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 :-)
While I was evaluating a research idea about a SCADA network router during the past week, I used available tools and resources on the Internet to unpack the device firmware and search for interesting components. During security assessments, you may find interesting embedded devices available on the network. Whilst many don't look at the feasibility of doing firmware analysis, I decided to document the steps I took to analysis my target firmware, so you can take the similar approach in the case of assessing such devices. This could also be a good indication on the feasibility of automating this process (An unfinished project was launched in 2007: http://www.uberwall.org/bin/project/display/85/UWfirmforce).
The following process would be easy for most of you who use *nix systems on a daily bases:
Step 1) Scanning the firmware image
The BinWalk tool is useful for scanning firmware image files to identify embedded file systems and compressed streams inside. It can detect common bootloaders, file systems and compressed archives inside a given firmware image file. Since it works by scanning for signature and magic values, it usually has false positives and the results need to be verified manually.
U-Boot bootloader (yes, it's German :-)) signature was identified at offset 262144 and the uImage header information, such as creation date, CPU type, etc appears to be valid. This bootloader was followed by a gzip compressed stream, which probably is the zImage kernel and a squashfs file system at offset 1522004. We will attempt to extract this file system in the next step. The following are common bootloaders that are used in embedded devices with ARM CPU:
The bootloader's task is to load the kernel image at the correct address and pass initial parameters to it. So in most cases we are not interested in analysing the bootloader itself, but in the root file system.
Step 2) Extracting file systems
First, I extracted the uImage content at offset 262144 by using dd command and then used uboot-mkimage (packages.debian.org/uboot-mkimage) to test if it's a valid uImage file and to discover more information about it:
The image format was valid and it contained two other file system images with 1MB and 2MB sizes, which probably are kernel zImage and root file systems (RAMdisk). If you check the uImage file format, you will notice a 64 bytes long header. There is a “multi-file” image list that contains each image size in bytes and this list is terminated by a 32bit zero. So, I would need to skip 64+2*4+4=76 bytes from start of the uImage file to get to the first Image content that would be kernel zImage:
The file command could not detect kernel image or squshfs in the extracted file systems; this might be due to lack of squashfs (with LZMA compression) in my Ubuntu kernel. I proceed by using Firmware Mod Kit which contains a set of programs to decompress various file system images including squashfs-LZMA. After trying the various unsquashfs version 3.x scripts, I was able extract the rootfs image files successfully:
Step 3) Searching the root file system
Once the root file system files were extracted, we can file and strings search tools to look for interesting files and patterns such as RSA private key files, password and configuration files, SQL database files, SQL query string and etc. In my case, I was looking for RSA certificate or private key files and found the following: (a database of private keys in embedded devices was published in 2011 but it's not actively maintained, you can access it at http://code.google.com/p/littleblackbox/)
One can write shell scripts to automate the file system search process.
Step 4) Running and debugging the Executables
The Qemu emulator supports multiple CPU architectures including ARM, MIPS, PowerPC, etc and can be used to run and debug the interesting executable extracted from the firmware image on your system for dynamic analysis purposes. You would need to build the Qemu with —static and —enable-debug options. The following figure demonstrates how to run the web server (httpd) that was extracted from my target firmware using chroot and Qemu:
For troubleshooting such cases, or monitoring an emulated process while fuzzing it, we would need to attach a debugger to it. This can be achieved by using —g switch in Qemu and using a debugger out of the emulator process or even on a remote windows machine. I used IDA pro remote GDB debugging tool as shown in the figures below:
Once successfully attached to the remote emulated process, IDA pro can be used to simply trace the execution of the process, placing breakpoints or running IDA scripts.
Often overlooked during assessments, firmware analysis of devices can yield results and often do when we target them at SensePost. Our methodology includes the above steps and we recommend yours does too.
We were asked to contribute an article to PenTest magazine, and chose to write up an introductory how-to on footprinting. We've republished it here for those interested.
Network foot printing is, perhaps, the first active step in the reconnaissance phase of an external network security engagement. This phase is often highly automated with little human interaction as the techniques appear, at first glance, to be easily applied in a general fashion across a broad range of targets. As a security analyst, footprinting is also one of the most enjoyable parts of my job as I attempt to outperform the automatons; it is all about finding that one target that everybody forgot about or did not even know they had, that one old IIS 5 webserver that is not used, but not powered off.
With this article I am going to share some of the steps, tips and tricks that pentesters and hackers alike use when starting on a engagement.
As with most things in life having a good approach to a problem will yield better results and overtime as your approach is refined you will consume less time while getting better results. By following a methodology, your footprinting will become more repeatable and thus reliable. A basic footprining methodology covers reconnaissance, DNS mining, various information services (e.g. whois, Robtex, routes), network registration information and active steps such as SSL host enumeration.
While the temptation exists to merely feed a domain name into a tool or script and take the output as your completed footprint, this will not yield a passable footprint for two reasons. Firstly, a single tool will not have access to all the disparate information sources that one should consult, and secondly the footprinting process is inherently iterative and continuous. A footprint is almost never complete; instead, a fork of the footprint data provides the best current view of the target, but the information could change tomorrow as new sites are brought online, or old sites are taken offline. As a new piece of data is found that could expand the footprint, a new iteration of the footprinting process triggers with that datum as the seed, and the results are combined with all discovered information.
Know your target
The very first thing to do is to get to know your target organisation. What they do, who they do it for, who does it for them, where they do it from - both online and in the kinetic world, what community or charity work they are involved in. This will give you an insight into what type of network/infrastructure you can expect. Reading public announcements, financial reports and any other documents published on or by the organisation might also yield interesting results. Any organisation that must publish regular reports (e.g. listed companies), provide a treasure trove of information for understanding the target's core business units, corporate hierarchy and lines of business. All these become very useful when selecting targets.
Dumpster diving, if you are up for it and have physical access to the target, means sifting through trash to get useful information, but in recent times social media can provide us with even more. Sites like LinkedIn, Facebook and Twitter can provide you with lists of employees and projects that the organisation is involved with and perhaps even information about third party products and suppliers that are in use.
One should even keep an eye out for evidence of previous breaches or loss of credentials. It has become common place for hackers to post information about security breaches on sites like pastebin.com. The most likely evidence would be credentials in the form of corporate emails and passwords being reused on unrelated sites that are hacked, and have their user databases uploaded. In addition, developers use sites like Pastebin to share code, ideas and patches, and if you are lucky you might just find a little snippet of code sitting out in the open on Pastebin, that will give you the edge.
In a nutshell, DNS is used to convert computer names to their numeric addresses.
Start by enumerating every possible domain owned by the target. This is where the information from the initial reconnaissance phase comes in handy, as the target's website will likely point to external domains of interest and also help you guess at possible names. With a list of most discovered domains in hand, move on to a TLD (Top level domain) expand. TLDs are the highest level subdomains in DNS; .com, .net, .za, .mobi are all examples of TLDs (The Mozilla Organization maintains a list of TLDs https://wiki.mozilla.org/TLD_List).
In the next step, we take a discovered discovered domain and check to see if there are any other domains with the same name, but with a different TLD. For example, if the target has the domain victim.com, test whether the domains victim.net, victim.info, victim.org etc. exist and if they exist check to see if they are owned by our target organization. To determine whether a domain exists or not, one should examine the SOA (start of authority) DNS record for the domain. Using commands like nslookup under Microsoft Windows or the dig/host commands under most of the *nix family will reveal SOA records.
Using dig, “dig zonetransfer.me soa”.
Figure 1: Using dig to get the SOA (Start of authority) record for a domain
If, by verifying the SOA, it is confirmed that the domain exists, then the next step is to track down who it belongs to. At this point the whois service is called upon. ‘Whois' is simply a registry that contains the information of the owner of a domain. Note that it is not entirely reliable and certainly not consistent. The following very simple query “whois zonetransfer.me” provides us with the owner of the domain “zonetransfer.me” detail.
Figure 2: Using whois to get the domain owner detail
After finding domains, running them through a TLD expansion and verifying their whois information, it is time to track down hosts. First we need to get the NS or name server records for the domains. Again using “dig zonetransfer.me ns” returns a list of all the name servers used by this domain. In many cases the name server will not be part of the target's network and is often out-of-scope, but they will still be used in the next step.
DNS yields much interesting information, but the default methods for extracting information from foreign servers effectively relies on a brute force. However, DNS supports a trick where all DNS information for a zone can be downloaded if the server allows it, and this is called a “zone transfer”. When enabled, they are extremely useful as they negate the need for guessing or brute-forcing; sadly they are commonly disabled. Still, given the usefulness of zone transfers it is always worth testing for. Zone transfers should be performed against all the name servers that are specified in the NS records of a domain as the data contained in each name server should be the same, but the security configuration might be different. Using dig, the following command will attempt to perform a zone transfer “dig axfr @ns12.zoneedit.com zonetransfer.me”
Figure 3: Performing a zone transfer using dig
As mentioned previously, zone transfers are not that common. When we cannot download the zone file, there are a couple of other tricks that might work. One is to brute force or guess host names: by using a long list of common hostnames one can test for names such as “fw.victim.com”, “intranet.victim.com”, “mail.victim.com” and so on. The names can be commonly seen hostnames, generated names when computers are assigned numeric or algorithmic names, or from sets of related names such as characters from a book series. When brute forcing DNS, be sure to check the following DNS records: CNAME, A and AAAA. Again this is easy using a tool like dig. “dig www.google.com a” produces the DNS configuration for www.google.com, note that the hostname www.google.com actually has multiple DNS entries, one CNAME record, and multiple A records. Looking at the IP addresses it is clear that there are several different hosts (2 in the screenshot below).
Figure 4: Using dig to get the a record for a host entry
Doing this manually seems easy and quick, (and it is) but if we want to brute force or guess many host names, then this will take too long. Of course, it is easy enough to script these commands to automate the process; however there are existing tools written specifically for this purpose. One of the most popular tools, Fierce, is a perl script written by RSnake (http://ha.ckers.org/fierce/), which is easy to use and has many useful functions. Additionally, there are tools like Paterva's Maltego and SensePost's Yeti (a tool I wrote) which provide graphical tools for this purpose.
If we happen to have a list of IP addresses or IP netblocks of the target, then a further DNS trick is to convert the addresses into hostnames using reverse lookups to get the PTR record entry. This is useful since reverse records are easily brute forced in IPv4. Bear in mind that DNS does not require a PTR record (reverse entry) or that entries in the reverse zone must match entries in the forward zone. But the result can give you an idea of whether the host is a shared host, owned and hosted by the company or just remote hosted.
To test once more, try using dig, “dig 220.127.116.11.in-addr.arpa ptr”. While this too can be easily automated, the previously mentioned tools will also handle PTR records.
DNS interrogation and mining forms the bulk foot printing, but thanks to modern search engines like Google and Bing, finding targets has become much easier.
Apart from the normal searching for your target, as you would do in your initial phase, you can actually use the data that you discovered during the course of the DNS mining to try and get further information using search engines. Bing from Microsoft provides us with two really useful search operators: “ip:” and “site:”. When using the “ip:” operator, Bing will return a list of hosts that it has indexed that resolve to the IP address that you have specified. Alternatively the “site:” operator when used with a domain name, will return a list of host names that have been indexed by the search engine and belong to the domain specified. Quick and easy, and Bing also provides you with a very simple free API that you can use to automate these searches.
All this fuss with DNS is important, but it is only useful insofar as they lead us to addresses. The next step is discovering where the target exists within the IP address space. Luckily useful tools and resources exist to help us uncover these ranges, by automating a combination of manual techniques such as whois querying, traceroute and netblock calculators. In the previous section the whois tool was used to get the domain owner information. The same tool can be used to discover the ownership/assignment details of a specific IP address. Let's take www.facebook.com; one of the IP addresses that it resolves to is 18.104.22.168. “whois 22.214.171.124” produces the following output.
Figure 5: Getting the netblock and owner using whois
From the whois output we get really useful information. First is a netblock range 126.96.36.199-188.8.131.52 as well as the owner of this net block, namely Facebook, Inc. In this case we are lucky and the netblock is registered to facebook, but often you will only get the network service provider to which the netblock is allocated to. In that case, you will have to query the service provider in order to gain more info about the specific netblock. Online resources can also be very useful, for example ARIN (American Registry for Internet Numbers) or any of the other regional registries (RIPE, AfriNIC, APNIC and LACNIC) provides a reverse whois search interface where one can search for organisation names and other terms, even performing wild card searches. Giving Facebook a second look, we try a search on the reverse whois interface found at http://whois.arin.net/ with the term “facebook”, and get a list of five additional network ranges.
Figure 6: Search results for ARIN reverse whois
Lastly, we turn to SSL. SSL may be more familiar as a “protection” against nasty eavesdroppers and men-in-the-middle, but it is useful for footprinters. How? It is really simple actually, one of the security checks performed by browsers when deciding on the validity of a SSL certificate is whether the Common Name contained in the certificate matches the DNS name of the host requested from the browser. How does this help? Say a list of IP addresses has been produced; the next step would be to perform a reverse lookup of all these addresses. However, if no reverse entry is present and Bing has no record of the IP, then some creativity is called for. If an HTTPS website is hosted on that address then simply browse to that IP address and, when presented with the invalid certificate error, message, look for the “real” host name.
Figure 7: Firefox reporting the common name contained in a SSL certificate for a host
Again, this is something that is easily automated, so we have included a module in Yeti to actually do this for you.
Foot printing might at first glance appear to be simple and mundane, but the more you do it, the more you will realise that very few organisations have a handle on exactly what they have and what they present to the Internet. As the Internet and networks evolve so will the way companies and organisations use it, and so will their footprint. A year-old footprint could be hopelessly outdated, and ongoing footprinting helps organisations maintain a current view of their threat landscape.
With the ongoing move away from local infrastructure to hosted infrastructure, the footprint expands, spreads and grows, and so will our quest to find as much as possible.