Adventures in Video Conferencing Part 5: Where Do We Go from Here?

Posted by Natalie Silvanovich, Project Zero

Overall, our video conferencing research found a total of 11 bugs in WebRTC, FaceTime and WhatsApp. The majority of these were found through less than 15 minutes of mutation fuzzing RTP. We were surprised to find remote bugs so easily in code that is so widely distributed. There are several properties of video conferencing that likely led to the frequency and shallowness of these issues.

WebRTC Bug Reporting

When we started looking at WebRTC, we were surprised to discover that their website did not describe how to report vulnerabilities to the project. They had an open bug tracker, but no specific guidance on how to flag or report vulnerabilities. They also provided no security guidance for integrators, and there was no clear way for integrators to determine when they needed to update their source for security fixes. Many integrators seem to have branched WebRTC without consideration for applying security updates. The combination of these factors make it more likely that vulnerabilities did not get reported, vulnerabilities or fixes got ‘lost’ in the tracker, fixes regressed or fixes did not get applied to implementations that use the source in part.

We worked with the WebRTC team to add this guidance to the site, and to clarify their vulnerability reporting process. Despite these changes, several large software vendors reached out to our team with questions about how to fix the vulnerabilities we reported. This shows there is still a lack of clarity on how to fix vulnerabilities in WebRTC.

Video Conferencing Test Tools

We also discovered that most video conferencing solutions lack adequate test tools. In most implementations, there is no way to collect data that allows for problems with an RTP stream to be diagnosed. The vendors we asked did not have such a tool, even internally.  WebRTC had a mostly complete tool that allows streams to be recorded in the browser and replayed, but it did not work with streams that used non-default settings. This tool has now been updated to collect enough data to be able to replay any stream. The lack of tooling available to test RTP implementations likely contributed to the ease of finding vulnerabilities, and certainly made reproducing and reporting vulnerabilities more difficult

Video Conferencing Standards

The standards that comprise video conferencing such as RTP, RTCP and FEC introduce a lot of complexity in achieving their goal of enabling reliable audio and video streams across any type of connection. While the majority of this complexity provides value to the end user, it also means that it is inherently difficult to implement securely.

The Scope of Video Conferencing

WebRTC has billions of users. While it was originally created for use in the Chrome browser, it is now integrated by at least two Android applications that eclipse Chrome in terms of users: Facebook and WhatsApp (which only uses part of WebRTC). It is also used by Firefox and Safari. It is likely that most mobile devices run multiple copies of the WebRTC library. The ubiquity of WebRTC coupled with the lack of a clear patch strategy make it an especially concerning target for attackers.

Recommendations for Developers

This section contains recommendations for developers who are implementing video conferencing based on our observations from this research.

First, it is a good idea to use an existing solution for video conferencing (either WebRTC or PJSIP) as opposed to implementing a new one. Video conferencing is very complex, and every implementation we looked at had vulnerabilities, so it is unlikely a new implementation would avoid these problems. Existing solutions have undergone at least some security testing and would likely have fewer problems.

It is also advisable to avoid branching existing video conferencing code. We have received questions from vendors who have branched WebRTC, and it is clear that this makes patching vulnerabilities more difficult. While branching can solve problems in the short term, integrators often regret it in the long term.

It is important to have a patch strategy when implementing video conferencing, as there will inevitably be vulnerabilities found in any implementation that is used. Developers should understand how security patches are distributed for any third-party library they integrate, and have a plan for applying them as soon as they are available.

It is also important to have adequate test tools for a video conferencing application, even if a third-party implementation is used. It is a good idea to have a way to reproduce a call from end to end. This is useful in diagnosing crashes, which could have a security impact, as well as functional problems.

Several mobile applications we looked at had unnecessary attack surface. Specifically codecs and other features of the video conferencing implementation were enabled and accessible via RTP even though no legitimate call would ever use them. WebRTC and PJSIP support disabling specific features such as codecs and FEC. It is a good idea to disable the features that are not being used.

Finally, video conferencing vulnerabilities can generally be split into those that require the target to answer the incoming call, and those that do not. Vulnerabilities that do not require the call to be answered are more dangerous. We observed that some video conferencing applications perform much more parsing of untrusted data before a call is answered than others. We recommend that developers put as much functionality after the call is answered as possible.

Tools

In order to open up the most popular video conferencing implementations to more security research, we are releasing the tools we developed to do this research. Street Party is a suite of tools that allows the RTP streams of video conferencing implementations to be viewed and modified. It includes:

• WebRTC: instructions for recording and replaying RTP packets using WebRTC’s existing tools
• FaceTime: hooks for recording and replaying FaceTime calls
• WhatsApp: hooks for recording and replaying WhatsApp calls on Android

We hope these tools encourage even more investigation into the security properties of video conferencing. Contributions are welcome.

Conclusion

We reviewed WebRTC, FaceTime and WhatsApp and found 11 serious vulnerabilities in their video conferencing implementations. Accessing and altering their encrypted content streams required substantial tooling. We are releasing this tooling to enable additional security research on these targets. There are many properties of video conferencing that make it susceptible to vulnerabilities. Adequate testing, conservative design and frequent patching can reduce the security risk of video conferencing implementations.

Adventures in Video Conferencing Part 1: The Wild World of WebRTC

Posted by Natalie Silvanovich, Project Zero

Over the past five years, video conferencing support in websites and applications has exploded. Facebook, WhatsApp, FaceTime and Signal are just a few of the many ways that users can make audio and video calls across networks. While a lot of research has been done into the cryptographic and privacy properties of video conferencing, there is limited information available about the attack surface of these platforms and their susceptibility to vulnerabilities. We reviewed the three most widely-used video conferencing implementations. In this series of blog posts, we describe what we found.

This part will discuss our analysis of WebRTC. Part 2 will cover our analysis of FaceTime. Part 3 will discuss how we fuzzed WhatsApp. Part 4 will describe some attacks against WhatsApp that didn’t work out. And finally, Part 5 will discuss the future of video conferencing and steps that  developers can take to improve the security of their implementation.

Typical Video Conferencing Architecture

All the video conferencing implementations we investigated allow at least two peers anywhere on the Internet to communicate through audiovisual streams. Implementing this capability so that it is reliable and has good audio and video quality presents several challenges. First, the peers need to be able to find each other and establish a connection regardless of NATs or other network infrastructure. Then they need to be able to communicate, even though they could be on different platforms, application versions or browsers. Finally, they need to maintain audio and video quality, even if the connection is low-bandwidth or noisy.

Almost all video conferencing solutions have converged on a single architecture. It assumes that two peers can communicate via a secure, integrity checked channel which may have low bandwidth or involve an intermediary server, and it allows them to create a faster, higher-bandwidth peer-to-peer channel.

The first stage in creating a connection is called signalling. It is the process through which the two peers exchange the information they will need to create a connection, including network addresses, supported codecs and cryptographic keys. Usually, the calling peer sends a call request including information about itself to the receiving peer, and then the receiving peer responds with similar information. SDP is a common protocol for exchanging this information, but it is not always used, and most implementations do not conform to the specification. It is common for mobile messaging apps to send this information in a specially formatted message, sent through the same channel text messages are sent. Websites that support video conferencing often use WebSockets to exchange information, or exchange it via HTTPS using the webserver as an intermediary.

Once signalling is complete, the peers find a way to route traffic to each other using the STUN, TURN and ICE protocols. Based on what these protocols determine, the peers can create UDP, UDP-over-STUN and occasionally TCP connections based of what is favorable for the network conditions.

Once the connection has been made, the peers communicate using Real-time Transport Protocol. Though this protocol is standardized, most implementations deviate somewhat from the standard. RTP can be encrypted using a protocol called Secure RTP (SRTP), and some implementations also encrypt streams using DTLS. Under the encryption envelope, RTP supports features that allow multiple streams and formats of data to be exchanged simultaneously. Then, based on how RTP classifies the data, it is passed on to other processing, such as video codecs.  Stream Control Transmission Protocol (SCTP) is also sometimes used to exchange small amounts of data (for example a text message on top of a call) during video conferencing, but it is less commonly used than RTP.

Even when it is encrypted, RTP often doesn’t include integrity protection, and if it does, it usually doesn’t discard malformed packets. Instead, it attempts to recover them using strategies such as Forward Error Correction (FEC). Most video conferencing solutions also detect when a channel is noisy or low-bandwidth and attempt to handle the situation in a way that leads to the best audio and video quality, for example, sending fewer frames or changing codecs. Real Time Control Protocol (RTCP) is used to exchange statistics on network quality and coordinate adjusting properties of RTP streams to adapt to network conditions.

WebRTC

WebRTC is an open source project that enables video conferencing. It is by far the most commonly used implementation. Chrome, Safari, Firefox, Facebook Messenger, Signal and many other mobile applications use WebRTC. WebRTC seemed like a good starting point for looking at video conferencing as it is heavily used, open source and reasonably well-documented.

WebRTC Signalling

I started by looking at WebRTC signalling, because it is an attack surface that does not require any user interaction. Protocols like RTP usually start being processed after a user has picked up the video call, but signalling is performed before the user is notified of the call. WebRTC uses SDP for signalling.

I reviewed the WebRTC SDP parser code, but did not find any bugs. I also compiled it so it would accept an SDP file on the commandline and fuzzed it, but I did not find any bugs through fuzzing either. I later discovered that WebRTC signalling is not implemented consistently across browsers anyhow. Chrome uses the main WebRTC implementation, Safari has branched slightly and Firefox uses their own implementation. Most mobile applications that use WebRTC implement their own signalling in a protocol that is not SDP as well. So it is not likely that a bug in WebRTC signalling would affect a wide variety of targets.

RTP Fuzzing

I then decided to look at how RTP is processed in WebRTC. While RTP is not an interaction-less attack surface because the user usually has to answer the call before RTP traffic is processed, picking up a call is a reasonable action to expect a user to take. I started by looking at the WebRTC source, but it is very large and complex, so I decided fuzzing would be a better approach.

The WebRTC repository contains fuzzers written for OSS-Fuzz for every protocol and codec supported by WebRTC, but they do not simulate the interactions between the various parsers, and do not maintain state between test cases, so it seemed likely that end-to-end fuzzing would provide additional coverage.

Setting up end-to-end fuzzing was fairly time intensive, so to see if it was likely to find many bugs, I altered Chrome to send malformed RTP packets. I changed the srtp_protect function in libsrtp so that it ran the following fuzzer on every packet:

void fuzz(char* buf, int len){ int q = rand()%10; if (q == 7){ int ind = rand()%len; buf[ind] = rand(); } if(q == 5){ for(int i = 0; i < len; i++) buf[i] = rand(); }

RTP fuzzer (fuzzer q)

When this version was used to make a WebRTC call to an unmodified instance of Chrome, it crashed roughly every 30 seconds.

Most of the crashes were due to divide-by-zero exceptions, which I submitted patches for, but there were three interesting crashes. I reproduced them by altering the WebRTC source in Chrome so that it would generate the packets that caused the same crashes, and then set up a standalone build of WebRTC to reproduce them, so that it was not necessary to rebuild Chrome to reproduce the issues.

The first issue, CVE-2018-6130 is an out-of-bounds memory issue related to the use of std::map find in processing VP9 (a video codec). In the following code, the value t10_pic_idx is pulled out of an RTP packet unverified (GOF stands for group of frames).

if (frame->frame_type() == kVideoFrameKey) {

   ...

   GofInfo info = gof_info_.find(codec_header.tl0_pic_idx)->second;

   FrameReceivedVp9(frame->id.picture_id, &info);

   UnwrapPictureIds(frame);

   return kHandOff;

 }

If this value does not exist in the gof_info_ array, std::map::find returns the end value of the map, which points to one element past the allocated values for the map. Depending on memory layout, dereferencing this iterator will either crash or return the contents of unallocated memory.

The second issue, CVE-2018-6129 is a more typical out-of-bounds read issue, where the index of a field is read out of an RTP packet, and not verified before it is used to index a vector.

The third issue, CVE-2018-6157 is a type confusion issue that occurs when a packet that looks like a VP8 packet is sent to the H264 parser. The packet will eventually be treated like an H264 packet even though it hasn’t gone through the necessary checks for H264. The impact of this issue is also limited to reading out of bounds.

There are a lot of limitations to the approach of fuzzing in a browser. It is very slow, the issues are difficult to reproduce, and it is difficult to fuzz a variety of test cases, because each call needs to be started manually, and certain properties, such as the default codec, can’t change in the middle of the call. After I reported these issues, the WebRTC team suggested that I use the video_replay tool, which can be used to replay RTP streams recorded in a patched browser. The tool was not able to reproduce a lot of my issues because they used non-default WebRTC settings configured through signalling, so I added the ability to load a configuration file alongside the RTP dump to this tool. This made it possible to quickly reproduce vulnerabilities in WebRTC.

This tool also had the benefit of enabling much faster fuzzing, as it was possible to fuzz RTP by fuzzing the RTP dump file and loading it into video_replay. There were some false positives, as it was also possible that fuzzing caused bugs in parsing the RTP dump file format, but most of the bugs were actually in RTP processing.

Fuzzing with the video_replay tool with code coverage and ASAN enabled led to four more bugs. We ran the fuzzer on 50 cores for about two weeks to find these issues.

CVE-2018-6156 is probably the most exploitable bug uncovered. It is a large overflow in FEC. The buffer WebRTC uses to process FEC packets is 1500 bytes, but it does no size checking of these packets once they are extracted from RTP. Practically, they can be up to about 2000 bytes long.

CVE-2018-6155 is a use-after-free in a video codec called VP8. It is interesting because it affects the VP8 library, libvpx as opposed to code in WebRTC, so it has the potential to affect software that uses this library other than WebRTC. A generic fix for libvpx was released as a result of this bug.

CVE-2018-16071 is a use-after-free in VP9 processing that is somewhat similar to CVE-2018-6130. Once again, an untrusted index is pulled out of a packet, but this time it is used as the upper bounds of a vector erase operation, so it is possible to delete all the elements of the vector before it is used.

CVE-2018-16083 is an out-of-bounds read in FEC that occurs due to a lack of bounds checking.

Overall, end-to-end fuzzing found a lot of bugs in WebRTC, and a few were fairly serious. They have all now been fixed. This shows that end-to-end fuzzing is an effective approach for finding vulnerabilities in this type of video conferencing solution. In Part 2, we will try a similar approach on FaceTime. Stay tuned!

Adventures in Video Conferencing Part 3: The Even Wilder World of WhatsApp

Posted by Natalie Silvanovich, Project Zero

WhatsApp is another application that supports video conferencing that does not use WebRTC as its core implementation. Instead, it uses PJSIP, which contains some WebRTC code, but also contains a substantial amount of other code, and predates the WebRTC project. I fuzzed this implementation to see if it had similar results to WebRTC and FaceTime.

Fuzzing Set-up

PJSIP is open source, so it was easy to identify the PJSIP code in the Android WhatsApp binary (libwhatsapp.so). Since PJSIP uses the open source library libsrtp, I started off by opening the binary in IDA and searching for the string srtp_protect, the name of the function libsrtp uses for encryption. This led to a log entry emitted by a function that looked like srtp_protect. There was only one function in the binary that called this function, and called memcpy soon before the call. Some log entries before the call contained the file name srtp_transport.c, which exists in the PJSIP repository. The log entries in the WhatsApp binary say that the function being called is transport_send_rtp2 and the PJSIP source only has a function called transport_send_rtp, but it looks similar to the function calling srtp_protect in WhatsApp, in that it has the same number of calls before and after the memcpy. Assuming that the code in WhatsApp is some variation of that code, the memcpy copies the entire unencrypted packet right before it is encrypted.

Hooking this memcpy seemed like a possible way to fuzz WhatsApp video calling. I started off by hooking memcpy for the entire app using a tool called Frida. This tool can easily hook native function in Android applications, and I was able to see calls to memcpy from WhatsApp within minutes. Unfortunately though, video conferencing is very performance sensitive, and a delay sending video packets actually influences the contents of the next packet, so hooking every memcpy call didn’t seem practical. Instead, I decided to change the single memcpy to point to a function I wrote.

I started off by writing a function in assembly that loaded a library from the filesystem using dlopen, retrieved a symbol by calling dlsym and then called into the library. Frida was very useful in debugging this, as it could hook calls to dlopen and dlsym to make sure they were being called correctly. I overwrote a function in the WhatsApp GIF transcoder with this function, as it is only used in sending text messages, which I didn’t plan to do with this altered version. I then set the memcpy call to point to this function instead of memcpy, using this online ARM branch finder.

sub_2F8CC MOV             X21, X30 MOV             X22, X0 MOV             X23, X1 MOV             X20, X2 MOV             X1, #1 ADRP            X0, #aDataDataCom_wh@PAGE ; "/data/data/com.whatsapp/libn.so" ADD             X0, X0, #aDataDataCom_wh@PAGEOFF ; "/data/data/com.whatsapp/libn.so" BL              .dlopen ADRP            X1, #aApthread@PAGE ; "apthread" ADD             X1, X1, #aApthread@PAGEOFF ; "apthread" BL              .dlsym MOV             X8, X0 MOV             X0, X22 MOV             X1, X23 MOV             X2, X20 NOP BLR             X8 MOV             X30, X21 RET

The library loading function

I then wrote a library for Android which had the same parameters as memcpy, but fuzzed and copied the buffer instead of just copying it, and put it on the filesystem where it would be loaded by dlopen. I then tried making a WhatsApp call with this setup. The video call looked like it was being fuzzed and crashed in roughly fifteen minutes.

Replay Set-up

To replay the packets I added logging to the library, so that each buffer that was altered would also be saved to a file. Then I created a second library that copied the logged packets into the buffer being copied instead of altering it. This required modifying the WhatsApp binary slightly, because the logged packet will usually not be the same size as the packet currently being sent. I changed the length of the hooked memcpy to be passed by reference instead of by value, and then had the library change the length to the length of the logged packet. This changed the value of the length so that it would be correct for the call to srtp_protect. Luckily, the buffer that the packet is copied into is a fixed length, so there is no concern that a valid packet will overflow the buffer length. This is a common design pattern in RTP processing that improves performance by reducing length checks. It was also helpful in modifying FaceTime to replay packets of varying length, as described in the previous post.

This initial replay setup did not work, and looking at the logged packets, it turned out that WhatsApp uses four streams with different SSRCs for video conferencing (possibly one for video, one for audio, one for synchronization and one for good luck). The streams each had only one payload type, and they were all different, so it was fairly easy to map each SSRC to its stream. So I modified the replay library to determine the current SSRC for each stream based on the payload types of incoming packets, and then to replace the SSRC of the replayed packets with the correct one based on their payload type. This reliably replayed a WhatsApp call. I was then able to fuzz and reproduce crashes on WhatsApp.

Results

Using this setup, I reported one heap corruption issue on WhatsApp, CVE-2018-6344. This issue has since been fixed. After this issue was resolved, fuzzing did not yield any additional crashes with security impact, and we moved on to other methodologies. Part 4 will describe our other (unsuccessful) attempts to find vulnerabilities in WhatsApp.

Adventures in Video Conferencing Part 2: Fun with FaceTime

Posted by Natalie Silvanovich, Project Zero

FaceTime is Apple’s video conferencing application for iOS and Mac. It is closed source, and does not appear to use any third-party libraries for its core functionality. I wondered whether fuzzing the contents of FaceTime’s audio and video streams would lead to similar results as WebRTC.

Fuzzing Set-up

Philipp Hancke performed an excellent analysis of FaceTime’s architecture in 2015. It is similar to WebRTC, in that it exchanges signalling information in SDP format and then uses RTP for audio and video streams. Looking at the FaceTime implementation on a Mac, it seemed the bulk of the calling functionality of FaceTime is in a daemon called avconferenced. Opening up the binary that supports its functionality, AVConference in IDA, it contains a function called SRTPEncryptData. This function then calls CCCryptorUpdate, which appeared to encrypt RTP packets below the header.

To do a quick test of whether fuzzing was likely to be effective, I hooked this function and altered the underlying encrypted data. Normally, this can be done by setting the DYLD_INSERT_LIBRARIES environment variable, but since avconferenced is a daemon that restarts automatically when it dies, there wasn’t an easy way to set an environment variable. I eventually used insert_dylib to alter the AVConference binary to load a library on startup, and restarted the process. The library loaded used DYLD_INTERPOSE to replace CCCryptorUpdate with a version that fuzzed every input buffer (using fuzzer q from Part 1) before it was processed. This implementation had a lot of problems: it fuzzed both encryption and decryption, it affected every call to CCCryptorUpdate from avconferenced, not just ones involved in SRTP and there was no way to reproduce a crash. But using the modified FaceTime to call an iPhone led to video output that looked corrupted, and the phone crashed in a few minutes. This confirmed that this function was indeed where FaceTime calls are encrypted, and that fuzzing was likely to find bugs.

I made a few changes to the function that hooked CCCryptorUpdate to attempt to solve these problems. I limited fuzzing the input buffer to the two threads that write audio and video output to RTP, which also solved the problem of decrypted packets being fuzzed, as these threads only ever encrypt. I then added functionality that wrote the encrypted, fuzzed contents of each packet to a series of log files, so that test cases could be replayed. This required altering the sandbox of avconferenced so that it could write files to the log location, and adding spinlocks to the hook, as calling CCCryptorUpdate is thread safe, but logging packets isn’t.

Call Replay

I then wrote a second library that hooks CCCryptorUpdate and replays packets logged by the first library by copying the logged packets in sequence into the packet buffers passed into the function. Unfortunately, this required a small modification to the AVConference binary, as the SRTPEncryptData function does not respect the length returned by CCCryptorUpdate; instead, it assumes that the length of the encrypted data is the same as the length as the plaintext data, which is reasonable when CCCryptorUpdate isn’t being hooked. Since SRTPEncryptData always uses a large fixed-size buffer for encryption, and encryption is in-place, I changed the function to retrieve the length of the encrypted buffer from the very end of the buffer, which was set in the hooked CCCryptorUpdate call. This memory is unlikely to be used for other purposes due to the typical shorter length of RTP packets. Unfortunately though, even though the same encrypted data was being replayed to the target, it wasn’t being processed correctly by the receiving device.

To understand why requires an explanation of how RTP works. An RTP packet has the following format.

It contains several fields that impact how its payload is interpreted. The SSRC is a random identifier that identifies a stream. For example, in FaceTime the audio and video streams have different SSRCs. SSRCs can also help differentiate between streams in a situation where a user could potentially have an unlimited number of streams, for example, multiple participants in a video call. RTP packets also have a payload type (PT in the diagram) which is used to differentiate different types of data in the payload. The payload type for a certain data type is consistent across calls. In FaceTime, the video stream has a single payload type for video data, but the audio stream has two payload types, likely one for audio data and the other for synchronization. The marker (M in the diagram) field of RTP is also used by FaceTime to represent when a packet is fragmented, and needs to be reassembled.

From this it is clear that simply copying logged data into the current encrypted packet won’t function correctly, because the data needs to have the correct SSRC, payload type and marker, or it won’t be interpreted correctly. This wasn’t necessary in WebRTC, because I had enough control over WebRTC that I could create a connection with a single SSRC and payload type for fuzzing purposes. But there is no way to do this in FaceTime, even muting a video call leads to silent audio packets being sent as opposed to the audio stream shutting down. So these values needed to be manually corrected.

An RTP feature called extensions made correcting these fields difficult. An extension is an optional header that can be added to an RTP packet. Extensions are not supposed to depend on the RTP payload to be interpreted, and extensions are often used to transmit network or display features. Some examples of supported extensions include the orientation extension, which tells the endpoint the orientation of the receiving device and the mute extension, which tells the endpoint whether the receiving device is muted.

Extensions mean that even if it is possible to determine the payload type, marker and SSRC of data, this is not sufficient to replay the exact packet that was sent. Moreover, FaceTime creates extensions after the packet is encrypted, so it is not possible to create the complete RTP packet by hooking CCCryptorUpdate, because extensions could be added later.

At this point, it seemed necessary to hook sendmsg as well as CCCryptorUpdate. This would allow the outgoing RTP header to be modified once it is complete. There were a few challenges in doing this. To start, audio and video packets are sent by different threads in FaceTime, and can be reordered between the time they are encrypted and the time they are sent by sendmsg. So I couldn’t assume that if sendmsg received an RTP packet that it was necessarily the last one that was encrypted. There was also the problem that SSRCs are dynamic, so replaying an RTP packet with the same SSRC it is recorded with won’t work, it needs to have the new SSRC for the audio or video stream.

Note that in MacOS Mojave, FaceTime can call sendmsg via either the AVConference binary or the IDSFoundation binary, depending on the network configuration. So to capture and replay unencrypted RTP traffic on newer systems, it is necessary  to hook CCCryptorUpdate in AConference and sendmsg in IDSFoundation (AVConference calls into IDSFoundation when it calls sendmsg). Otherwise, the process is the same as on older systems.

I ended up implementing a solution that recorded packets by recording the unencrypted payload, and then recorded its RTP header, and using a snippet of the encrypted payload to pair headers with the correct unencrypted payload. Then to replay packets, the packets encrypted in CCCryptorUpdate were replaced with the logged packets, and once the encrypted payload came through to sendmsg, the header was replaced with the logged one for that payload. Fortunately, the two streams with unique SSRCs used by FaceTime do not share any payload types, so it was possible to determine the new SSRC for each stream by waiting for an incoming packet with the correct payload type. Then in each subsequent packet, the SSRC was replaced with the correct one.

Unfortunately, this still did not replay a FaceTime call correctly, and calls often experienced decryption failures. I eventually determined that audio and video on FaceTime are encrypted with different keys, and updated the replay script to queue the CCCryptor used by CCCryptorUpdate function based on whether it was audio or video content. Then in sendmsg, the entire logged RTP packet, including the unencrypted payload, was copied into the outgoing packet, the SSRC was fixed, and then the payload encrypted with the next CCCryptor out of the appropriate queue. If a CCCryptor wasn’t available, outgoing packets were dropped until a new one was created. At this point, it was possible to stop using the modified AVConference binary, as all the packet modification was now happening in sendmsg. This implementation still had reliability problems.

Digging more deeply into how FaceTime encryption works, packets are encrypted in CTS mode, which requires a counter. The counter is initialized to a unique value for each packet that is sent. During the initialization of the RTP stream, the peers exchange two 16-byte random tokens, one for audio and one for video. The counter value for each packet is then calculated by exclusive or-ing the token with several values found in the packet, including the SSRC and the sequence number. Only one value in this calculation, the sequence number, changes between each packet. So it is possible to calculate the counter value for each packet by knowing the initial counter value and sequence number, which can be retrieved by hooking CCCryptorCreateWithMode. The sequence number is xor-ed with the random token at index 0x12 when FaceTime constructs a counter, so by xor-ing this location with the initial sequence number and then a packet’s sequence number, the counter value for that packet can be calculated. The key can also be retrieved by hooking CCCryptorCreateWithMode.This allowed me to dispense with queuing cryptors, as I now had all the information I needed to construct a cryptor for any packet. This allowed for packets to be encrypted faster and more accurately.

Sequence numbers still posed a problem though, as the initial sequence number of an RTP stream is randomly generated at the beginning of the call, and is different between subsequent calls. Also, sequence numbers are used to reconstruct video streams in order, so they need to be correct. I altered the replay tool determine the starting sequence number of each stream, and then calculate the difference between the starting sequence number of each logged stream and the sequence number of the logged packet and then add it to this value. These two changes finally made the replay tool work, though replay gets slower and slower as a stream is replayed due to dropped packets.   

Results

Using this setup, I was able to fuzz FaceTime calls and reproduce the crashes. I reported three bugs in FaceTime based on this work. All these issues have been fixed in recent updates.

CVE-2018-4366 is an out-of-bounds read in video processing that occurs on Macs only.

CVE-2018-4367 is a stack corruption vulnerability that affects iOS and Mac. There are a fair number of variables on the stack of the affected function before the stack cookie, and several fuzz crashes due to this issue caused segmentation faults as opposed to stack_chk crashes, so it is likely exploitable.

CVE-2018-4384 is a kernel heap corruption issue in video processing that affects iOS. It is likely similar to this vulnerability found by Adam Donenfeld of Zimperium.

All of these issues took less than 15 minutes of fuzzing to find on a live device. Unfortunately, this was the limit of fuzzing that could be performed on FaceTime, as it would be difficult to create a command line fuzzing tool with coverage like we did for WebRTC as it is closed source.

In Part 3, we will look at video calling in WhatsApp.