Monday morning, raring for a week of pwnage and you see you've just been handed a new assessment, awesome. The problem? It's a mobile assessment and you've never done one before. What do you do, approach your team leader and ask for another assessment? He's going to tell you to learn how to do a mobile assessment and do it quickly, there are plenty more to come.

Now you set out on your journey into mobile assessments and you get lucky, the application that needs to be assessed is an Android app. A few Google searches later and you are feeling pretty confident about this, Android assessments are meant to be easy, there are even a few tools out there that "do it all". You download the latest and greatest version, run it and the app gets a clean bill of health. After all, the tool says so, there is no attack surface; no exposed intents and the permissions all check out. You compile your report, hand it off to the client and a week later the client gets owned through the application... Apparently the backend servers were accepting application input without performing any authentication checks. Furthermore, all user input was trusted and no server side validation was being performed. What went wrong? How did you miss these basic mistakes? After-all, you followed all the steps, you ran the best tools and you ticked all the boxes. Unfortunately this approach is wrong, mobile assessments are not always simply about running a tool, a lot of the time they require the same steps used to test web applications, just applied in a different manner. This is where SensePost's Hacking by numbers: Mobile comes to the fore, the course aims to introduce you to mobile training from the ground up.

The course offers hands-on training, introducing techniques for assessing applications on Android, IOS, RIM and Windows 8. Some of the areas covered include:

Unlike other mobile training or tutorials that focus on a specific platform or a specific tool on that platform, Hacking by Numbers aims to give you the knowledge to perform assessments on any platform with a well established methodology. Building on everything taught in the Hacking by Numbers series, the mobile course aims to move assessments into mobile sphere, continuing the strong tradition of pwnage. The labs are a direct result of the assessments we've done for clients. Our trainers do this on a weekly basis, so you get the knowledge learned from assessing numerous apps over the last few years.

On your next mobile assessment you'll be able to do both static and dynamic analysis of mobile applications. You will know where to find those credit card numbers stored on the phone and how to intercept traffic between the application and the backend servers.

The course: Hacking by numbers: Mobile

When doing wireless assessments, I end up generating a ton of different scripts for various things that I thought it would be worth sharing. I'm going to try write some of them up. This is the first one on decrypting WPA/2 PSK traffic. The second will cover some tricks/scripts for rogue access-points. If you are keen on learn further techniques or advancing your wifi hacking knowledge/capability as a whole, please check out the course Hacking by Numbers: Unplugged, I'll be teaching at BlackHat Las Vegas soon.

When hackers find a WPA/2 network using a pre-shared key, the first thing they try and do most times, is to capture enough of the 4-way handshake to attempt to brute force the pairwise master key (PMK, or just the pre-shared key PSK). But, this often takes a very long time. If you employ other routes to find the key (say a client-side compromise) that can still take some time. Once you have the key, you can of course associate to the network and perform your layer 2 hackery. However, if you had been capturing traffic from the beginning, you would now be in a position to decrypt that traffic for analysis, rather than having to waste time by only starting your capture now. You can use the airdecap-ng tool from the aircrack-ng suite to do this:

airdecap-ng -b <BSSID of target network> -e <ESSID of target network> -p <WPA passphrase> <input pcap file>

However, because the WPA 4-way handshake generates a unique temporary key (pairwise temporal key PTK) every time a station associates, you need to have captured the two bits of random data shared between the station and the AP (the authenticator nonce and supplicant nonce) for that handshake to be able to initialise your crypto with the same data. What this means, is that if you didn't capture a handshake for the start of a WPA/2 session, then you won't be able to decrypt the traffic, even if you have the key.

So, the trick is to de-auth all users from the AP and start capturing right at the beginning. This can be done quite simply using aireplay-ng:

aireplay-ng --deauth=5 -e <ESSID>

Although, broadcast de-auth's aren't always as successful as a targeted one, where you spoof a directed deauth packet claiming to come from the AP and targeting a specific station. I often use airodump-ng to dump a list of associated stations to a csv file (with --output-format csv), then use some grep/cut-fu to excise their MAC addresses. I then pass that to aireplay-ng with:

cat <list of associated station MACs>.txt | xargs -n1 -I% aireplay-ng --deauth=5 -e <ESSID> -c % mon0

This tends to work a bit better, as I've seen some devices which appear to ignore a broadcast de-auth. This will make sure you capture the handshake so airdecap can decrypt the traffic you capture. Any further legitimate disconnects and re-auths will be captured by you, so you shouldn't need to run the de-auth again.

In summary:

• Don't forget how useful examining traffic can be, and don't discount that as an option just because it's WPA/2


• Start capturing as soon as you get near the network, to maximise how much traffic you'll have to examine


• De-auth all connected clients to make sure you capture their handshakes for decryption

Once again, I'll be teaching a course covering this and other techniques at BlackHat Las Vegas, please check it out or recommend it to others if you think it's worthwhile. We're also running a curriculum of other courses at BH, including a brand new mobile hacking course.

There are multiple paths one could take to getting Domain Admin on a Microsoft Windows Active Directory Domain. One common method for achieving this is to start by finding a system where a privileged domain account, such as a domain admin, is logged into or has recently been logged into. Once access to this system has been gained, either stealing their security tokens (ala Incognito or pass-the-hash attacks) or querying Digest Authentication (with Mimikatz/WCE) to get their clear-text password. The problem is finding out where these user's are logged in.

I've often seen nmap and the smb-enum-sessions script (http://nmap.org/nsedoc/scripts/smb-enum-sessions.html) used to retrieve all the user sessions on the network. This (not so grep'pable) output is then grep'ed to find the hosts where our target user is logged in. The process of smb-enum-sessions and subsequent analysis can be quite time consuming and clumsy. On a recent assessment, multiple tunnels in, where uploading nmap wasn't a great idea, we realised that there has to be a better way of doing this. While searching for an alternative solution we came across PsLoggedOn (SysInternals Suite) which, with a single binary, allows you search the network for locations where a user is logged in. The downside with this is that it doesn't cleanly run via psexec or other remote shells and you need graphical logon to a system on the domain, and you need to upload another binary (the PsLoggedOn executable) to the target system. Examining how PsLoggedOn worked we figured out that it was simply using the Windows NetSessionEnum API. Having a look at the API I figured that it should be possible to write a simple post exploit module for Metasploit using the railgun.

After some trial and error, we now present enum_domain_user.rb a simple Metasploit post exploit module capable of finding network sessions for a specific user. Below is a screenshot of the module in action.

To use the module,

1.) Download and copy it to:
<msfinstall>/modules/post/windows/gather/
(we'll send a pull request to metasploit-framework's github shortly).

2.) In MSF:
use post/windows/gather/enum_domain_user

3.) Set the USER and SESSION variables.

4.) Then simply run it with "exploit".

The module can also be used directly from meterpreter with:
run post/windows/gather/enum_domain_user USER=username

Warning, this doesn't seem to work with x64 meterpreter yet mostly likely due to some memory pointer stuff I haven't worked out. Hopefully this will get updated shortly, or even better, one of you smart people out there can fix my horrible Ruby.

Bonus

As an added extra I've included a Metapsloit history plugin. This plugin will simply allow you to view all the commands executed since the module was loaded and then execute them "bash style".

Typing "history" will give display the last 10 commands executed. If you wish to see more commands, type history <numberof entries>

To run a command from the history list type:
history !<command number>

Below is an action shot of the history module.

To install:

1.) Download and Copy history.rb to the plugins folder: <msf install>/plugins/
2.) In msfconsole type: load history
3.) For usage info type: help history

Both modules are available for download on Github, and I'll submit a pull request to metasploit-framework shortly. Please feel free to fork and be merry. Any updates/fixes/comments are welcome.
Github: https://github.com/sensepost/metasploit

A cloud storage service such as Microsoft SkyDrive requires building data centers as well as operational and maintenance costs. An alternative approach is based on distributed computing model which utilizes portion of the storage and processing resources of consumer level computers and SME NAS devices to form a peer to peer storage system. The members contribute some of their local storage space to the system and in return receive "online backup and data sharing" service. Providing data confidentiality, integrity and availability in such de-centerlized storage system is a big challenge to be addressed. As the cost of data storage devices declines, there is a debate that whether the P2P storage could really be cost saving or not. I leave this debate to the critics and instead I will look into a peer to peer storage system and study its security measures and possible issues. An overview of this system's architecture is shown in the following picture:

Each node in the storage cloud receives an amount of free online storage space which can be increased by the control server if the node agrees to "contribute" some of its local hard drive space to the system. File synchronisation and contribution agents that are running on every node interact with the cloud control server and other nodes as shown in the above picture. Folder/File synchronisation is performed in the following steps:

1) The node authenticates itself to the control server and sends file upload request with file meta data including SHA1 hash value, size, number of fragments and file name over HTTPS connection.

2) The control server replies with the AES encryption key for the relevant file/folder, a [IP Address]:[Port number] list of contributing nodes called "endpoints list" and a file ID.

3) The file is split into blocks each of which is encrypted with the above AES encryption key. The blocks are further split into 64 fragments and redundancy information also gets added to them.

4) The node then connects to the contribution agent on each endpoint address that was received in step 2 and uploads one fragment to each of them

Since the system nodes are not under full control of the control server, they fall offline any time or the stored file fragments may become damaged/modified intentionally. As such, the control server needs to monitor node and fragment health regularly so that it may move lost/damaged fragments to alternate nodes if need be. For this purpose, the contribution agent on each node maintains an HTTPS connection to the control server on which it receives the following "tasks":

a) Adjust settings : instructs the node to modify its upload/download limits , contribution size and etc

b) Block check : asks the node to connect to another contribution node and verify a fragment existence and hash value

c) Block Recovery : Assist the control server to recover a number of fragments

By delegating the above task, the control system has placed some degree of "trust" or at least "assumptions" about the availability and integrity of the agent software running on the storage cloud nodes. However, those agents can be manipulated by malicious nodes in order to disrupt cloud operations, attack other nodes or even gain unauthorised access to the distributed data. I limited the scope of my research to the synchronisation and contribution agent software of two storage nodes under my control - one of which was acting as a contribution node. I didn't include the analysis of the encryption or redundancy of the system in my preliminary research because it could affect the live system and should only be performed on a test environment which was not possible to set up, as the target system's control server was not publicly available. Within the contribution agent alone, I identified that not only did I have unauthorised access file storage (and download) on the cloud's nodes, but I had unauthorised access to the folder encryption keys as well.

a) Unauthorised file storage and download

The contribution agent created a TCP network listener that processed commands from the control server as well as requests from other nodes. The agent communicated over HTTP(s) with the control server and other nodes in the cloud. An example file fragment upload request from a remote node is shown below:

Uploading fragments with similar format to the above path name resulted in the "bad request" error from the agent. This indicated that the fragment name should be related to its content and this condition is checked by the contribution agent before accepting the PUT request. By decompiling the agent software code, it was found that the fragment name must have the following format to pass this validation:

<SHA1(uploaded content)>.<Fragment number>.<Global Folder Id>

I used the above file fragment format to upload notepad.exe to the remote node successfully as you can see in the following figure:

The download request (GET request) was also successful regardless of the validity of "Global Folder Id" and "Fragment Number". The uploaded file was accessible for about 24 hours, until it was purged automatically by the contribution agent, probably because it won't receive any "Block Check" requests for the control server for this fragment. Twenty four hours still is enough time for malicious users to abuse storage cloud nodes bandwidth and storage to serve their contents over the internet without victim's knowledge.

b) Unauthorised access to folder encryption keys

The network listener responded to GET requests from any remote node as mentioned above. This was intended to serve "Block Check" commands from the control server which instructs a node to fetch a number of fragments from other nodes (referred to as "endpoints") and verify their integrity but re-calculating the SHA1 hash and reporting back to the control server. This could be part of the cloud "health check" process to ensure that the distributed file fragments are accessible and not tampered with. The agent could also process "File Recovery" tasks from the control server but I didn't observe any such command from the control server during the dynamic analysis of the contribution agent, so I searched the decompiled code for clues on the file recovery process and found the following code snippet which could suggest that the agent is cable of retrieving encryption keys from the control server. This was something odd, considering that each node should only have access to its own folders encryption keys and it stores encrypted file fragments of other nodes.

One possible explanation for the above file recovery code, could be that the node first downloads its own file fragments from remote endpoints (using an endpoint list received from the control server) and then retrieves the required folder encryption key from the control server in order to decrypt and re-assemble its own files. In order to test if it's possible to abuse the file recovery operation to gain access to encryption key of the folders belonging to other nodes. I extracted the folderInfo request format from the agent code and set up another storage node as a target to test this idea. The result of the test was successful as shown in the following figure and it was possible to retrieve the AES-256 encryption key for the Folder Id "1099869693336". This could enable an attacker who has set up an contributing storage node to decrypt the fragments that belong to other cloud users.

Conclusion:

While peer to peer storage systems have lower setup/maintenance costs, they face security threats from the storage nodes that are not under direct physical/remote control of the cloud controller system. Examples of such threats relate to the cloud's client agent software and the cloud server's authorisation control, as demonstrated in this post. While analysis of the data encryption and redundancy in the peer to peer storage system would be an interesting future research topic, we hope that the findings from this research can be used to improve the security of various distributed storage systems.

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>

But there is an easier way, use the official Android debugging tools... Dalvik Debug Monitor Server (DDMS), -- "provides port-forwarding services, screen capture on the device, thread and heap information on the device, logcat, process, and radio state information, incoming call and SMS spoofing, location data spoofing, and more." Once DDMS is set up in Eclipse, it's simply a matter of connecting to your emulator, picking the application you want to investigate and then to dump the heap (hprof).

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'

Using part of the card number associated with the banking app, we can locate any references to it in memory. And we get a lot of information..

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.

Appendix:

The remoteAddress field in the response is very interesting as it maps back to a range owned by Merck (one of the largest pharmaceutical companies in the world http://en.wikipedia.org/wiki/Merck_%26_Co.) .. No idea what it's doing in this particular app, but it appears in every session I've looked at.