
We can explore data my dragging the 'Snoopy' entity onto the canvas. This entity has two useful properties - 'start_time' and 'end_time'. If these are left blank Snoopy will run in 'real time' mode - that is to say displaying data from the last 5 minutes (variable can be set in server configuration menu). This time value will be 'inherited' by entities created from this point. The transforms should be obvious to explore, but below are some examples (further examples were in the original blog post).
I shall write a separate blog post detailing all the transforms. For now, enjoy playing around.
You can access the web interface via http://yoursnoopyserver:5000/. You can write your own data exploration plugins. Check the Appendix of the README file for more info on that.
At this year's 44Con conference (held in London) Daniel and I introduced a project we had been working on for the past few months. Snoopy, a distributed tracking and profiling framework, allowed us to perform some pretty interesting tracking and profiling of mobile users through the use of WiFi. The talk was well received (going on what people said afterwards) by those attending the conference and it was great to see so many others as excited about this as we have been.
In addition to the research, we both took a different approach to the presentation itself. A 'no bullet points' approach was decided upon, so the slides themselves won't be that revealing. Using Steve Jobs as our inspiration, we wanted to bring back the fun to technical conferences, and our presentation hopefully represented that. As I type this, I have been reliably informed that the DVD, and subsequent videos of the talk, is being mastered and will be ready shortly. Once we have it, we will update this blog post. In the meantime, below is a description of the project.
"Snoopy is a distributed tracking and profiling framework."
Below is a diagram of the Snoopy architecture, which I'll elaborate on:
Snoopy runs client side code on any Linux device that has support for wireless monitor mode / packet injection. We call these "drones" due to their optimal nature of being small, inconspicuous, and disposable. Examples of drones we used include the Nokia N900, Alfa R36 router, Sheeva plug, and the RaspberryPi. Numerous drones can be deployed over an area (say 50 all over London) and each device will upload its data to a central server.
A large number of people leave their WiFi on. Even security savvy folk; for example at BlackHat I observed >5,000 devices with their WiFi on. As per the RFC documentation (i.e. not down to individual vendors) client devices send out 'probe requests' looking for networks that the devices have previously connected to (and the user chose to save). The reason for this appears to be two fold; (i) to find hidden APs (not broadcasting beacons) and (ii) to aid quick transition when moving between APs with the same name (e.g. if you have 50 APs in your organisation with the same name). Fire up a terminal and bang out this command to see these probe requests:
tshark -n -i mon0 subtype probereq
(where mon0 is your wireless device, in monitor mode)
Each Snoopy drone collects every observed probe-request, and uploads it to a central server (timestamp, client MAC, SSID, GPS coordinates, and signal strength). On the server side client observations are grouped into 'proximity sessions' - i.e device 00:11:22:33:44:55 was sending probes from 11:15 until 11:45, and therefore we can infer was within proximity to that particular drone during that time.
We now know that this device (and therefore its human) were at a certain location at a certain time. Given enough monitoring stations running over enough time, we can track devices/humans based on this information.
3. Passive Profiling?
We can profile device owners via the network SSIDs in the captured probe requests. This can be done in two ways; simple analysis, and geo-locating.
Simple analysis could be along the lines of "Hmm, you've previously connected to hooters, mcdonalds_wifi, and elCheapoAirlines_wifi - you must be an average Joe" vs "Hmm, you've previously connected to "BA_firstclass, ExpensiveResataurant_wifi, etc - you must be a high roller".
Of more interest, we can potentially geo-locate network SSIDs to GPS coordinates via services like Wigle (whose database is populated via wardriving), and then from GPS coordinates to street address and street view photographs via Google. What's interesting here is that as security folk we've been telling users for years that picking unique SSIDs when using WPA is a "good thing" because the SSID is used as a salt. A side-effect of this is that geo-locating your unique networks becomes much easier. Also, we can typically instantly tell where you work and where you live based on the network name (e.g BTBusinessHub-AB12 vs BTHomeHub-FG12).
The result - you walk past a drone, and I get a street view photograph of where you live, work and play.
4. Rogue Access Points, Data Interception, MITM attacks?
Snoopy drones have the ability to bring up rogue access points. That is to say, if your device is probing for "Starbucks", we'll pretend to be Starbucks, and your device will connect. This is not new, and dates back to Karma in 2005. The attack may have been ahead of its time, due to the far fewer number of wireless devices. Given that every man and his dog now has a WiFi enabled smartphone the attack is much more relevant.
Snoopy differentiates itself with its rogue access points in the way data is routed. Your typical Pineapple, Silica, or various other products store all intercepted data locally, and mangles data locally too. Snoopy drones route all traffic via an OpenVPN connection to a central server. This has several implications:
(i) We can observe traffic from *all* drones in the field at one point on the server. (ii) Any traffic manipulation needs only be done on the server, and not once per drone. (iii) Since each Drone hands out its own DHCP range, when observing network traffic on the server we see the source IP address of the connected clients (resulting in a unique mapping of MAC <-> IP <-> network traffic). (iv) Due to the nature of the connection, the server can directly access the client devices. We could therefore run nmap, Metasploit, etc directly from the server, targeting the client devices. This is a much more desirable approach as compared to running such 'heavy' software on the Drone (like the Pineapple, pr Pwnphone/plug would). (v) Due to the Drone not storing data or malicious tools locally, there is little harm if the device is stolen, or captured by an adversary.
On the Snoopy server, the following is deployed with respect to web traffic:
(i) Transparent Squid server - logs IP, websites, domains, and cookies to a database (ii) sslstrip - transparently hijacks HTTP traffic and prevent HTTPS upgrade by watching for HTTPS links and redirecting. It then maps those links into either look-alike HTTP links or homograph-similar HTTPS links. All credentials are logged to the database (thanks Ian & Junaid). (iii) mitmproxy.py - allows for arbitary code injection, as well as the use of self-signed SSL certificates. By default we inject some JavaScipt which profiles the browser to discern the browser version, what plugins are installed, etc (thanks Willem).
Additionally, a traffic analysis component extracts and reassembles files. e.g. PDFs, VOiP calls, etc. (thanks Ian).
5. Higher Level Profiling? Given that we can intercept network traffic (and have clients' cookies/credentials/browsing habbits/etc) we can extract useful information via social media APIs. For example, we could retrieve all Facebook friends, or Twitter followers.
6. Data Visualization and Exploration? Snoopy has two interfaces on the server; a web interface (thanks Walter), and Maltego transforms.
-The Web Interface The web interface allows basic data exploration, as well as mapping. The mapping part is the most interesting - it displays the position of Snoopy Drones (and client devices within proximity) over time. This is depicted below:
-Maltego Maltego Radium has recently been released; and it is one awesome piece of kit for data exploration and visualisation.What's great about the Radium release is that you can combine multiple transforms together into 'machines'. A few example transformations were created, to demonstrate:
2. Devices at 44Con, pruned
Here we look at all devices and the SSIDs they probed for at 44Con. The pruning consisted of removing all SSIDs that only one client was looking for, or those for which more than 20 were probing for. This could reveal 'relationship' SSIDs. For example, if several people from the same company were attending- they could all be looking for their work SSID. In this case, we noticed the '44Con crew' network being quite popular. To further illustrate Snoopy we 'targeted' these poor chaps- figuring out where they live, as well as their Facebook friends (pulled from intercepted network traffic*).
The pi chart below depicts the proportion of observed devices per vendor, from the total sample of 77,498 devices. It is interesting to see Apple's dominance. pi_chart
The barchart below depicts my day sitting at King's Cross station. The horizontal axis depicts chunks of time per hour, and the vertical access number of unique device observations. We clearly see the rush hours.
Legal -Collecting anonymized statistics on thoroughfare. For example, Transport for London could deploy these devices at every London underground to get statistics on peak human traffic. This would allow them to deploy more staff, or open more pathways, etc. Such data over the period of months and years would likely be of use for future planning. -Penetration testers targeting clients to demonstrate the WiFi threat.
Borderline -This type of technology could likely appeal to advertisers. For example, a reseller of a certain brand of jeans may note that persons who prefer certain technologies (e.g. Apple) frequent certain locations. -Companies could deploy Drones in one of each of their establishments (supermarkets, nightclubs, etc) to monitor user preference. E.g. a observing a migration of customers from one establishment to another after the deployment of certain incentives (e.g. promotions, new layout). -Imagine the Government deploying hundreds of Drones all over a city, and then having field agents with mobile Drones in their pockets. This could be a novel way to track down or follow criminals. The other side of the coin of course being that they track all of us...
Illegal -Let's pretend we want to target David Beckham. We could attend several public events at which David is attending (Drone in pocket), ensuring we are within reasonable proximity to him. We would then look for overlap of commonly observed devices over time at all of these functions. Once we get down to one device observed via this intersection, we could assume the device belongs to David. Perhaps at this point we could bring up a rogue access point that only targets his device, and proceed maliciously from there. Or just satisfy ourselves by geolocating places he frequents. -Botnet infections, malware distribution. That doesn't sound very nice. Snoopy drones could be used to infect users' devices, either by injection malicious web traffic, or firing exploits from the Snoopy server at devices. -Unsolicited advertising. Imagine browsing the web, and an unscrupulous 3rd party injects viagra adverts at the top of every visited page?
Q. I use Apple/Android/Foobar - I'm safe! A. This attack is not dependent on device/manufacture. It's a function of the WiFi specification. The vast majority of observed devices were in fact Apple (>75%).
Q. How can I protect myself? A. Turn off your WiFi when you l leave home/work. Be cautions about using it in public places too - especially on open networks (like Starbucks). A. On Android and on your desktop/laptop you can selectively remove SSIDs from your saved list. As for iPhones there doesn't seem to be option - please correct me if I'm wrong? A. It'd be great to write an application for iPhone/Android that turns off probe-requests, and will only send them if a beacon from a known network name is received.
Q. Your research is dated and has been done before! A. Some of the individual components, perhaps. Having them strung together in our distributed configuration is new (AFAIK). Also, some original ideas where unfortunately published first; as often happens with these things.
Q. But I turn off WiFi, you'll never get me! A. It was interesting to note how many people actually leave WiFi on. e.g. 30,000 people at a single London station during one day. WiFi is only one avenue of attack, look out for the next release using Bluetooth, GSM, NFC, etc :P
Q. You're doing illegal things and you're going to jail! A. As mentioned earlier, the broadcast nature of probe-requests means no laws (in the UK) are being broken. Furthermore, I spoke to a BT Engineer at 44Con, and he told me that there's no copyright on SSID names - i.e. there's nothing illegal about pretending to be "BTOpenzone" or "SkyHome-AFA1". However, I suspect at the point where you start monitoring/modifying network traffic you may get in trouble. Interesting to note that in the USA a judge ruled that data interception on an open network is not illegal.
Q. But I run iOS 5/6 and they say this is fixed!! A. Mark Wuergler of Immunity, Inc did find a flaw whereby iOS devices leaked info about the last 3 networks they had connected to. The BSSID was included in ARP requests, which meant anyone sniffing the traffic originating from that device would be privy to the addresses. Snoopy only looks at broadcast SSIDs at this stage - and so this fix is unrelated. We haven't done any tests with the latest iOS, but will update the blog when we have done so.
Q. I want Snoopy! A. I'm working on it. Currently tidying up code, writing documentation, etc. Soon :-)