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A New Program for Your Peloton – Whether You Like It or Not

16 June 2021 at 04:01
Connected Fitness

Executive Summary 

The McAfee Advanced Threat Research team (ATR) is committed to uncovering security issues in both software and hardware to help developers provide safer products for businesses and consumers. As security researchers, something that we always try to establish before looking at a target is what our scope should be. More specifically, we often assume well-vetted technologies like network stacks or the OS layers are sound and instead focus our attention on the application layers or software that is specific to a target. Whether that approach is comprehensive sometimes doesn’t matter; and it’s what we decided to do for this project as well, bypassing the Android OS itself and with a focus on the Peloton code and implementations. During our research process, we uncovered a flaw (CVE-2021-33887) in the Android Verified Boot (AVB) process, which was initially out of scope, that left the Peloton vulnerable. 

For those that are not familiar with Peloton, it is a brand that has combined high end exercise equipment with cutting-edge technology. Its products are equipped with a large tablet that interfaces with the components of the fitness machine, as well as provides a way to attend virtual workout classes over the internet. “Under the hood” of this glossy exterior, however, is a standard Android tablet, and this hi-tech approach to exercise equipment has not gone unnoticed. Viral marketing mishaps aside, Peloton has garnered attention recently regarding concerns surrounding the privacy and security of its products. So, we decided to take a look for ourselves and purchased a Pelton Bike+.

Attempting to Backup 

One of the first things that we usually try do when starting a new project, especially when said projects involve large expenses like the Peloton, is to try to find a way to take a backup or system dump that could be used if a recovery is ever needed. Not all of our research techniques keep the device in a pristine state (we’d be poor hackers if they did)and having the ability to restore the device to its factory settings is a safety net that we try to implement on our targets 

Because we are working with a normal Android device with only the Peloton customizations running at the application layer, many of the processes used to back up an Android phone would also work with the Peloton. It is common in the Android custom ROM scene to use a custom recovery image that allows the user to take full flash dumps of each critical partition and provides a method to restore them later. In such communities, it often also goes without saying that the device must first be unlocked in order to perform any of these steps. While the Android OS allows users to flash these critical partitions, there are restrictions in place that typically prevent an attacker from gaining access to the “currently” running system. If an attacker was able to get their hands on an Android device with the goal of installing a rootkit, they would have to jump through some hoops. The first step that an attacker would need to take is to enable “Original Equipment Manufacturer (OEM) Unlocking”, which is a user mode setting within the “developer options” menu. Even with physical access to the bootloader, an attacker would not be able to “unlock” the Android device unless this setting is checked. This option is usually secured behind the user’s password, PIN, or biometric phone lock, preventing an attacker from accessing it easily. The second security measure in place is that even with the “OEM Unlocking” setting on, issuing commands to the bootloader to perform the unlock first causes all data on the Android device, including applications, files, passwords, etc., to be wiped. This way, even if an attacker did gain access to the Android device of an unsuspecting victim, they wouldn’t be able to install a rootkit or modify the existing kernel without deleting all the data, which both prevents personal data from falling into the attacker’s hands and makes it obvious the device has been tampered with. 

For this research effort, wresisted the urge to unlock the Peloton, as there are ways for apps to query the unlock status of a device within Android, and we wanted to ensure that any vulnerabilities we found weren’t the result of the device behaving differently due to it being unlocked. These discrepancies that arise from our research are usually identified by having two target devices: one to serve as the control and the other to serve as the test device. Unfortunately, we only had one Peloton to play with. Another issue was that the Peloton hardware is not very common and the developers of the aforementioned custom recovery images, like Team Win Recovery Project (TWRP), don’t create images for every device,  just the most common ones. So, the easy method of taking a backup would not only require unlocking the device but also trying to create our own custom recovery image 

This left us as at a crossroads. We could unlock the bootloader and root the device, granting us access to the flash memory block devices (raw interfaces to the flash partitions) internallywhich would allow us to create and restore backups as needed. However, as mentioned before, this would leave the bike in a recognizably “tampered” state. Alternatively, we could try to capture one of the bike’s Over-The-Air (OTA) updates to use as backup, but we would still need to “unlock” the device to actually flash the OTA image manually. Both options were less than ideal so we kept looking for other solutions. 

Android Verified Boot Process

Just as Secure Boot provides a security mechanism for properly booting the OS on Windows PCs, Android has implemented measures to control the boot process, called Android Verified Boot (AVB). According to Android’s documentation, AVB requires cryptographically verifying all executable code and data that is part of the Android version being booted before it is used. This includes the kernel (loaded from the boot partition), the device tree (loaded from the dtbo partition), system partition, vendor partition, and so on. 

The Peloton Bike+ ships with the default settings of “Verity Mode” set to trueas well as “Device Unlocked” and “Device Critical Unlocked” set to falsewhich is intended to prevent the loading of modified boot images and provide a way to determine if the device has been tampered with. This information was verified by running fastboot oem device-info on the Peloton, as demonstrated in Figure 1. 


Figure 1: OEM device info showing verity mode and unlocked status. 

To clarify, a simplified Android boot process can be visualized as follows: 

Figure 2: Simplified Android Boot Process 

If modified code is found at any of the stages in Figure 2, the boot process should abort or, if the device is unlocked, warn the user that the images are not verified and give the option to the user to abort the boot. 

Given that we defined our scope of this project to not include the Android boot process as a part of our research and verifying that Peloton has attempted to use the security measures provided by Android, we again found ourselves debating if a backup would be possible.  

In newer Android releases, including the Peloton, the update method uses Android’s Seamless System Updates (A/B). This update method no longer needs the “recovery” partition, forcing users who wish to use a custom recovery to use the fastboot boot command which will download and boot the supplied image. This is a temporary boot that doesn’t “flash“ or alter any of the flash partitions of the device and will revert to the previous boot image on restartSince this option allows for modified code to be executed, it is only available when the device is in an unlocked state and will error out with a message stating Please unlock device to enable this command, if attempted on a locked device.  

This is a good security implementation because if this command was always allowed, it would be very similar to the process of booting from a live USB on your PC, where you can login as a root user and have full control over the underlying system and components. 

Booting Modified Code 

This is where our luck or maybe naïveté worked to our advantage. Driven by our reluctance to unlock the device and our desire to make a backup, we tried to boot a generic TWRP recovery image just to see what would happen. The image ended up leaving us at a black screen, and since each recovery image needs to contain a small kernel with the correct drivers for the display, touch digitizer, and other devicespecific hardware, this was to be expectedWhat we didn’t expect, however, was for it to get past the fastboot boot command. While we didn’t get a custom recovery running, it did tell us one thingthe system was not verifying that the device was unlocked before attempting to boot a custom imageNormally this command would be denied on a “locked” device and would have just errored out on the fastboot command, as mentioned previously. 

It is also important to point out that despite having booted a modified image, the internal fuse had not been burned. These fuses are usually burned during the OEM unlocking process to identify if a device has allowed for a different “root of trust” to be installed. The burning of such a fuse is a permanent operation and a burnt fuse often indicates that the device has been tampered with. As shown in Figure 3, the “Secure Boot” fuse was still present, and the device was reporting a locked bootloader. 

Figure 3: Secure boot enabled with fused protection 

Acquiring an OTA Image 

This discovery was unexpected and we felt like we had stumbled upon a flaw that gave us the ability to finally take a backup of the device and leave the Peloton in an “untampered” state. Knowing that a custom image could be booted even with a “locked” bootloader, we began looking at ways to gather a valid boot image, which would contain the correct kernel drivers to facilitate a successful boot. If we could piece together the OTA update URL and just download an update package directly from Peloton, it would likely contain a boot image that we could modifyHaving the ability to modify a boot image would give us root and access to the blocked devices. 

Even with just ADB debugging enabled we were able to pull the Pelotonspecific applications from the device. We listed all the Peloton APKand sought out the ones that could help us get the OTA path, shown in Figure 4. 

Figure 4: Listing Peloton Specific Applications and Highlighting the one related to OTA Updates. 

Finding the name OTAService promising, we pulled down the APK and began to reverse-engineer it using JADX. After some digging, we discovered how the app was building the download URL string for OTA updateswhich would then be passed to beginDownload(), as seen in Figure 5. 

Figure 5OTA image path being constructed as “key” 

We also noticed quite a few Android log calls that could help us, such as the one right before the call to beginDownload(), so we used Android’s builtin logcat command and grepped the output for “OTA” as seen in Figure 6. Doing so, we were able to find which S3 bucket was used for the OTA updates and even a file manifest titled OTAConfig.json  

Figure 6: Relevant OTA logs in red 

Combining the information obtained from OTAService.apk and the logs, we were able to piece together the full path to the OTA images manifest file and names for each OTA zip file, as shown in Figure 7.  

Figure 7: Contents of OTAConfig.json 

Our next step was to extract the contents of the OTA update to get a valid boot.img file that would contain all the specific kernel drivers for the Peloton hardware. Since the Peloton is using AndroidA/B partitions, which facilitate seamless updates, the update packages were stored in a “payload.bin” format. Using the Android payload dumper tool, we were able to extract all of the images contained in the bin file. 

Modifying the Boot Image 

Once the boot.img was extracted, we needed a way to modify the initial kernel to allow us to gain root access on the device. Although there are a variety of ways to accomplish this, we decided to keep things simple and just use the Magisk installer to patch the boot.img file to include the “su” binary. With the boot.img patched, we were able to use the fastboot boot command again but this time passing it our patched boot.img file. Since the Verified Boot process on the Peloton failed to identify the modified boot image as tampered, the OS booted normally with the patched boot.img file. After this process was complete, the Peloton Bike+ was indistinguishable from its “normal” state under visual inspection and the process left no artifacts that would tip off the user that the Pelton had been compromised. But appearances can be deceiving, and in reality the Android OS had now been rootedallowing us to use the su” command to become root and perform actions with UID=0, as seen in Figure 8. 

Figure 8: Booting modified boot.img and executing whoami as Root 

Impact Scenarios 

As we just demonstrated, the ability to bypass the Android Verified Boot process can lead to the Android OS being compromised by an attacker with physical accessA worst-case scenario for such an attack vector might involve a malicious agent booting the Peloton with a modified image to gain elevated privileges and then leveraging those privileges to establish a reverse shell, granting the attacker unfettered root access on the bike remotely. Since the attacker never has to unlock the device to boot a modified image, there would be no trace of any access they achieved on the device. This sort of attack could be effectively delivered via the supply chain process. A malicious actor could tamper with the product at any point from construction to warehouse to delivery, installing a backdoor into the Android tablet without any way the end user could know. Another scenario could be that an attacker could simply walk up to one of these devices that is installed in a gym or a fitness room and perform the same attack, gaining root access on these devices for later use. The Pelobuddy interactive map in figure 9 below could help an attacker find public bikes to attack. 

Figure’s interactive map to help locate public Peloton exercise equipment. 

Once an attacker has root, they could make their presence permanent by modifying the OS in a rootkit fashion, removing any need for the attacker to repeat this step. Another risk is that an attacker could modify the system to put themselves in a man-in-the-middle position and sniff all network traffic, even SSL encrypted traffic, using a technique called SSL unpinning, which requires root privileges to hook calls to internal encryption functionality. Intercepting and decrypting network traffic in this fashion could lead to users personal data being compromised. Lastly, the Peloton Bike+ also has a camera and a microphone installed. Having remote access with root permissions on the Android tablet would allow an attacker to monitor these devices and is demoed in the impact video below. 

Disclosure Timeline and Patch 

Given the simplicity and criticality of the flaw, we decided to disclose to Peloton even as we continue to audit the device for remote vulnerabilities. We sent our vendor disclosure with full details on March 2, 2021 – shortly after, Peloton confirmed the issue and subsequently released a fix for it in software version “PTX14A-290”. The patched image no longer allows for the “boot” command to work on a user build, mitigating this vulnerability entirelyThe Peloton vulnerability disclosure process was smooth, and the team were receptive and responsive with all communications. Further conversations with Peloton confirmed that this vulnerability is also present on Peloton Tread exercise equipment; however, the scope of our research was confined to the Bike+.

Peloton’s Head of Global Information Security, Adrian Stone, shared the following “this vulnerability reported by McAfee would require direct, physical access to a Peloton Bike+ or Tread. Like with any connected device in the home, if an attacker is able to gain physical access to it, additional physical controls and safeguards become increasingly important. To keep our Members safe, we acted quickly and in coordination with McAfee. We pushed a mandatory update in early June and every device with the update installed is protected from this issue.”

We are continuing to investigate the Peloton Bike+, so make sure you stay up to date on McAfee’s ATR blogs for any future discoveries. 

The post A New Program for Your Peloton – Whether You Like It or Not appeared first on McAfee Blogs.

Netop Vision Pro – Distance Learning Software is 20/20 in Hindsight

22 March 2021 at 04:01

The McAfee Labs Advanced Threat Research team is committed to uncovering security issues in both software and hardware to help developers provide safer products for businesses and consumers. We recently investigated software installed on computers used in K-12 school districts. The focus of this blog is on Netop Vision Pro produced by Netop. Our research into this software led to the discovery of four previously unreported critical issues, identified by CVE-2021-27192, CVE-2021-27193, CVE-2021-27194 and CVE-2021-27195. These findings allow for elevation of privileges and ultimately remote code execution, which could be used by a malicious attacker, within the same network, to gain full control over students’ computers. We reported this research to Netop on December 11, 2020 and we were thrilled that Netop was able to deliver an updated version in February of 2021, effectively patching many of the critical vulnerabilities.

Netop Vision Pro is a student monitoring system for teachers to facilitate student learning while using school computers. Netop Vision Pro allows teachers to perform tasks remotely on the students’ computers, such as locking their computers, blocking web access, remotely controlling their desktops, running applications, and sharing documents. Netop Vision Pro is mainly used to manage a classroom or a computer lab in a K-12 environment and is not primarily targeted for eLearning or personal devices. In other words, the Netop Vision Pro Software should never be accessible from the internet in the standard configuration. However, as a result of these abnormal times, computers are being loaned to students to continue distance learning, resulting in schooling software being connected to a wide array of networks increasing the attack surface.

Initial Recon

Netop provides all software as a free trial on its website, which makes it easy for anyone to download and analyze it. Within a few minutes of downloading the software, we were able to have it configured and running without any complications.

We began by setting up the Netop software in a normal configuration and environment. We placed four virtual machines on a local network; three were set up as students and one was set up as a teacher. The three student machines were configured with non-administrator accounts in our attempt to emulate a normal installation. The teacher first creates a “classroom” which then can choose which student PCs should connect. The teacher has full control and gets to choose which “classroom” the student connects to without the student’s input. Once a classroom has been setup, the teacher can start a class which kicks off the session by pinging each student to connect to the classroom. The students have no input if they want to connect or not as it is enforced by the teacher. Once the students have connected to the classroom the teacher can perform a handful of actions to the entire class or individual students.

During this setup we also took note of the permission levels of each component. The student installation needs to be tamperproof and persistent to prevent students from disabling the service. This is achieved by installing the Netop agent as a system service that is automatically started at boot. The teacher install executes as a normal user and does not start at boot. This difference in execution context and start up behavior led us to target the student installs, as an attacker would have a higher chance of gaining elevated system permissions if it was compromised. Additionally, the ratio of students to teachers in a normal school environment would ensure any vulnerabilities found on the student machines would be wider spread.

With the initial install complete, we took a network capture on the local network and took note of the traffic between the teacher and student. An overview of the first few network packets can been seen in Figure 1 below and how the teacher, student transaction begins.

Figure 1: Captured network traffic between teacher and student

Our first observation, now classified as CVE-2021-27194, was that all network traffic was unencrypted with no option to turn encryption on during configuration. We noticed that even information normally considered sensitive, such as Windows credentials (Figure 2) and screenshots (Figure 4), were all sent in plaintext. Windows credentials were observed on the network when a teacher would issue a “Log on” command to the student. This could be used by the teacher or admin to install software or simply help a student log in.

Figure 2: Windows credentials passed in plaintext

Additionally, we observed interesting default behavior where a student connecting to a classroom immediately began to send screen captures to the classroom’s teacher. This allows the teacher to monitor all the students in real time, as shown in Figure 3.

Figure 3: Teacher viewing all student machines via screenshots

Since there is no encryption, these images were sent in the clear. Anyone on the local network could eavesdrop on these images and view the contents of the students’ screens remotely. A new screenshot was sent every few seconds, providing the teacher and any eavesdroppers a near-real time stream of each student’s computer. To capture and view these images, all we had to do was set our network card to promiscuous mode ( and use a tool like Driftnet ( These two steps allowed us to capture the images passed over the network and view every student screen while they were connected to a classroom. The image in Figure 4 is showing a screenshot captured from Driftnet. This led us to file our first vulnerability disclosed as CVE-2021-27194, referencing “CWE-319: Cleartext Transmission of Sensitive Information” for this finding. As pointed out earlier, the teacher and the student clients will communicate directly over the local network. The only way an eavesdropper could access the unencrypted data would be by sniffing the traffic on the same local network as the students.

Figure 4: Image of student’s desktop captured from Driftnet over the network

Fuzzing the Broadcast Messages

With the goal of remote code execution on the students’ computers, we began to dissect the first network packet, which the teacher sends to the students, telling them to connect to the classroom. This was a UDP message sent from the teacher to all the students and can be seen in Figure 5.

Figure 5: Wireshark capture of teacher’s UDP message

The purpose of this packet is to let the student client software know where to find the teacher computer on the network. Because this UDP message is sent to all students in a broadcast style and requires no handshake or setup like TCP, this was a good place to start poking at.

We created a custom Scapy layer ( (Figure 6) from the UDP message seen in Figure 5 to begin dissecting each field and crafting our own packets. After a few days of fuzzing with UDP packets, we were able to identify two things. First, we observed a lack of length checks on strings and second, random values sent by the fuzzer were being written directly to the Windows registry. The effect of these tests can easily be seen in Figure 7.

Figure 6: UDP broadcast message from teacher

Even with these malformed entries in the registry (Figure 7) we never observed the application crashing or responding unexpectedly. This means that even though the application wasn’t handling our mutated packet properly, we never overwrote anything of importance or crossed a string buffer boundary.

Figure 7: Un-sanitized characters being written to the Registry

To go further we needed to send the next few packets that we observed from our network capture (Figure 8). After the first UDP message, all subsequent packets were TCP. The TCP messages would negotiate a connection between the student and the teacher and would keep the socket open for the duration of the classroom connection. This TCP negotiation exchange was a transfer of 11 packets, which we will call the handshake.

Figure 8: Wireshark capture of a teacher starting class

Reversing the Network Protocol

To respond appropriately to the TCP connection request, we needed to emulate how a valid teacher would respond to the handshake; otherwise, the student would drop the connection. We began reverse engineering the TCP network traffic and attempted to emulate actual “teacher” traffic. After capturing a handful of packets, the payloads started to conform to roughly the same format. Each started with the size of the packet and the string “T125”. There were three packets in the handshake that contained fields that were changing between each classroom connection. In total, four changing fields were identified.

The first field was the session_id, which we identified in IDA and is shown in the UDP packet from Figure 6. From our fuzzing exercise with the UDP packet, we learned if the same session_id was reused multiple times, the student would still respond normally, even though the actual network traffic we captured would often have a unique session_id.

This left us three remaining dynamic fields which we identified as a teacher token, student token, and a unique unknown DWORD (8 bytes). We identified two of these fields by setting up multiple classrooms with different teacher and student computers and monitoring these values. The teacher token was static and unique to each teacher. We discovered the same was true with the student token. This left us with the unique DWORD field that was dynamic in each handshake. This last field at first seemed random but was always in the same relative range. We labeled this as “Token3” for much of our research, as seen in Figure 9 below.

Figure 9: Python script output identifying “Token3”

Eventually, while using WinDbg to perform dynamic analysis, the value of Token3 started to look familiar. We noticed it matched the range of memory being allocated for the heap. This can be seen in Figure 10.

Figure 10: WinDbg address space analysis from a student PC

By combining our previous understanding of the UDP broadcast traffic with our ability to respond appropriately to the TCP packets with dynamic fields, we were able to successfully emulate a teacher’s workstation. We demonstrated this by modifying our Python script with this new information and sending a request to connect with the student. When a student connects to a teacher it displays a message indicating a successful connection has been made. Below are two images showing a teacher connecting (Figure 11) and our Python script connecting (Figure 12). Purely for demonstration purposes, we have named our attack machine “hacker”, and our classroom “hacker-room.”

Figure 11: Emulation of a teacher successful

Figure 12: Emulated teacher connection from Python script

To understand the process of reverse engineering the network traffic in more detail, McAfee researchers Douglas McKee and Ismael Valenzuela have released an in-depth talk on how to hack proprietary protocols like the one used by Netop. Their webinar goes into far more detail than this blog and can be viewed here.

Replaying a Command Action

Since we have successfully emulated a teacher’s connection using Python, for clarity we will refer to ourselves as the attacker and a legitimate connection made through Netop as the teacher.

Next, we began to look at some of the actions that teachers can perform and how we could take advantage of them. One of the actions that a teacher can perform is starting applications on the remote students’ PCs. In the teacher suite, the teacher is prompted with the familiar Windows Run prompt, and any applications or commands set to run are executed on the student machines (Figure 13).

Figure 13: The teacher “Run Application” prompt

Looking at the network traffic (shown in Figure 14), we were hoping to find a field in the packet that could allow us to deviate from what was possible using the teacher client. As we mentioned earlier, everything is in plaintext, making it quite easy to identify which packets were being sent to execute applications on the remote systems by searching within Wireshark.

Figure 14: Run “calc” packet

Before we started to modify the packet that runs applications on the student machines, we first wanted to see if we could replay this traffic successfully. As you can see in the video below, our Python script was able to run PowerShell followed by Windows Calculator on each of the student endpoints. This is showcasing that even valid teacher actions can still be useful to attackers.

The ability for an attacker to emulate a teacher and execute arbitrary commands on the students’ machines brings us to our second CVE. CVE-2021-27195 was filed for “CWE-863: Incorrect Authorization” since we were able to replay modified local network traffic.

When the teacher sends a command to the student, the client would drop privileges to that of the logged-in student and not keep the original System privileges. This meant that if an attacker wanted unrestricted access to the remote system, they could not simply replay normal traffic, but instead would have to modify each field in the traffic and observe the results.

In an attempt to find a way around the privilege reduction during command execution, we continued fuzzing all fields located within the “run command” packet. This proved unsuccessful as we were unable to find a packet structure that would prevent the command from lowering privileges. This required a deeper dive into the code in handling the remote command execution processed on the student endpoint. By tracing the execution path within IDA, we discovered there was in fact a path that allows remote commands to execute without dropping privileges, but it required a special case, as shown in Figure 15.

Figure 15: IDA graph view showing alternate paths of code execution

Figure 16: Zoomed in image of the ShellExecute code path

The code path that bypasses the privilege reduction and goes directly to “ShellExecute” was checking a variable that had its value set during startup. We were not able to find any other code paths that updated this value after the software started. Our theory is this value may be used during installation or uninstallation, but we were not able to legitimately force execution to the “ShellExecute” path.

This code path to “ShellExecute” made us wonder if there were other similar branches like this that could be reached. We began searching the disassembled code in IDA for calls not wrapped with code resulting in lower privileges. We found four cases where the privileges were not reduced, however none of them were accessible over the network. Regardless, they still could potentially be useful, so we investigated each. The first one was used when opening Internet Explorer (IE) with a prefilled URL. This turned out to be related to the support system. Examining the user interface on the student machine, we discovered a “Technical Support” button which was found in the Netop “about” menu.

When the user clicks on the support button, it opens IE directly into a support web form. The issue, however, is privileges are never dropped, resulting in the IE process being run as System because the Netop student client is also run as System. This can be seen in Figure 11. We filed this issue as our third CVE, CVE-2021-27192 referencing “CWE-269: Incorrect Privilege Assignment”.

Figure 17: Internet Explorer running as System

There are a handful of well-documented ways to get a local elevation of privilege (LPE) using only the mouse when the user has access to an application running with higher privileges. We used an old technique which uses the “Save as” button to navigate to the folder where cmd.exe is located and execute it. The resulting CMD process inherits the System privileges of the parent process, giving the user a System-level shell.

While this LPE was exciting, we still wanted to find something with a remote attack vector and utilize our Python script to emulate teacher traffic. We decided to take a deeper dive into the network traffic to see what we could find. Simulating an attacker, we successfully emulated the following:

  • Remote CMD execution
  • Screen blank the student
  • Restart Netop
  • Shutdown the computer
  • Block web access to individual websites
  • Unlock the Netop properties (on student computer)

During the emulation of all the above actions we performed some rudimentary fuzzing on various fields of each and discovered six crashes which caused the Netop student install to crash and restart. We were able to find two execution violations, two read violations, one write exception, and one kernel exception. After investigation, we determined these crashes were not easily exploitable and therefore a lower priority for deeper investigation. Regardless, we reported them to Netop along with all other findings.

Exploring Plugins

Netop Vision Pro comes with a handful of plugins installed by default, which are used to separate different functionality from the main Netop executable. For example, to enable the ability for the teacher and student to instant message (IM) each other, the MChat.exe plugin is used. With a similar paradigm to the main executable, the students should not be able to stop these plugins, so they too run as System, making them worth exploring.

Mimicking our previous approach, we started to look for “ShellExecute” calls within the plugins and eventually discovered three more privilege escalations, each of which were conducted in a comparable way using only the mouse and bypassing restrictive file filters within the “Save as” windows. The MChat.exe, SSView.exe (Screen Shot Viewer), and the About page’s “System Information” windows all had a similar “Save as” button, each resulting in simple LPEs with no code or exploit required. We added each of these plugins under the affected versions field on our third CVE, CVE-2021-27192, mentioned above.

We were still searching for a method to achieve remote code execution and none of the “ShellExecute” calls used for the LPEs were accessible over the network. We started to narrow down the plugins that pass user supplied data over the network. This directed our attention back to the MChat plugin. As part of our initial recon for research projects, we reviewed change logs looking for any relevant security changes. During this review we noted an interesting log pertaining to the MChat client as seen in Figure 13.


Figure 18: Change log from

The Chat function runs as System, like all the plugins, and can send text or files to the remote student computer. An attacker can always use this functionality to their advantage by either overwriting existing files or enticing a victim to click on a dropped executable. Investigating how the chat function works and specifically how files are sent, we discovered that the files are pushed to the student computers without any user interaction from the student. Any files pushed by a teacher are stored in a “work directory”, which the student can open from the IM window. Prior to the latest release it would have been opened as System; this was fixed as referenced in Figure 18. Delving deeper into the functionality of the chat application, we found that the teacher also has the ability to read files in the student’s “work directory” and delete files within it. Due to our findings demonstrated with CVE-2021-27195, we can leverage our emulation code as an attacker to write, read, and delete files within this “work directory” from a remote attack vector on the same local network. This ability to read and write files accounted for the last CVE that we filed, CVE-2021-27193 referencing “CWE-276: Incorrect Default Permissions,” with the overall highest CVSS score of 9.5.

In order to determine if the MChat plugin would potentially give us System-level access, we needed to investigate if the plugin’s file operations were restricted to the student’s permissions or if the plugin inherited the System privileges from the running context. Examining the disassembled code of the MChat plugin, as displayed in Figure 14, we learned that all file actions on the student computer are executed with System privileges. Only after the file operation finishes will the permissions be set to allow access for everyone, essentially the effect of using the Linux “chmod 777” command, to make the files universally read/writable.

Figure 19: IDA screenshot of MChat file operations changing access to everyone

To validate this, we created several test files using an admin account and restricted the permissions to disallow the student from modifying or reading the test files. We proceeded to load the teacher suite, and through an MChat session confirmed we were able to read, write, and delete these files. This was an exciting discovery; however, if the attacker is limited to the predetermined “work directory” they would be limited in the effect they could have on the remote target. To investigate if we could change the “work directory” we began digging around in the teacher suite. Hidden in a few layers of menus (Figure 20) we found that a teacher can indeed set the remote student’s “work directory” and update this remotely. Knowing we can easily emulate any teacher’s command means that we could modify the “work directory” anywhere on the student system. Based on this, an attacker leveraging this flaw could have System access to modify any file on the remote PC.

Figure 20: Changing the remote student path from a teacher’s client

Reversing MChat Network Traffic

Now that we knew that the teacher could overwrite any file on the system, including system executables, we wanted to automate this attack and add it to our Python script. By automating this we want to showcase how attackers can use issues like this to create tools and scripts that have real world impacts. For a chat session to begin, we had to initiate the 11-packet handshake we previously discussed. Once the student connected to our attack machine, we needed to send a request to start a chat session with the target student. This request would make the student respond using TCP, yet this time, on a separate port, initiating an MChat seven-packet handshake. This required us to reverse engineer this new handshake format in a similar approach as described earlier. Unlike the first handshake, the MChat handshake had a single unique identifier for each session, and after testing, it was determined that the ID could be hardcoded with a static value without any negative effects.

Finally, we wanted to overwrite a file that we could ensure would be executed with System privileges. With the successful MChat handshake complete we needed to send a packet that would change the “work directory” to that of our choosing. Figure 21 shows the packet as a Scapy layer used to change the work directory on the student’s PC. The Netop plugin directory was a perfect target directory to change to since anything executed from this directory would be executed as System.

Figure 21: Change working directory on the student PC

The last step in gaining System-level execution was to overwrite and execute one of the plugins with a “malicious” binary. Through testing we discovered that if the file already exists in the same directory, the chat application is smart enough to not overwrite it, but instead adds a number to the filename. This is not what we wanted since the original plugin would get executed instead of our “malicious” one. This meant that we had to also reverse engineer a packet containing commands that are used to delete files. The Scapy layer used to delete a file and save a new one is shown in Figure 22.

Figure 22: Python Scapy layers to “delete” (MChatPktDeleteFile)  and “write” (MChatPkt6) files

With these Scapy layers we were able to replace the target plugin with a binary of our choosing, keeping the same name as the original plugin. We chose the “SSView.exe” plugin, which is a plugin used to show screenshots on the student’s computer. To help visualize this entire process please reference Figure 23.

Figure 23: An attack flow using the MChat plugin to overwrite an executable

Now that the SSView.exe plugin has been overwritten, triggering this plugin will execute our attacker-supplied code. This execution will inherit the Netop System privileges, and all can be conducted from an unauthenticated remote attack vector.


It is not hard to imagine a scenario where a culmination of these issues can lead to several negative outcomes. The largest impact being remote code execution of arbitrary code with System privileges from any device on the local network. This scenario has the potential to be wormable, meaning that the arbitrary binary that we run could be designed to seek out other devices and further the spread. In addition, if the “Open Enrollment” option for a classroom is configured, the Netop Vision Pro student client broadcasts its presence on the network every few seconds. This can be used to an attacker’s advantage to determine the IP addresses of all the students connected on the local network. As seen in Figure 24, our Python script sniffed for student broadcast messages for 5 seconds and found all three student computers on the same network. Because these broadcast messages are sent out to the entire local network, this could very well scale to an entire school system.

Figure 24: Finding all students on the local network.

With a list of computers running the student software, an attacker can then issue commands to each one individually to run arbitrary code with System privileges. In the context of hybrid and e-learning it is important to remember that this software on the student’s computer doesn’t get turned off. Because it is always running, even when not in use, this software assumes every network the device connects to could have a teacher on it and begins broadcasting its presence. An attacker doesn’t have to compromise the school network; all they need is to find any network where this software is accessible, such as a library, coffee shop, or home network. It doesn’t matter where one of these student’s PCs gets compromised as a well-designed malware could lay dormant and scan each network the infected PC connects to, until it finds other vulnerable instances of Netop Vision Pro to further propagate the infection.

Once these machines have been compromised the remote attacker has full control of the system since they inherit the System privileges. Nothing at this point could stop an attacker running as System from accessing any files, terminating any process, or reaping havoc on the compromised machine. To elaborate on the effects of these issues we can propose a few scenarios. An attacker could use the discoverability of these machines to deploy ransomware to all the school computers on the network, bringing the school or entire school district to a standstill. A stealthier attacker could silently install keylogging software and monitor screenshots of the students which could lead to social media or financial accounts being compromised. Lastly, an attacker could monitor webcams of the students, bridging the gap from compromised software to the physical realm. As a proof of concept, the video below will show how an attacker can put CVE-2021-27195 and CVE-2021-27193 together to find, exploit, and monitor the webcams of each computer running Netop Vision Pro.

Secure adaptation of software is much easier to achieve when security is baked in from the beginning, rather than an afterthought. It is easy to recognize when software is built for “safe” environments. While Netop Vision Pro was never intended to be internet-facing or be brought off a managed school network, it is still important to implement basic security features like encryption. While designing software one should not assume what will be commonplace in the future. For instance, when this software was originally developed the concept of remote learning or hybrid learning was a far-out idea but now seems like it will be a norm. When security decisions are integrated from inception, software can adapt to new environments while keeping users better protected from future threats.

Disclosure and Recommended Mitigations

We disclosed all these findings to Netop on December 11, 2020 and heard back from them shortly after. Our disclosure included recommendations for implementing encryption of all network traffic, adding authentication, and verification of teachers to students, and more precise packet parsing filters. In Netop Vision Pro 9.7.2, released in late February, Netop has fixed the local privilege escalations, encrypted formerly plaintext Windows credentials, and mitigated the arbitrary read/writes on the remote filesystem within the MChat client. The local privilege escalations were fixed by running all plugins as the student and no longer as System. This way, the “Save as” buttons are limited to the student’s account. The Windows credentials are now encrypted using RC4 before being sent over the network, preventing eavesdroppers from gathering account credentials. Lastly, since all the plugins are running as the student, the MChat client can no longer delete and replace system executables which successfully mitigates the attack shown in the impact section. The network traffic is still unencrypted, including the screenshots of the student computers but Netop has assured us it is working on implementing encryption on all network traffic for a future update. We’d like to recognize Netop’s outstanding response and rapid development and release of a more secure software version and encourage industry vendors to take note of this as a standard for responding to responsible disclosures from industry researchers.

The post Netop Vision Pro – Distance Learning Software is 20/20 in Hindsight appeared first on McAfee Blogs.

My Adventures Hacking the iParcelBox

18 June 2020 at 07:01

In 2019, McAfee Advanced Threat Research (ATR) disclosed a vulnerability in a product called BoxLock. Sometime after this, the CEO of iParcelBox, a U.K. company, reached out to us and offered to send a few of their products to test. While this isn’t the typical M.O. for our research we applaud the company for being proactive in their security efforts and so, as the team over at iParcelBox were kind enough to get everything shipped over to us, we decided to take a look.

The iParcelBox is a large steel box that package couriers, neighbors, etc. can access to retrieve or deliver items securely without needing to enter your home. The iParcelBox has a single button on it that when pushed will notify the owner that someone wants to place an object inside. The owner will get an “open” request push notification on their mobile device, which they can either accept or deny.

The iParcelBox (Photo Credit:


The first thing we noticed about this device is the simplicity of it. In the mindset of an attacker, we are always looking at a wide variety of attack vectors. This device only has three external vectors: remote cloud APIs, WIFI, and a single physical button.

iParcelBox Delivery Button (Photo Credit:

During setup the iParcelBox creates a WIFI access point for the mobile application to connect with and send setup information. Each iParcelBox has a unique randomly generated 16-character WiFi password that makes brute forcing the WPA2 key out of the question; additionally, this Access Point is only available when the iParcelBox is in setup mode. The iParcelBox can be placed into setup mode by holding the button down but it will warn the owner via a notification and will only remain in setup mode for a few minutes before returning to normal operation.

iParcelBox Random WiFi Access Point Password (16 Characters)

Since we have the WiFi password for the iParcelBox in our lab, we connected to the device to see what we could glean from the webserver. The device was only listening on port 443, meaning that the traffic between the application and iParcelBox was most likely encrypted, which we later verified. This pointed us to the Android app to try to decipher what type of messages were being sent to the iParcelBox during setup.

iParcelBox Port Scan

Using dex2jar we were able to disassemble the APK file and look at the code within the app. We noticed quickly that the iParcelBox was using MQTT (MQ Telemetry Transport) for passing messages back and forth between the iParcelBox and the cloud. MQTT is a publish/subscribe message protocol where devices can subscribe to “topics” and receive messages. A simple description can be found here: (

Dex2Jar Command

A typical next step is to retrieve the firmware for the device, so we started to look through the disassembled APK code for interesting URLs. While we didn’t find any direct firmware links, we were able to find some useful information.

Disassembled Code pulled from APK

The code snipped above shows a few interesting points, including the string “” as well as the line with “app” and “TBpwkjoU68M”. We thought that this could be credentials for an app user passed to the iParcelBox during setup; however, we’ll come back to this later. The URL didn’t resolve on the internet, but when connecting to the iParcelBox access point and doing a Dig query we were able to see that it resolves to the iParcelBox.

DNS Lookup of

Nothing from the Android app or the webserver on the device popped out to us so we decided to look deeper. One of the most common ways that information about targets can be gathered is by looking through user forums and seeing if there are others trying to tweak and modify the device. Often with IOT devices, home automation forums have numerous examples of API usage as well as user scripts to interact with such devices. We wanted to see if there was anything like this for the iParcelBox. Our initial search for iParcelBox came up empty, other than some marketing content, but when the search was changed to iParcelBox API, we noticed a few interesting posts.

Google Search for “iparcelbox api”

We could see that even on the first page there are a few bug reports and a couple of user forums for “Mongoose-OS”. After going to the Mongoose-OS forums we could clearly see that one user was a part of the iParcelBox development team. This gave us some insight that the device was running Mongoose-OS on an ESP32 Development board, which is important since an ESP32 device can be flashed with many other types of code. We started to track the user’s posts and were able to discover extensive information about the device and the development decisions throughout the building process. Most importantly this served as a shortcut to many of the remaining analysis techniques.

As mentioned earlier, a high priority is to try to gain access to the device’s firmware by either pulling it from the device directly or by downloading it from the vendor’s site. Pulling firmware is slightly more tedious since you must often solder wires to the flash chip or remove the chip all-together to interface with the flash. Before we began to attempt to pull the firmware from the ESP32, we noticed another post within the forums that mentioned that the flash memory on the device was encrypted.

Post describing flash encryption

With this knowledge, we skipped soldering wires to the ESP32 and didn’t even try to pull the firmware manually since it would have proven difficult to get anything off it. This also gave us insight into the provisioning process and how each device is set up. With this knowledge we started to look for how the OTA updates are downloaded.

Searching around a little longer we were able to find a file upload of a large log file containing what seemed like the iParcelBox boot procedure. Searching through the log we found some highly sensitive data.

Admin Credentials and gh-token from boot log

In the snippet above you can see that the admin credentials are passed as well as the GitHub token. Needless to say, this isn’t good practice, we will see if we can use that later. But in this log, we also found a firmware URL.

Firmware URL from boot log

However, the URL required a username and password. .htaccess

We found this forum post where “.htaccess” is set up to prevent unintended access to the firmware download.

.htaccess post

The admin password found earlier didn’t authenticate, so we wanted to get the logs off the device to see if these were old credentials and if we could print the new credentials out to UART.

The internals of the iParcelBox (TX and RX highlighted in red)

The ESP32 RX and TX pins are mapped to the USB-C connection, but if you look at the circuit there is no FTDI (Future Technology Devices International) chip to do processing, so this is just raw serial. We decided to just solder to the vias (Vertical Interconnect Access) highlighted in red above, but still no data was transferred.

Then we started to search those overly helpful forum postings again, and quickly found the reason.

Disable UART

This at least verified that it wasn’t something that we set up incorrectly, but rather that logging was simply disabled over UART.

Method #1 – RPC

From our recon work we pretty much settled on the fact that we were not going to get into the iParcelBox easily from a physical standpoint and decided to switch a network approach. We knew that during setup the iParcelBox creates a wireless AP and that we can connect to it. Armed with our knowledge from the forums we decided to revisit the web server on the iParcelBox. We began by sending some “MOS” (Mongoose-OS) control commands to see what stuck.

Setup instructions for Mongoose-OS can be found here. Instead of installing directly to the OS we did it in Docker for portability.

Docker file used to create mos

Referencing the forums provided several examples of how to use the mos command.

Docker mos commands

The first command returned a promising message that we just need to supply credentials. Remember when we found the boot log earlier? Yep, the admin credentials were posted online, and they actually work!

At this point we had full effective root access to the iParcelBox including access to all the files, JavaScript code, and even more importantly, the AWS certificate and private key.

With the files extracted from the device we noticed that the developers at iParcelBox implemented an Access Control List (ACL). For an IOT device this is uncommon but a good practice.

ACL showing users permissions

The credentials we found earlier in the disassembled Android APK with the username “app” were RPC credentials but with limited permissions to only run Sys.GetInfo, Wifi.Scan, Wifi.PortalSave and Sys.Reboot. Nothing too interesting can be done with those credentials, so for the rest of this method we will stick with the “admin” credentials.

Now that we have the credentials, certificates, and private keys we wanted to try to pivot to other devices. During setup we noticed that the MAC address was labeled “TopicID.”

Setup process linking MAC Address to the TopicID

As we determined earlier, the iParcelBox uses MQTT for brokering the communication between the device, cloud, and mobile application. We were interested to find out if there were any authentication barriers in place, or if all you need is the MAC address of the device to initiate commands remotely.

Since we essentially had root access, enabling logging was a logical next step so we could see what was happening on the device. From one of the Mongoose-OS forums posts we saw that you can enable UDP logging to a local device by changing the configuration on the iParcelBox.

How to enable UDP logging post

We provisioned the iParcelBox, then held the button down until we entered setup mode (where the AP was available), thus reenabling RPC calls. Then we set the “udp_log_addr” to our local machine.

Reenabling Logging on iParcelBox

Now we have logs and much more information. We wanted to test if we could access the MQTT broker and modify other iParcelBoxes. In the logs we were able to validate that the MQTT broker was setup on AWS IOT and was using the certificate and keys that we pulled earlier. We found some Python examples of connecting to the AWS MQTT broker ( but it assumed it knows the full topic path (e.g. topic_id/command/unlock).

UDP Log file

Parsing through the extracted logs from UDP, we were able to find the format for the “shadow/update” MQTT topic. However, when trying to subscribe to it with the Python script, it seemed to connect to the MQTT broker, but we couldn’t ever get any messages to send or receive. Our best guess is that it was limited to one subscribe per topic or that our code was broken.

We went searching for another way to control devices. This brought us back to the Mongoose-OS forum (seeing a pattern here?). We found this post explaining that the devices can run RPC commands over MQTT.


This would be better for an attacker than only MQTT access, since this gives full access to the device including certificates, keys, user configuration files, WIFI passwords, and more. We could also use RPC to write custom code or custom firmware at this point.  We found the official Mongoose-OS support for this here (, to which they even included an example with AWS IOT.

After plugging that into the “mos” command we were able to run all administrative RPC commands on the device that we pulled the keys from, but also any other device that we knew the MAC address of.

Running RPC commands on multiple ATR lab devices

From looking at the two iParcelBoxes that were sent to us, the MAC addresses are only slightly different and strongly suggest that they are probably generated incrementally.

  • 30AEA4C59D30
  • 30AEA4C59D6C

Theoretically, with the MAC addresses incremental we could have just written a simple script to iterate through each of the iParcelBoxes’ MAC addresses, found any iParcelBox connected to the internet, and controlled or modified them in any way we wanted. However, the most common attack would likely be a more targeted one, where the attacker was looking to steal a package or even a victim’s home WiFi credentials. An attacker could do a simple network scan to find the MAC address of the target iParcelbox using a tool like “airodump-ng”. Then, after the attacker knows the target MAC address, they could use the admin credentials to initiate a “mos” command over MQTT and execute a “GPIO.Toggle” command directed at the GPIO (General Purpose Input Output) pin that controls the locking mechanism on the iParcelBox. A toggle will invert the state, so if the iParcelBox is locked, a GPIO toggle will unlock the box. If the attacker had other motives, they could also initiate a config dump to gain access to the WiFi credentials to which the iParcelBox is connected.

Scanning for iParcelBoxes and Controlling them with RPC

Method #2 – AWS Misconfiguration

While writing this blog we wanted to double check that SSL pinning was done properly. After we saw this post during our recon, we assumed it was pinning a certificate. We set up an Android with a certificate unpinner using Frida.  With the unpinner installed and working we were able to decrypt traffic between the application and the AWS servers, but it failed to decrypt the data from application to the iParcelBox. Please follow this technique if you’d like to learn how you can unpin certificates on Android devices.

Next, we reran the iParcelBox application without the Frida SSL Unpinner, which returned the same AWS server transactions, meaning that pinning wasn’t enabled. We browsed through some of the captures and found some interesting requests.

Cognito Credential SSL Network Capture

The “credentials” in the capture immediately piqued our interest. They are returned by a service called “Cognito”, which is an AWS service allowing apps and users to access resources within the AWS ecosystem for short periods of time and with limited access to private resources.

AWS Cognito example (Photo Credit:

When an application wants to access an AWS service, it can ask for temporary credentials for the specific task. If the permissions are configured correctly, the credentials issued by the Cognito service will allow the application or user to complete that one task and deny all other uses of the credentials to other services.

To use these credentials, we needed the AWS-CLI interface. Thankfully, Amazon even has a Docker image for AWS-CLI which made things much easier for us. We just saved the credentials returned from the Cognito service inside of a “~/.aws” folder. Then we checked what role these credentials were given.

AWS-CLI docker command

The credentials captured from the Android application were given the “AppAuth_Role”. To find out what the “AppAuth_Role” had access to we then ran a cloud service enumeration using the credentials; the scripts can be found here ( and are provided by the NotSoSecure team. The AWS script didn’t find any significant security holes and showed that the credentials were properly secured. However, looking at the next few network captures we noticed that these credentials were being used to access the DynamoDB database.

Checking if the user is subscribed to the Premium service

Getting the owner’s devices

After reading through some of the DynamoDB documentation we were able to craft database queries.

DynamoDB Query

Because the “primary key” for the database is the “DeviceID” which we know is just the MAC address of the iParcelBox, we can then modify this query and get any other device’s database entries. While we didn’t test this for ethical reasons, we suspect that we could have used this information to gain access to the MQTT services. We also did not attempt to write to the database since this was a live production database and we didn’t want to corrupt any data.

We investigated the Android application attempting to trigger some more database interactions to see what other queries were being sent, but were limited to the following:

  • Accounts – Shows premium subscription info
  • Owners – Shows devices and guests of each iParcelBox
  • Users – Used to save owners of each iParcelBox (only during setup)

With our self-imposed database write restrictions, none of these tables really helped us anyway. That is when we began looking at the disassembled code of the Android app for more clues. Since we now knew the table names, we searched for “ClientID”, which turned up the Java file “DBConstants.class.”

Constants file from APK

This constants file gave us information that there are more database tables and fields, even though we never saw them in the network traffic. The “TABLE_DEVICES_PASSWORD” caught our eyes from the “iParcelBox_devices” table.

We tested the “AppAuth_Role” credentials on this table as well, which was accepted.

Requesting information from the iParcelBox_devices table

We were able to get the device password and serial number all from the MAC address. Recall the “iParcelBox Setup Information” image at the beginning of the blog and how it mentions that you should keep this information safe. The reason that this information should be kept safe is that you can become the owner of the iParcelBox if you know the MAC address, serial number, and password even without the QR code thanks to the “Add Manually” button.

“Add manually” option during setup

With this information an attacker could register for a new iParcelBox account, login to the application, capture the Cognito credentials, begin the “setup” process, click “Add Manually” and then enter all the required information returned from the database to gain full control over any iParcelBox. This could all take place from simply knowing the MAC address since the “AppAuth_Role” can read any database entry.

Required Information to set up the iParcelBox

Lessons Learned

This project took a turn from a classic hardware/IOT device research project to an OSINT research topic very early on. It really goes to show that even simple mistakes with online data hygiene could expose key details to attackers allowing them to narrow down attack vectors or expose sensitive information like credentials.

Since this was a sponsored project from iParcelBox, we reported this to the company immediately. They promptly changed the admin password for every iParcelBox and asked the developers at Mongoose-OS to implement a change where one device’s AWS certificate and private key cannot control any other device. This was patched within 12 hours after our vendor disclosure, which puts iParcelBox in the top response time for a patch that we have ever seen. We have tested the patch and can no longer control other devices or use the old admin password to access the devices from within setup mode.

iParcelBox also fixed the Android application not pinning certificates properly and removed all direct calls to the DynamoDB. We were still able to decrypt some traffic using the Frida SSL unpinner, but the application would freeze, which we believe is due to the MQTT broker not accepting a custom certificate. The DynamoDB queries are now wrapped in API calls which also check against the customer ID. This prevents someone from using their extracted Cognito credentials to obtain information from any device other than their own. Wrapping the database queries within API calls is an effective security fix as well, as the data can be parsed, verified, and sanitized all before committing to the database.

We wanted to give props to the team at iParcelBox for their focus on security throughout the development of this product. It is easy to see from the device and the forum posts that the developers have been trying to make this device secure from the start and have done it well. All non-essential features like UART and Bluetooth are turned off by default and a focus on data protection is clearly there as evidenced through the use of SSL and encryption of the flash memory. There are not many attack surfaces that an attacker could leverage from the device and is a great refreshment to see IOT devices heading this direction.

The post My Adventures Hacking the iParcelBox appeared first on McAfee Blogs.

We Be Jammin’ – Bypassing Chamberlain myQ Garage Doors

7 January 2020 at 05:01

The idea of controlling your garage door remotely and verifying that everything is secure at home, or having packages delivered directly into your garage is enticing for many people. The convenience that many of these IOT devices provide often persuades consumers away from thinking about the possible security concerns.

McAfee Advanced Threat Research recently investigated Chamberlain’s MyQ Hub, a “Universal” garage door automation platform. The way Chamberlain has made this device universal is via a Hub, which acts as a new garage door opener, similar to the one that you would have in your car. This allows the MyQ Hub to retrofit and work with a wide variety of garage doors.

We found that Chamberlain did a fairly good job of securing this device, which is typically uncommon for IOT devices. However, we discovered that there is a flaw in the way the MyQ Hub communicates with the remote sensor over radio frequencies.

From an attack perspective there are three main vectors that we began to look at: local network, remote access (API, or third-party integration), and RF communications between the sensor and the Hub. The first thing we attempted was to gain access to the device via the local network. A quick port scan of the device revealed that it was listening on port 80. When attempting to navigate to the device at port 80 it would redirect to start.html and return a 404 error. No other ports were open on the device.

The inside of the Chamberlain MyQ Hub

Disassembling the Hub revealed a small SOC (system on a chip) module that was handling the Wi-Fi and web communications and a secondary PIC microcontroller which was responsible for controlling the RF side of things for both the garage door and the remote door sensor. The MyQ Hub listed on FCC’s website also included a Bluetooth module that was not present on the two MyQ Hubs that we purchased.

The UART connection was disconnected or not enabled, but the JTAG connection worked to communicate directly with the main Wi-Fi module. With the JTAG connection we were able to dump the entire contents of the flash chip and debug the system unrestricted. The main Wi-Fi module was a Marvell microcontroller that was running a RTOS (Real Time Operating System), which acts much different than a normal Linux system. While it will still run predefined applications, RTOS’ usually don’t have a filesystem like traditional systems do.  We extracted the entire contents of the Marvell microprocessor, and were able to analyze the assembly and determine how the web server behaves.

From looking through the web server code we were able to identify how the device is setup through the local API as well as finding some interesting, albeit not very useful commands that we could send.

Local API commands

There were more URLs that we found to be accessible and some additional API paths, but nothing stood out as a good place to start an attack from. At this point we decided to investigate the other attack vectors.

We didn’t spend too much time looking into the third-party attack vector and remote API since it becomes sort of a gray area for researching. While we were testing with the /sys/mode API call we were able to put the device into a soft factory reset, where we were able to attempt to add the device to a different account. From capturing the SSL traffic on the mobile application, we were able to see that it was failing since the serial number was already registered to another account. We used a technique called SSL unpinning to decrypt traffic from the Android application; we’ll post a future blog explaining this process in greater detail. One thing that we wanted to try was to modify the Android app to send a different serial number. Since we don’t believe that the device ever cleared the original garage door information, we could have potentially opened the device from the new account. However, this is all speculation and was not tested because we didn’t want to access the remote API.

The last vector we looked at was RF. We began trying to break down the frequency modulation between the remote door sensor and the Hub. We originally thought it was some sort of FSK (frequency shift keying) where data is transmitted digitally. If the signal is in one frequency the corresponding bit is 0 and if the signal is shown on another frequency the bit is 1. This idea was thrown out since the MyQ remote sensor was using 3 different frequencies not just two.

Looking at the door sensor’s FCC filing we noticed a particularly helpful revision that they made.

OOK stands for “On OFF Keying” and is another method of encoding digital bits into RF. OOK will either be sending a signal (1) or not sending a signal (0). This means both the transmitter and receiver must be synchronized.

On Off Keying Graphical Representation

Here is the binary representation for the signal captured from the MyQ remote door sensor. This is a tightly zoomed-in window of the entire signal.

One full message captured, each color is a different frequency


We can observe the state transmission captured from all three frequencies and converted to hexadecimal. It’s easy to identify data patterns within the transmission, as represented in color above, but we were never able to crack it to arbitrarily transmit false states from our SDR (Software Defined Radio). We also noticed that the RF engineers at Chamberlain had security in mind not only by separating the signal into three separate frequencies, but also by implementing rolling codes. You may be familiar with the rolling code technique from things like your standard garage door opener or your car key fob. Rolling code devices prevent an attacker from directly capturing a signal and replaying it. This is prevented by the signal containing a unique identifier that is noted by the receiver and if the receiver ever sees that signal with the unique ID again it will ignore it.

The way attackers have overcome rolling code devices is by a method called “Roll Jam.” An attacker will jam the rolling code signal from the transmitter, blocking it from ever making it to the receiver, while simultaneously capturing the valid rolling code and storing it. This way the attacker now has an unused and valid rolling code that the receiver has never seen before. The caveat to this method is that normally the victim will notice that either the garage door or car didn’t unlock. A stealthier method to Roll Jam is always capturing the latest code and replaying the latest signal minus 1. This way the car or door will open but the attacker still owns the latest code for their use.

The MyQ also had a rolling code implementation that we were able to develop a variant of this technique against. We took the concept of jamming the original code from the receiver by transmitting a large amount of “noise” directly adjacent to the valid signal frequency. This causes the receiver in the MyQ Hub to overload and not hear the valid signal. However, with the precision of the SDR, we were able to ignore the noise that we are transmitting and store the signal. This was further complicated by the fact that there were three frequencies that we had to simultaneously listen for and jam. If you are interested in this FHSS (Frequency Hopping Spread Spectrum) Roll Jam technique, please read our white paper.

Within the research related to Chamberlain Garage Door Hub described in this blog, the only interference was to unlicensed spectrum radio frequency for the minimum period while the garage door hub was transmitting state signal, and there was no interference with any communications signal licensed or authorized under the Communications Act or FCC rules.

This technique worked, but since the remote sensor and the MyQ Hub always have the advantage in RF landscape, it was unreliable. The jamming aspect of the attack worked nicely; however, since we are outside of the garage and the remote sensor and the Hub are both located within the same garage, it is harder to jam and listen at the same time with the garage door and walls acting as barriers. With higher powered radios, frequency-tuned antennas, and disregard for FCC’s laws, the jamming of the remote sensor could take place at a much further distance than we were able to test in our lab environment.

A waterfall view of the remote sensor signal (red) and jamming (black)

With our jamming working reliably, we confirmed that when a user closes the garage door via the MyQ application, the remote sensor never responds with the closed signal because we are jamming it. The app will alert the user that “Something went wrong. Please try again.” This is where a normal user, if not in direct sight of the garage door, would think that their garage door is indeed open, when in reality it is securely closed. If the user believes the MyQ app then they would do as the application indicates and “try again” – this is where the statelessness of garage doors comes into play. The MyQ Hub will send the open/closed signal to the garage door and it will open, because it is already closed, and it is simply changing state. This allows an attacker direct entry into the garage, and, in many cases, into the home.

Since now the garage door is really open the attacker probably doesn’t want to leave the state as-is, notifying the victim that something went wrong again. Putting the garage door into a closed state and allowing the app to clear the error will put the victim at ease. This could be executed either by a replay from a previously captured closed signal, or, in the most simplistic manner by removing the remote sensor from the Velcro on the garage door and placing it in the vertical position, signaling to the Hub that the door closed successfully.

Attack Reproduction State Flowchart

We also realized that in a real-world scenario, an attacker wouldn’t likely sit outside of a garage all day, so we decided to automate the attack. We used GNU radio to implement a JIT (just in time) method of jamming where the SDR will sit dormant listening on the MyQ’s three separate frequencies. The moment it notices that the remote door sensor is beginning a transmission, it will dynamically enable and start jamming of the signal.

GNU Radio JIT Jamming and State Capture over 3 Simultaneous Frequencies

This expands the use cases of this type of attack by being able to create a small device that could be placed out of sight near the garage door. This technique is also described in more detail in our FHSS white paper. The JIT jamming makes it very difficult to locate the device using RF triangulation and allows it to be better equipped for battery operation.

While this may not be too common for individuals using the MyQ Hub, recall the earlier reference to third-party partnerships with MyQ for garage delivery. Another possible attack would be when a delivery driver uses the application. The primary reason users sign up for this service is the concept of a package delivery to a secure location (garage) even when they are not home. The victim can be absent from the property yet have access via the MyQ app over the internet to open or close the garage door if a delivery driver uses the MyQ hub for an in-garage delivery. A determined hacker could pull this attack off and the victim may have a higher probability of believing that the door may in fact be open. We disclosed our findings in full to Chamberlain on 9/25/2019, including detailed reproduction steps for the jamming attack. We also talked to Chamberlain on this issue with the third-party delivery drivers and how it could fit into this attack model. After extensive testing and validation of the issue, the vendor released an update to the myQ App as of version This update provides a valuable warning message to users indicating the garage door state may not be accurate, but it does not eliminate the user from remotely controlling the door itself.

The beauty of IOT devices are that they solve problems that we have learned to deal with. After we experience the convenience and the way these devices can automate, secure, or assist in our lives it is hard to see them ever going away. This ease and automation often overshadows the potential security threat that they may pose. Even simple enhancements to manual products over time have this effect; take for example the now-legacy garage door opener in your car. The ability to capture and replay the basic signals transformed the threat from physical to digital space. While the Chamberlain MyQ Hub ultimately produces a generally more secure method of accessing garages than its predecessors, consumers should be aware that any extension of a technology platform, such as using WiFi, a mobile app and FHSS RF transmissions, also extends possible threat vectors.

We would like to finish by commenting that the likelihood of a real-world attack on this target is low, based on the complexity of the attack and installation footprint. We have discussed this with Chamberlain, who has validated the findings and agrees with this assessment. Chamberlain has made clear efforts to build a secure product and appears to have eliminated much of the low-hanging fruit common to IoT devices. This vendor has been a pleasure to work with and clearly prioritizes security as a foresight in the development of its product.

NOTE: Within the research related to Chamberlain Garage Door Hub described in this blog, the only interference was to unlicensed spectrum radio frequency for the minimum period while the garage door hub was transmitting state signal, and there was no interference with any communications signal licensed or authorized under the Communications Act or FCC rules.



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Mr. Coffee with WeMo: Double Roast

30 May 2019 at 16:50

McAfee Advanced Threat Research recently released a blog detailing a vulnerability in the Mr. Coffee Coffee Maker with WeMo. Please refer to the earlier blog to catch up with the processes and techniques I used to investigate and ultimately compromise this smart coffee maker. While researching the device, there was always one attack vector that I had wanted to revisit. It was during the writing of that blog that I was finally able to circle back to it. As it turns out, my intuition was accurate; the second vulnerability I found was much simpler and still allowed me to gain root access to the target.

Recapping the original vulnerability

The first vulnerability modified the “template” section of the brew schedule rule file, which a is unique file that is sent when the user schedules a brew in advance. I also needed to modify the template itself, sent from the WeMo App directly to the coffee maker. During that research I noticed that many of the other fields could be impactful but did not investigate them as thoroughly as the template field.

Figure 1: Brew schedule rule

When the user schedules a brew, an individual rule is added to the Mr. Coffee root crontab. The crontab entry uses the rule’s “id” field to make sure the correct rule is executed at the desired time.

Figure 2: Root crontab entry

Crontab allows for basic scheduling features from the OS level. The user provides both the command to execute as well as timing details down to the minute, as shown in Figure 3.

Figure 3: Crontab syntax

During the initial research, I started to fuzz the rule id field; however, because every rule name that I placed in the malicious schedule was always prepended by the “/sbin/rtng_run_rule”, I could not get anything abnormal to happen. I also noticed that a lot of characters that could be useful for command injection were being filtered.

The following is a list of characters sanitized or filtered on input.

At this point I moved on and ended up finding the template vulnerability as laid out in the previous blog.

Finding an even more simple vulnerability

A few months after disclosing to Belkin, I revisited the steps to achieve this template abuse feature, in preparation for a public disclosure blog. Having the ability to write arbitrary code directly into the root’s crontab is enticing, so I began looking into it again. I needed to find a way to terminate the “rtng_run_rule” and add my own commands to the crontab file by modifying the “id” field. The “rtng_run_rule” file is a shell script that directly calls a Lua script named “rtng_run_rule.lua”. I noticed that I could send the double pipe “||” character but the “rtng_run_rule” wrapper script would never return a failing return code. Next, I looked at the how the wrapper script is handling command line arguments as shown below.

Figure 4: rtng_run_rule wrapper script

At this point I created a new rule: “-f|| touch test”. The “-f” is not a parsed argument, meaning it will take the “Bad option” case, causing the “rtng_run_rule” wrapper script to return “-1”. With the wrapper script returning a failing return code, the “||” (or) statement is initiated, which executes “touch test” and creates an empty file named “test”. Since I still had serial access (I explain in detail in my previous blog how I achieved this) I was able to log in to the coffee maker and find where the “test” file was located. I found it in root’s home directory.

Being able to write arbitrary files and execute commands without the “/” character is still somewhat limiting, as most file paths and web URLs will need forward slashes. I needed to find a way to execute commands that had “/” characters in them. I decided to do this by downloading a file from a webserver I control and executing it in Ash to bypass file path sanitization characters.

Figure 5: Commands allowing for execution of filtered characters.

Let me break this down. The “-f” as indicated before will cause the wrapper script to execute the “||” command. Then the “wget” command will initiate a download from my web server, located at IP address “” The “-q” will force wget to only print what it receives, and the “-O -“ tells wget to print to STDOUT instead of a file. Finally, the “| ash” command grabs all the output from STDOUT and executes it as Linux shell commands.

This way I can set up a server that simply returns a file containing necessary Linux commands and host it on my local machine. When I send the rule with the above command injection it will reach out to my local server and execute everything as root. The technique of piping wget into Ash also bypasses all the character filtering so I can now execute any command I want.

Status with Vendor

Belkin did patch the original template vulnerability and released new firmware. The vulnerability explained in this blog was found on the new firmware and, as of today, we have not heard of any plans for a patch. This vulnerability was disclosed to Belkin on February 25th, 2019. In accordance with our vulnerability disclosure policy, we are releasing details of this flaw today in hopes of alerting consumers of the device of the ongoing security findings. While this bug is also within the Mr. Coffee with WeMo’s scheduling function, it is much easier for an attacker to leverage since it does not require any modifications to templates or rehashing of code changes.The following demo video shows how this vulnerability can be used to compromise other devices on the network, including a fully patched Windows 10 PC.

Key takeways for enterprises, consumers and vendors

Devices such as the Mr. Coffee Coffee Maker with WeMo serve as a good reminder of the pros and cons to “smart” IoT. While advances in automation and technology offer exciting new capabilities, they should be weighed against the potential security concerns. In a home setting, consumers should set up these types of devices on a segmented network, isolated from sensitive network traffic and more critical devices. They should implement a strong password policy to make network access more challenging and apply patches or updates for all networked devices whenever available. Enterprises should restrict access to devices such as these in corporate environments or, at a minimum, provide a policy for oversight and management. They should be treated just the same as any other asset on the network, as IoT devices are often unmonitored pivot points into more critical network infrastructure. Network scanning and vulnerability assessments should be performed, in conjunction with a rigorous patching cycle for known issues. While the vendor has not provided a CVE for this vulnerability, we calculated a CVSS score of 9.1 out of 10. This score would categorize this as a critical vulnerability.Finally, as consumers of these products, we need to ask more of the vendors and manufacturers. A better understanding of secure coding and vulnerability assessment is critical, before products go to market. Vendors who implement a vulnerability reporting program and respond quickly can gain consumers’ trust and ensure product reputation is undamaged. One goal of the McAfee Advanced Threat Research team is to identify and illuminate a broad spectrum of threats in today’s complex and constantly evolving landscape. Through analysis and responsible disclosure, we aim to guide product manufacturers toward a more comprehensive security posture.

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Your Smart Coffee Maker is Brewing Up Trouble

25 February 2019 at 10:10

IOT devices are notoriously insecure and this claim can be backed up with a laundry list of examples. With more devices “needing” to connect to the internet, the possibility of your WiFi enabled toaster getting hacked and tweeting out your credit card number is, amazingly, no longer a joke.

With that in mind, I began to investigate the Mr. Coffee Coffee Maker with Wemo (WeMo_WW_2.00.11058.PVT-OWRT-Smart) since we had previously bought one for our research lab (and we don’t have many coffee drinkers, so I didn’t feel bad about demolishing it!). My hope was to build on previous work done by my colleague Douglas McKee (@fulmetalpackets) and his Wemo Insight smart plug exploit. Finding a similar attack vector absent in this product, I explored a unique avenue and was able to find another vulnerability.  In this post I will explore my methodology and processes in detail.

All Wemo devices have two ways of communicating with the Wemo App, remotely via the internet or locally directly to the Wemo App. Remote connectivity is only present when the remote access setting is enabled, which it is by default. To allow the Wemo device to be controlled remotely, the Wemo checks Belkin’s servers periodically for updates. This way the Wemo doesn’t need to open any ports on your network. However, if you are trying to control your Wemo devices locally, or the remote access setting is disabled, the Wemo app connects directly to the Wemo. All my research is based on local device communication with the remote access setting turned off.

To gain insight on how the coffee maker communicates with its mobile application, I first set up a local network capture on my cellphone using an application called “SSL Capture.” SSL Capture allows the user to capture traffic from mobile applications. In this case, I selected the Wemo application. With the capture running, I went through the Wemo app and initiated several standard commands to generate network traffic. By doing this, I was able to view the communication between the coffee maker and the Wemo application. One of the unique characteristics about the app is that the user is able schedule the coffee maker to brew at a specified time. I made a few schedules and saved them.

I began analyzing the network traffic between the phone application and the Mr. Coffee machine. All transmissions between the two devices were issued in plaintext, meaning no encryption was used. I also noticed that the coffee maker and the mobile app were communicating over a protocol called UPNP (Universal Plug and Play), which has preset actions called “SOAP ACTIONS.” Digging deeper into the network capture from the device, I saw the SOAP action “SetRules.” This included XML content that pertained to the “brew schedule” I had set from the mobile application.

A Mr. Coffee “brew” being scheduled.

At this point I was able to see how the Wemo mobile application handled brewing schedules. Next, I wanted to see if the coffee maker performed any sort of validation of these schedules so I went back into the mobile application and disabled them all. I then copied the data and headers from the network capture and used the Linux Curl command to send the packet back to the coffee maker. I got the return header status of “200” which means “OK” in HTTP. This indicated there was no validation of the source of brewing schedules; I further verified with the mobile application and the newly scheduled brew appeared.

Curl command to send a “Brew” schedule to the Wemo Coffee maker.

Screenshot of the Curl command populating the Wemo app with a brew schedule

At this point I could change the coffee maker’s brew schedule without ever using the Wemo mobile application. To understand how the schedules were stored on the Wemo coffee maker, I decided to physically disassemble it and look at the electronics inside. Once disassembled, I saw there was a Wemo module connected to a larger PCB responsible for controlling the functions of the coffee maker. I then extracted the Wemo module from the coffee maker. This looked almost Identical to the Wemo module that was in the Wemo Insight device. I leveraged Doug’s blog on exploitation of the Wemo Insight to replicate the serial identification, firmware extraction, and root password change. After I obtained root access via the serial port on the Wemo device, I began to investigate the way in which the Wemo application is initiated from the underlying Linux Operating System. While looking through some of the most common Linux files and directories, I noticed something odd in the “crontab” file (used in Linux to execute and schedule commands).

It appeared the developers decided to take the easy route and used the Linux crontab file to schedule tasks instead of writing their own brew scheduling function. The crontab entry was the same as the scheduled brew I sent via the Wemo application (coffee-3) and executed as root. This was especially interesting; if I could add some sort of command to execute from the replayed UPNP packet, I could potentially execute my command as root over the network.

With the firmware dumped, I decided to look at the “rtng_run_rule” executable that was called in the crontab. The rtng_run_rule is a Lua script. As Lua is a scripting language, it was written in plaintext and not compiled like all the other Wemo executables. I followed the flow of execution until I noticed the rule passing parameters to a template for execution. At this point, I knew it would be useless trying to inject commands directly into the rule and instead looked at modifying the template performing the execution.

I went back to the Wemo mobile application network captures and started to dig around again. I found the application also sends the templates to the Wemo coffee maker. If I could figure out how to modify the template and still have the Wemo think it is valid, I could get arbitrary code execution.

Template with the correct syntax to pass Wemo’s verification

There were 3 templates sent over, “do,” “do_if,” and “do_unless.” Each of the templates were Lua scripts and encoded with base64. Based on this, I knew it would be trivial to insert my own code; the only remaining challenge would be the MD5 hash included at the top of the template. As it turned out, that was hardly an obstacle.

I created an MD5 hash of the base-64 decoded Lua script and the base64 encoded script separately, simply to see if one or the other matched the hash that was being sent; however, neither matched the MD5 being sent in the template. I began to think the developers used some sort of HMAC or clever way to hash the template, which would have made it much harder to upload a malicious template. Instead, I was astounded to find out that it was simply the base64 code prepended by the string “begin-base64 644 <template name>” and appended with the string “====.”

At last I had the ability to upload any template of my choice and have it pass all the Wemo’s verification steps necessary to be used by a scheduled rule.

I appended a new template called “hack” and added a block of code within the template to download and execute a shell script.

Within that shell command, I instructed the Mr. Coffee Coffee Maker with Wemo to download a cross-complied version of Netcat so I can get a reverse shell, and also added an entry to “rc.local.” This was done so that if the coffee maker was power cycled, I would have persistent access to the device after reboot, via the Netcat reverse shell.

The final aspect of this exploit was to use what I learned earlier to schedule a brew with my new “hack” template executing my shell script. I took the schedule I was able to replay earlier and modified it to have the “hack” template execute 5 minutes from the time of sending. I did have to convert the time value required into the epoch time format.

Converting time to Epoch time.

Now, I sat back and waited as the coffee maker (at my specified time delay) connected to my computer, downloaded my shell script, and ran it. I verified that I had a reverse shell and that it ran as intended, perfectly.

This vulnerability does require network access to the same network the coffee maker is on. Depending on the complexity of the user’s password, WiFi cracking can be a relatively simple task to accomplish with today’s computing power. For example, we demonstrate a quick and easy brute force dictionary attack to crack a semi-complex WPA2 password (10 characters alpha-numeric) in the demo for the Wemo Insight smart plug.  However, even a slightly more complex password, employing special characters, would exponentially increase the difficulty of a brute force attack. We contacted Belkin (who owns Wemo) on November 16th, 2018 and disclosed this issue to them. While the vendor did not respond to this report, we were pleasantly surprised to see that the latest firmware update has patched the issue. Despite a general lack of communication, we’re delighted to see the results of our research further securing home automation devices.

This vulnerability shows that not all exploits are overly complicated or require an exceptional amount of effort to pull off, if you know what to look for. This vulnerability exists solely because a few poor coding decisions were made in conjunction with a lack of input sanitation and validation. Even though this target does not contain sensitive data and is limited to your local network, it doesn’t mean malicious hackers are not targeting IOT devices like this. These devices may serve as a sought-after target as they are often overlooked from a security standpoint and can provide a simple and unmonitored foothold into your home or business network. It is very important for any consumer, when purchasing new IOT gadgets, to ask themself: “Does this really need to be connected to the internet?” In the case of a coffee maker, I’ll let you be the judge.

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What’s in the Box?

25 February 2019 at 10:09

2018 was another record-setting year in the continuing trend for consumer online shopping.  With an increase in technology and efficiency, and a decrease in cost and shipping time, consumers have clearly made a statement that shopping online is their preferred method.

Chart depicting growth of online, web-influenced and offline sales by year.1

In direct correlation to the growth of online shopping preferences is the increase in home delivery, and correspondingly, package theft. Though my initial instinct was to attempt to recreate YouTuber Mark Rober’s glitter bomb, I practiced restraint and instead settled on investigating an innovative product called the BoxLock (BoxLock Firmware: 94.50 or below). The BoxLock is a smart padlock that you can setup outside of your house to secure a package delivery container. It can be opened either via the mobile application (Android or iPhone) or by using the built-in barcode scanner to scan a package that is out for delivery. The intent is that delivery drivers will use the BoxLock to unlock a secure drop box and place your package safely out of reach of package thieves. The homeowner can then unlock the lock from their phone using the app to retrieve their valuable deliveries.

Since I am more of a hardware researcher, the first step I did when I got the BoxLock was to take it apart to view the internals.

With the device disassembled and the main PCB extracted, I began to look for interesting pins, mainly UART and JTAG connections. I found 5 pins below the WiFi module that I thought could be UART, but after running it through a logic analyzer I didn’t see anything that looked like communication.

The BoxLock uses a SOC (System-on-a-Chip) which contains the CPU, RAM, ROM, and flash memory all in one. However, there was still an additional flash chip which I thought was odd. I used my Exodus Intelligence hardware interface board to connect to the SPI flash chip and dump the contents.

Exodus Intelligence XI Hardware Interface Board

The flash chip was completely empty. My working theory is that this flash chip is used to store the barcodes of packages out for delivery. There could also have been in issue with my version of Flashrom, which is the software I used to dump flash. The only reason I question my version of Flashrom is because I had to compile it myself with support for the exact flash chip (FT25H04S), since it is not supported by default.

The Main SOC (ATSAMD21J18)

Even though I couldn’t get anything from that flash chip, my main target here was the SOC. On the underside of the Process Control Board (PCB), I identified two tag-connect connection ports. I identified the SWD (Serial Wire Debug) pins located on the SOC (Pin 57 and 58 on the image above) and very slowly and carefully visually traced the paths to the smaller Tag-Connect connection.


Adafruit Feather M0 Development board

Since I have not done much JTAG analysis before, I grabbed an Adafruit Feather M0 that we had in our lab for testing, since the Feather uses the exact same SOC and WiFi chip as the BoxLock. The Adafruit Feather has excellent documentation on how to connect to the SOC via SWD pins I traced. I used Atmel Studio to read the info off the ATSAMD21 SOC; this showed me how to read the fuses as well as dump the entire flash off the Adafruit Feather.

SWD information of the Adafruit Feather M0

Atmel Studio also will let you know if the device has the “Security Bit” enabled. When set, the security bit is used to disable all external programming and debugging interfaces, making memory extraction and analysis extremely difficult. Once the security bit is set, the only way to bypass or clear the bit is to completely erase the chip.

Showing how to set the security bit on the Adafruit Feather M0

After I felt comfortable with the Adafruit feather I connected the BoxLock to a Segger JLink and loaded up Atmel Studio. The Segger JLink is a debugging device that can be used for JTAG and SWD. I was surprised that the developers set the security bit; this is a feature often overlooked in IOT devices. However, with the goal of finding vulnerabilities, this was a roadblock. I started to look elsewhere.

Segger JLink used for SWD communication

After spending some time under the microscope, I was able to trace back the larger Tag-Connect port to the BLE (Bluetooth Low Energy) module. The BLE module also has a full SOC which could be interesting to look at, but before I began investigating the BLE chip I still had two vectors to look at first: BLE and WiFi network traffic.

BLE is different to Bluetooth. The communication between BLE devices is secured by the use of encryption, whose robustness depends on the pairing mode used and BLE allows a few different pairing modes; the least secure “Just Works ” pairing mode is what the BoxLock is using. This mode allows any device to connect to it without the pin pairing that normal Bluetooth connections are known for. This means BLE devices can be passively intercepted and are susceptible to MITM (Man in The Middle) attacks.

BLE roles are defined at the connection layer. GAP (Generic Access Profile) describes how devices identify and connect to each other. The two most important roles are the Central and Peripheral roles. Low power devices like the BoxLock follow the Peripheral role and will broadcast their presence (Advertisement). More powerful devices, such as your phone, will scan for advertising devices and connect to them (this is the Central role). The communication between the two roles is done via special commands usually targeted at a GATT (Generic Attributes) services. GATT services can be standard and generic, such as the command value 0x180F, which is the Battery Service. Standardized GATT services help devices communicate with one another without the need for custom protocols. The GATT services present on the BoxLock were all custom, which means they will be displayed as “Unknown Service” when enumerated in a Bluetooth/BLE app.  I chose Nordic’s NRF Connect, available in both the Apple and Android app stores or as a desktop application.

NRF Connect application connected to the BoxLock via BLE

Since the BoxLock was using custom GATT commands I decided to disassemble the Android APK to see if I could find any more information on the “Unknown” UUIDs. I used a tool called “dex2jar” to disassemble the Android APK and then ran the JavaScript code through JSBeautify to clean up the code.

Next, I began searching for UUIDs and the keyword “GATT”. I was able to find the entire list of GATT services and what they pertain to.

GATT services UUID descriptions

The one I was most interested in was labeled as “Command Service”, where the unlock GATT command is sent to. To try it out, I used the NRF Connect application to send a GATT “sendOpenSignal” command with an attribute value of “2”.

How the Android application sends the unlock command

It was just that simple; lo and behold, the BoxLock unlocked!

I was amazed; the phone that I used to send the GATT command over had never connected to the BoxLock before and did not have the BoxLock application installed, yet it was able to unlock the BoxLock. (The vulnerable application version is v1.25 and below).

Continuing to explore the other GATT UUIDs, I was able to read the WiFi SSID, access token, user’s email, and client ID directly off the device. I was also able to write any of these same values arbitrarily.

Information that you can see about the BoxLock via the NRF Connect application

The mandatory identifiers required for the BoxLock to unlock are the access token, user email, and client ID. If those values are not present the device will not authenticate via the cloud API and will not unlock.

The most glaring issue with having all these fields readable and writeable is that I was able to successfully replay them on the device, ultimately bypassing any authentication which led to the BoxLock unlocking.

From my testing, these values never expired and the only way I found that the device cleared the credentials necessary to authenticate was when I removed the battery from the BoxLock. The BoxLock battery is “technically” never supposed to be removed, but since I physically disassembled the lock, (which took a decent amount of effort), I was able to test this.

Even though I was able to unlock the BoxLock, I still wanted to explore one other common attack vector.  I analyzed the network traffic between the device and the internet. I quickly noticed that, apart from firmware updates, device-to-cloud traffic was properly secured with HTTPS and I could not easily get useful information from this vector.

I do not currently have an estimate of the extent of this product’s deployment, so I cannot comment on how wide the potential impact could have been if this issue had been found by a malicious party. One constraint to the attack vector is that it requires BLE, which communicates from a distance of approximately 30 or 40 feet. However, for someone looking to steal packages this would not be a challenge difficult to overcome, as the unlocking attack could be completed very quickly and easily, making the bar for exploitation simply a smart phone with Bluetooth capability. The ease and speed of the exploit could have made for an enticing target for criminals.

I want to take a moment to give some very positive feedback on this vendor. Vulnerability disclosure can be a challenging issue for any company to deal with, but BoxLock was incredibly responsive, easy to work with and immediately recognized the value that McAfee ATR had provided. Our goal is to eliminate vulnerabilities before malicious actors find them, as well as illuminate security issues to the industry so we can raise the overall standard for security. BoxLock was an excellent example of this process at work; the day after disclosing the vulnerability, they set up a meeting with us to discuss our findings, where we proposed a mitigation plan. The BoxLock team set a plan in place to patch not only the BoxLock firmware but the mobile applications as well. Within a week, the vendor created a patch for the vulnerability and updated the mobile apps to force mandatory update to the patched firmware version. We tested the firmware and app update and verified that the application properly clears credentials after use on the vulnerable firmware. We also tested the new firmware which clears the credentials even without the mobile app’s interaction.

IoT security has increasingly become a deciding factor for consumers. The process of vulnerability disclosure is an effective method to increase collaboration between vendors, manufacturers, the security community and the consumer. It is our hope that vendors move towards prioritizing security early in the product development lifecycle. We’d like to thank BoxLock for an effective end-to-end communication process, and we’re pleased to report that this significant flaw has been quickly eradicated. We welcome any questions or comments on this blog!

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