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Welcome. After reading other digital forensic blogs over the past couple of years I decided to start my own. I have gained a lot by reading research done by others, so I thought it would only be right to give back to the digital forensic community.

I work in the government sector, so I will be limiting identifying information about me or my organization. If you’re able to figure out who I am that’s fine. Just know that I will actively try to not put anything identifying in here, although that may not be possible at times. The posts will focus on the forensics.

A bit about me: I manage a digital forensics lab in an ISO 17025 environment. In addition to all of the managerial duties, I am also responsible for technical operations of my group and carry a full caseload. If I am expected to lead, I also better do. 🙂

My vision for this blog will start small. I hold a full-time job, have a family, and am in the middle of a master’s program. I am aiming for one post a month, but there may be more or less depending on life.

Again, welcome, and thank you for taking time to read the posts. I welcome feedback, regardless if it is positive or negative. If you see something that is inaccurate please let me know so I can correct whatever it is that you see. I do not want to proliferate bad information to the DF community.

Me(n)tal Health in DFIR – It’s Kind of a Big Deal

When I initially started this blog I set a modest goal of making one post a month with the understanding that sometimes life will happen and take priority. Well, life is happening for me this month: an imminent house move, an upcoming presentation at DFRWS USA, the GCFE, and several cases at work have kept me extremely busy. With all that going on there has been absolutely zero time for any research. Being the stubborn person I am, though, I couldn’t NOT post something, so here we are. Fortunately, there are no screenshots this month. 🙂

A few days ago I was cruising around the DFIR Discord channel when someone asked an important question. The question was this: how are examiners/investigators who are exposed to child sexual exploitation material (i.e. child pornography) given mental health support, if any. The few replies that came were all over the place. Some responses indicated they received zero support, others got what I would consider partial support, and one responder indicated they got a lot of support.

Why?

I have the unfortunate experience of being exposed to this material at my current job assignment, and have been for several years now due to past job assignments. No one wants to see it, be around it, or be around individuals who willingly seek out this material. This material doesn’t magically appear out of thin air; it has to be created, which means a child has to be sexually exploited. This is against the law. Period.

Viewing these acts is…terrible.

In addition to the social implications, there is a societal need to investigate people who possess, distribute, and create this material. These investigations are mentally taxing because the material is tough to look at, plain and simple. But, the investigations have to be done. There is no way around it. The well-being of a child is at stake.

The subject matter of these investigations require a special kind of person to do them. I cannot tell you how many times I have had seasoned investigators say to me “I don’t know how you do it. I would jump across the table and kill them.” The thing is that I believe that they would do just that. Investigators/examiners are human, and, just like everyone else, we are all wired differently. Certain things may trigger a severe emotional response in one investigator/examiner, and not trigger a severe emotional response in another. Investigators/examiners who do these types of investigations/examinations have to have a particular mindset. Having done all kinds of criminal investigations and examinations for various criminal offenses, I can tell you, for example, that there is a difference in mindset between dealing with a homicide suspect and an individual who peddles in this material.

Investigators/examiners who are exposed to this material have to keep severe emotional responses in check in order to remain professional and do their job, and it takes a lot of mettle to do this. That mental effort, along with being repeatedly exposed to this material, takes a toll on the mind and the heart. I have seen colleagues crumble under the mental and emotional stress caused by these investigations/examinations, and walk away from investigations/digital forensics. I even had a co-worker take their own life.

And the need for mental fortitude doesn’t just apply to law enforcement investigators/examiners. The private sector has its own set of stressors that takes a mental and physical toll on DFIR personnel that operate in that arena. Long hours, being away from family/friends, conflicting priorities, deadlines and employer/peer expectations can all introduce stress and cause the mind to buckle and suffer.

And, if you think the non-law enforcement DFIR people don’t see some disturbing material, you are wrong. Digital devices act as a sort of safe for the mind (in addition to being the bicycle Steve Jobs liked to talk about), so people will store valuable things in them. Sometimes these valuable things may have a (negative) social stigma associated with them, and the owner wants to keep them secret, afraid that someone will find out their secret. DFIR practitioners who operate in the private/non-law enforcement sector will find this stuff, and while it may not be unlawful to posses the material, it may still be disturbing, so viewing it takes a toll.

I will add that this discussion also applies to those who conduct forensic audio/video examinations. Our team does those exams, too. We have the unfortunate experience, at times, of watching/listening to a person die or be seriously injured or maimed. Audio/video examinations are some of the toughest we do because we actually see/hear the event.

It Doesn’t Have To Be This Way

There have been a few DFIR blog posts published in the past few months that have addressed burnout/mental health in our discipline, so I am not going to re-hash what they have said. They are good articles, and DFIR folks should read them. If you are interested, they are:

Christa Miller (Forensic Focus) – Burnout in DFIR (And Beyond)

Brett Shavers – Only Race Cars Should Burnout

Thom Langford – Drowning, Not Waving

If you are struggling, seek help. Just know that you are not the only one, and there are resources out there to help you, including others in the DFIR community; generally speaking, we are a supportive bunch. Even if your employer doesn’t offer support, the DFIR community will.

One of the responses I saw in the Discord channel indicated that there is a negative connotation around seeking out help for mental health. I understand that because I have worked in environments where expressing mental/emotional distress was seen as a sign of weakness among peers and supervisors. However, I was fortunate enough to find my way into an environment where mental health is taken seriously and when people were in distress (expressed or not), peers and supervisors listened and took action to help. The few responses I saw made me think environments like mine are the exception and not the rule. I hope I am wrong.

The thing is, it doesn’t have to be that way.

What To Do?

I am not a health professional, so I don’t know the answer to the question or if there even IS an answer.

However, I do know mental health is important, in both DFIR and non-DFIR careers. Even for those of us DFIR’ers who are not exposed to child sexual exploitation material on a regular basis, the other major stressors I previously mentioned can have a negative impact on mental health (see Thom’s article above). Our minds are subjected to so much, it would make sense to have someone check it from time to time.

To use Brett Shaver’s car analogy, it would be silly to not take your car in for a maintenance checkup after an extended period of use. Why would you not give your mind the same checkup by someone who is licensed to do so? We do that for our physical bodies (most of us do, anyway), so why not for the mind? Our minds and bodies are symbiotic just like the systems in a car; a change in one can affect the other, good or bad. If your mind starts to break down due to ongoing mental stress, it can have a negative impact on your physical health…just like a breakdown in one system in a car can negatively impact other systems in the car. This impacts overall performance. A breakdown in your mind can have the same effect on your physical health, job performance, personal habits, and interpersonal relationships.

I have been in supportive environments, and am now responsible for not only maintaining that type of environment, but looking after team members’ well-being. Their families have entrusted my organization with their well-being, and my organization has delegated that responsibility to me. Those of you who supervise a DFIR team have the same responsibility, whether you realize it or not. Sure, one more thing to be responsible for, but guess what. You are in THE seat, and this is extremely important.

For those of you who are not supervisors, you should be looking out for your colleagues, and that includes your supervisor. I have tried to establish a relationship with my fellow team members that encourages free flowing communication, regardless of whether it is positive or negative, and I have experienced both. I would like to think they would come to me if they noticed a change in my behavior.

Again, I am not a health professional, and I am not sure there is a one-size-fits-all answer to how an organization effectively deals with mental health issues for DFIR. That being said I thought I would share what my organization does to try and keep a healthy environment for its DF examiners (we have no incident response function). What we do may work for other organizations, it may not, but I do want to show that it can be done.

An Example

The first thing, and I think this probably the most important, is that we have agency buy-in. If we did not have support from our administration, the rest of what we do would not happen. They fully support what we do and they recognize that happy employees are not only productive employees, but employees that are more likely to stay than to leave. What does that support entail? Well, they provide the funding and approve policies. Without those two things, it would have been impossible to do anything. Again, this applies to my organization, which happens to be 400-ish strong (only three of us are DF). If your agency is small and not very bureaucratic, you may have an easier time with this.

Policies. Some may roll their eyes at them, despise them, or completely ignore them. Regardless of you feelings toward them, they work for the purposes here. Our policy requires….requires…that our examiners go see a licensed psychologist at least once a year and the organization pays for the visit. (Update: this is separate from the employee assistance program, or EAP). Having this in the policy puts the agency on the hook, so to speak, and my organization is completely ok with that. Again, they fully support the mission and the employees who carry out that mission. By making the visit mandatory in a policy, it inoculates it (somewhat) from budget shortfalls which we encounter from time to time.

If a DF employee requests to go to see a licensed psychologist after/before their annual visit because they feel they are struggling, we send them, and the organization pays for it, no questions asked. Any examination (regardless of what it is for) can suddenly hit an examiner the wrong way at the wrong time and have a detrimental effect on their mental health. We realize that, thus we do not tell the employee “Can’t this wait until your scheduled visit?” No, we send them as quickly as we can get an appointment. Again, this is separate from EAP.

Along those same lines, we also realize that an examination may not have a contemporaneous emotional effect, and that it can take a while for the emotional distress to manifest itself to the point the examiner realizes there is a problem, or others notice a change. Again, this is why we do not lock them in to a set schedule.

There is a second part of this. Sometimes we carry our work home with us. If we are struggling at work, we can carry that home with us, and that can start to wear on our family members/significant others who live in the home with us. Our policy allows for a DF spouse to go see a licensed psychologist, too. They may need help helping the examiner cope, or they may need to offload what the examiner offloads on them. Just like the examiner, the spouse can go multiple times if needed, and, the agency pays for it.

Meet Our Lady

Who in DFIR doesn’t like dog pictures? Well, this isn’t just any random dog. Meet Lady. She is the therapy K-9 that is attached to our team. Lady is considered a working K-9, just like a K-9 who detects narcotics or explosives, so the usual rules apply to her (e.g. no people food). She is considered an employee; she has an identification badge, a uniform, and an entry in the employee directory.

Just like other working dogs, Lady lives with her handler, who is a member of our DF team. She is a part of our family, and we treat her as such.

Lady came to us by way of the Paws and Stripes program at the Brevard County, Florida Sheriff’s Office. I will not get in to the specifics of that program, but just know she came to us after having undergone four months of training at the program site. We have a separate policy that addresses Lady. It addresses things such as her medical care, food, lodging, grooming, appearance, the person who is responsible for Lady (her handler), and certification requirements. Just as an example, my organization pays for all food and medical care so as long as she is able to serve in her official capacity. In the event she is not able to serve, she retires from service. The Director of my organization has the final say-so about with whom she retires, but, in keeping with standards, she would probably retire with her handler. Once that occurs, the handler absorbs the cost of food, but my organization will continue to pay for medical care for Lady until her death. We believe Lady is around 2 years old (she was rescued from a shelter), so we plan on her being with us for a LONG time.

Lady is a certified therapy K-9, and is certified through the Alliance of Therapy Dogs. You can read more about that organization and the certification requirements here.

In my opinion, this K-9 program is money well-spent. The mental health benefits Lady provides really is incalculable. Not only to the DF examiners, but to the organization as a whole. For the DF examiners she can be a pleasant distraction; whether it’s to take her out to potty, or to just toss a ball or frisbee, she can provide a short, necessary, and welcome distraction from tough examinations. Lady is intuitive, too. She can sense if someone is having a hard time, and happily go apply a wet nose to a leg or hand to get your attention, which gets you out from behind your workstation and not thinking about your exam.

The budget for Lady is modest compared to other costs in my organization. We budget around $1600 (USD) per year, but we have yet to come close to tapping that whole pot of money. If we were to lose an examiner due to mental health issues, we would have to spend time recruiting and hiring (my hourly salary plus the others involved) and training (DFIR training is not cheap) a replacement. From a financial perspective, Lady is “spend a little money up front, save a lot of money later.” By investing in Lady, we invest in the mental health of our examiners.

Here’s a picture of Lady hard at work….or not. I promise she has beds scattered all throughout our work areas (along with toys).

And here is a picture of her when she visited a medical facility over the holidays (periodic therapy visits outside of work are a requirement of her certification).

And the last one (I feel like a parent). One of our team members rides his motorcycle into work when the weather is nice. Lady randomly hopped up there one afternoon (she wasn’t allowed to ride on the bike).

From a supervisory standpoint there are a couple of things that I do to help with mental health. A small thing is rotating examination types. In other words, if an examiner has had a tough examination, I will assign a not-so-tough subject matter examination after that (“not-so-tough”, of course is subjective). For example, if an examiner had a child sexual exploitation examination, I try to assign something else other than a child sexual exploitation to that examiner for their next exam or two. Sometimes, our case queue will not allow for this, but I am monitoring what exam types they are working and doing what I can from that angle.

Another small thing that I do is leave my door open as much as I can, i.e. I have an open door policy. Usually every morning the team stops by the office, coffee in hand, and have a seat. We discuss current examinations and any issues that have risen during those examinations. A lot of times we are trading ideas on ways to overcome those issues. We also discuss other ancillary subjects and non-work related matters, too. I appreciate that communication and exchange of ideas. I typically learn something from those discussions, too. I will note, that this is not a required meeting…it just happens, and it may happen again, spontaneously, throughout the workday.

While I am invested in and appreciative of our daily discussions, these discussions also serve another purpose: I get a chance to observe the team. Is there any change in their mood or behavior that I can detect? Have they said anything that gives me cause for concern? Are they passively expressing some type of emotional distress? Does any change I detect coincide with a current or recent examination they have conducted? I am looking and listening for these things. As I mentioned before, their families have lent them to the citizens of our state via our organization to deal with some of the toughest subject matters in the criminal justice system. I would be remiss if I didn’t take their well-being to heart.

We try to go out for a team dinner, off-site, after hours every so often. The team usually leaves a little early and heads to the location, and I stay behind for a bit and meet them. We’ll discuss a few work-related matters and then we officially go off the clock. Work is done, and so is our discussion of it. I will say that schedules have been all over the place as of late so we are a bit off schedule. This happens.

Encouraging team members to not feel bad when taking time off from work is something I have noticed that I have to do every so often. I usually have to do this when something unexpected arises and causes a team member to request leave on short notice. Life happens…to all of us…at some point during our career. Whether you work in DFIR or not, things will happen outside of your work that will require you to divert your focus and energy from your work to that thing, whatever it is. Diverting like that requires time away from work, and that’s ok. That’s what paid time off (PTO) is for.

Conclusion

I hope readers find this helpful. Mental health in our field is an important subject, and it is one that I don’t think gets talked about enough. If you have any questions about our program or anything else, please feel free to reach out; I am responsive to communication through the site.

Mental health is something that impacts all of us in DFIR. It is important that we recognize that and to take steps to foster environments in which mental health is taken seriously and not dismissed.

Take care of yourselves, and each other.

Two Snaps and a Twist – An In-Depth (and Updated) Look at Snapchat on Android

There is an update to this post. It can be found after the ‘Conclusion’ section.

I was recently tasked with examining a two-year old Android-based phone which required an in-depth look at Snapchat. One of the things that I found most striking (and frustrating) during this examination was the lack of a modern, in-depth analysis of the Android version of the application beyond the tcspahn.db file, which, by the way, doesn’t exist anymore, and the /cache folder, which isn’t really used anymore (as far as I can tell). I found a bunch of things that discussed decoding encrypted media files, but this information was years old (Snapchat 5.x). I own the second edition of Learning Android Forensics by Skulkin, Tyndall, and Tamma, and while this book is great, I couldn’t find where they listed the version of Snapchat they examined or the version of Android they were using; what I found during my research for this post did not really match what was written in their book. A lot of things have changed.

Googling didn’t seem to help either; I just kept unearthing the older research. The closest I got was a great blog post by John Walther that examined Snapchat 10.4.0.54 on Android Marshmallow. Some of John’s post lined up with what I was seeing, while other parts did not.

WHAT’S THE BIG DEAL?

Snapchat averages 190 million users daily, which is just under half of the U.S. population, and those 190 million people send three billion snaps (pictures/videos) daily. Personally, I have the app installed on my phone, but it rarely sees any usage. Most of the time I use it on my kid, who likes the filters that alter his voice or requires that he stick out his tongue. He is particularly fond of the recent hot dog filter.

One of the appealing things about Snapchat is that direct messages (DMs) and snaps disappear after a they’re opened. While the app can certainly be used to send silly, ephemeral pictures or videos, some people find a way to twist the app for their own nefarious purposes.

There has been plenty written in the past about how some traces of activity are actually recoverable, but, again, nothing recent. I was surprised to find that there was actually more activity-related data left behind than I thought.

Before we get started just a few things to note (as usual). First, my test data was generated using a Pixel 3 running Android 9.0 (Pie) with a patch level of February 2019. Second, the version of Snapchat I tested is 10.57.0.0, which was the most current version as of 05/22/2019. Third, while the phone was not rooted, it did have TWRP, version 3.3.0-0, installed. Extracting the data was straight forward as I had the Android SDK Platform tools installed on my laptop. I booted into TWRP and then ran the following from the command line:

adb pull /data/data/com.snapchat.android

That’s it. The pull command dropped the entire folder in the same path as where the platform tools resided.

As part of this testing, I extracted the com.snapchat.android folder five different times over a period of 8 days as I wanted to see what stuck around versus what did not. I believe it is also important to understand the volatility of the data that is provided in this app. I think understanding the volatility will help investigators in the field and examiners understand exactly how much time, if any, they have before the data they are seeking is no longer available.

I will add that I tested two tools to see what they could extract: Axiom (version 3.0) and Cellebrite (UFED 4PC 7.18 and Physical Analyzer 7.19). Both tools failed to extract (parsing not included) any Snapchat data. I am not sure if this is a symptom of these tools (I hope not) or my phone. Regardless, both tools extracted nothing.

TWO SNAPS AND…SOME CHANGE

So, what’s changed? Quite a bit as far as I can tell. The storage location of where some of the data that we typically seek has changed. There are enough changes that I will not cover every single file/folder in Snapchat. I will just focus on those things that I think may be important for examiners and/or investigators.

One thing has not changed: the timestamp format. Unless otherwise noted, all timestamps discussed are in Unix Epoch.

The first thing I noticed is that the root level has some new additions (along with some familiar faces). The folders that appear to be new are “app_textures”, “lib”, and “no_backup.” See Figure 1.

Figure 1. Root level of the com.snapchat.android folder.

The first folder that may be of interest is one that has been of interest to forensicators and investigators since the beginning: “databases.” The first database of interest is “main.db.” This database replaces tcspahn.db as it now contains a majority of user data (again, tcspahn.db does not exist anymore). There is quite a bit in here, but I will highlight a few tables. The first table is “Feed.” See Figure 2.

This table contains the last action taken in the app. Specifically, the parties involved in that action (seen in Figure 2), what the action was, and when the action was taken (Figure 3). In Figure 4 you can even see which party did what. The column “lastReadTimestamp” is the absolute last action, and the column “lastReader” show who did that action. In this instance, I had sent a chat message from Fake Account 1 (“thisisdfir”) to Fake Account 2 (“hickdawg957”) and had taken a screenshot of the conversation using Fake Account 1. Fake Account 2 then opened the message.

The second table is “Friend.” This table contains anyone who I may be my friend. The table contains the other party’s username, user ID, display name, the date/time I added that person as a friend (column “addedTimestamp”), and the date/time the other person added me as a friend (column “reverseAddedTimestamp”). Also seen is any emojis that may be assigned to my friends. See Figures 5, 6, and 7.

Figure 5. Username, User ID, & Display Name.

Figure 6. Friendmojis (Emojis added to my Friends.

Figure 7. Timestamps for when I added friends and when they added me.

Note that the timestamps are for when I originally added the friend/the friend added me. The timestamps here translate back to dates in November of 2018, which is when I originally created the accounts during the creation of my Android Nougat image.

One additional note here. Since everyone is friends with the “Team Snapchat” account, the value for that entry in the “addedTimestamp” column is a good indicator of when the account you’re examining was created.

The next table is a biggie: Messages. I will say that I had some difficulty actually capturing data in this table. The first two attempts involved sending a few messages back and forth, letting the phone sit for a 10 or so minutes, and then extracting the data. In each of those instances, absolutely NO data was left behind in this table.

In order to actually capture the data, I had to leave the phone plugged in to the laptop, send some messages, screenshot the conversation quickly, and then boot into TWRP, which all happened in under two minutes time. If Snapchat is deleting the messages from this table that quickly, they will be extremely hard to capture in the future.

Figure 8 is a screenshot of my conversation (all occurred on 05/30/2019) taken with Fake Account 1 (on the test phone) and Figure 9 shows the table entries. The messages on 05/30/2019 start on Row 6.

Figure 8. A screenshot of the conversation.

Figure 9. Table entries of the conversation.

The columns “timestamp” and “seenTimestamp” are self-explanatory. The column “senderId” is the “id” column from the Friends table. Fake Account 1 (thisisdfir) is senderId 2 and Fake Account 2 (hickdawg957) is senderId 1. The column “feedRowId” tells you who the conversation participants are (beyond the sender). The values link back to the “id” column in the Feed table previously discussed. In this instance, the participants in the conversation are hickdawg957 and thisisdifr.

In case you missed it, Figure 8 actually has two saved messages between these two accounts from December of 2018. Information about those saved messages appear in Rows 1 and 2 in the table. Again, these are relics from previous activity and were not generated during this testing. This is an interesting find as I had completely wiped and reinstalled Android multiple times on this device since the those messages were sent, which leads me to speculate these messages may be saved server-side.

In Figure 10, the “type” column is seen. This column shows the type of message was transmitted. There are three “snap” entries here, but, based on the timestamps, these are not snaps that I sent or received during this testing.

After the “type” column there is a lot of NULL values in a bunch of columns, but you eventually get to the message content, which is seen in Figure 11. Message content is stored as blob data. You’ll also notice there is a column “savedStates.” I am not sure exactly what the entries in the cells are referring to, but they line up with the saved messages.

In Figure 12, I bring up one of the messages that I recently sent.

The next table is “Snaps.” This table is volatile, to say the least. The first data extraction I performed was on 05/22/2019 around 19:00. However, I took multiple pictures and sent multiple snaps on 05/21/2019 around lunch time and the following morning on 05/22/2019. Overall, I sent eight snaps (pictures only) during this time. Figure 13. Shows what I captured during my first data extraction.

Figure 13. I appear to be messing some snaps.

Of the eight snaps that I sent, only six appear in the table. The first two entries in the table pre-date when I started the testing (on 05/21/2019), so those entries are out (they came from Team Snapchat). The first timestamp is from the first snap I sent on 05/22/2019 at 08:24. The two snaps from 05/21/2019 are not here. So, within 24 hours, the data about those snaps had been purged.

On 05/25/2019 I conducted another data extraction after having received a snap and sending two snaps. Figure 14 shows the results.

The entries seen in Figure 13 (save the first two) are gone, but there are two entries there for the snaps I sent. However, there is no entry for the snap I received. I checked all of the tables and there was nothing. I received the snap at 15:18 that day, and performed the extraction at 15:51. Now, I don’t know for sure that a received snap would have been logged. I am sure, however, that it was not there. There may be more testing needed here.

Figure 15 shows the next table, “SendToLastSnapRecipients.” This table shows the user ID of the person I last sent a snap to in the “key” column, and the time at which I sent said snap.

MEMORIES

During the entire testing period I took a total of 13 pictures. Of those 13, I saved 10 of them to “Memories.” Memories is Snapchat’s internal gallery, separate from the phone’s Photos app. After taking a picture and creating an overlay (if desired), you can choose to save the picture, which places it in Memories. If you were to decide to save the picture to your Photos app, Snapchat will allow you to export a copy of the picture (or video).

And here is a plus for examiners/investigators: items placed in Memories are stored server-side. I tested this by signing into Fake Account 1 from an iOS device, and guess what…all of the items I placed in Memories on the Pixel 3 appeared on the iOS device.

Memories can be accessed by swiping up from the bottom of the screen. Figure 16 shows the Snapchat screen after having taken a photo but before snapping (sending) it. Pressing the area in the blue box (bottom left) saves the photo (or video) to Memories. The area in the red box (upper right) are the overlay tools.

Figure 17 shows the pictures I have in my Memories. Notice that there are only 9 pictures (not 10). More on that in a moment.

Figure 17. My memories. It looks like I am short one picture.

The database memories.db stores relevant information about files that have been saved to Memories. The first table of interest is “memories_entry.” This table contains an “id,” the “snap_id,” and the date the snap was created. There are two columns regarding the time: “created_time” and “latest_created_time.” In Figure 18 there is a few seconds difference between the values in some cells in the two columns, but there are also a few that are the same value. In the cells where there are differences, the differences are negligible.

There is also a column titled “is_private” (seen in Figure 19). This column refers to the My Eyes Only (MEO) feature, which I will discuss shortly. For now, just know that the value of 1 indicates “yes.”

(FOR) MY EYES ONLY

I have been seeing a lot of listserv inquires as of late regarding MEO. Cellebrite recently added support for MEO file recovery in Android as of Physical Analyzer 7.19 (iOS to follow), and, after digging around in the memories database, I can see why this would be an issue.

MEO allows a user to protect pictures or videos with a passcode; this passcode is separate from the user’s password for their Snapchat account. A user can opt to use a 4-digit passcode, or a custom alphanumeric passcode. Once a user indicates they want to place a media file in MEO, that file is moved out of the Memories area into MEO (it isn’t copied to MEO).

MEO is basically a private part of Memories. So, just like everything else in Memories, MEO items are also stored server-side. I confirmed this when I signed in to Fake Account 1 from the iOS device; the picture I saved to MEO on the Pixel 3 appeared in MEO on the iOS device. The passcode was the same, too. Snapchat says if a user forgets the passcode to MEO, they cannot help recover it. I’m not sure how true that is, but who knows.

If you recall, I placed 10 pictures in Memories, but Figure 17 only showed 9 pictures. That is because I moved one picture to MEO. Figure 20 shows my MEO gallery.

In the memories database, the table “memories_meo_confidential” contains entries about files that have been placed in MEO. See Figure 21.

Figure 21. MEO table in the memories database.

This table contains a “user_id,” the hashed passcode, a “master_key,” and the initialization vector (“iv”). The “master_key” and “initialization vector” are both stored in base64. And, the passcode….well, it has been hashed using bcrypt (ugh). I will add that Cellebrite reports Physical Analyzer 7.19 does have support for accessing MEO files, and, while I did have access to 7.19, I was not able to tell if it was able to access my MEO file since it failed to extract any Snapchat data.

The “user_id” is interesting: “dummy.” I have no idea what that is referring to, and I could not find it anywhere else in the data I extracted.

The next table is “memories_media.” This table. Does have a few tidbits of interesting data: another “id,” the size of the file (“size”), and what type of file (“format”). Since all of my Memories are pictures, all of the cells show “image_jpeg.” See Figures 22 and 23.

The next table is “memories_snap.” This table has a lot of information in about my pictures, and brings together data from the other tables in this database. Figure 24 shows a column “media_id,” which corresponds to the “id” in the “memories_media” table discussed earlier. There is also a “creation_time” and “time_zone_id” column. See Figure 24.

Figure 24. id, media_id, creation_time, and time zone.

Figure 25 shows the width and height of the pictures. Also note the column “duration.” The value is 3.0 for each picture. I would be willing to be that number could be higher or lower if the media were videos.

Figure 25 also shows the “memories_entry_id,” which corresponds to the “id” column in the “memories_entry” table. There is also a column for “has_location.” Each of the pictures I placed in Memories has location data associated with it (more on that in a moment).

Figure 25. Picture size, another id, and a location indicator.

Figure 26 is interesting as I have not been able to find the values in the “external_id” or “copy_from_snap_id” columns anywhere.

The data seen in Figure 27 could be very helpful in situations where an examiner/investigator thinks there may be multiple devices in play. The column “snap_create_user_agent” contains information on what version of Snapchat created the the snap, along with the Android version and, in my case, my phone model.

The column “snap_capture_time” is the time I originally took the picture and not the time I sent the snap.

Figure 28 shows information about the thumbnail associated with each entry.

Figure 29 is just like Figure 27 in its level of value. It contains latitude and longitude of the device when the picture was taken. I plotted each of these entries and I will say that the coordinates are accurate +/- 10 feet. I know the GPS capabilities of every device is different, so just be aware that your mileage may vary.

Figure 29 also has the column “overlay_size.” This is a good indication if a user has placed an overlay in the picture/video. Overlays are things that are placed in a photo/video after it has been captured. Figure 30 shows an example of an overlay (in the red box). The overlay here is caption text.

If the value in the overlay_size column is NULL that is a good indication that no overlay was created.

Figure 31 shows the “media_key” and “media_iv,” both of which are in base64. Figure 32 shows the “encrypted_media_key” and “encrypted_media_iv” values. As you can see there is only one entry that has values for these columns; that entry is the picture I placed in MEO.

The next table that may be of interest is “memories_remote_operation.” This shows all of the activity taken within Memories. In the “operation” column, you can see where I added the 10 pictures to Memories (ADD_SNAP_ENTRY_OPERATION). The 11th entry, “UPDATE_PRIVATE_ENTRY_OPERATION,” is where I moved a picture into MEO. See Figure 33.

The column “serialized_operation” stores information about the operation that was performed. The data appears to be stored in JSON format. The cell contains a lot of the same data that was seen in the “memories_snap” table. I won’t expand it here, but DB Browser for SQLite does a good job of presenting it.

Figure 34 shows a better view of the column plus the “created_timestamp” column. This is the time of when the operation in the entry was performed.

Figure 34. JSON and a timestamp for the operation.

Figure 35 contains the “target_entry” column. The values in these columns refer to the “id”column in the “memories_entry” table.

To understand the next database, journal, I first have to explain some additional file structure of the com.snapchat.android folder. If you recall all the way back to Figure 1, there was a folder labeled “files.” Entering that folder reveals the times seen in Figure 36. Figure 37 shows the contents of the “file_manager” folder.

The first folder of interest here is “media_package_thumb,” the contents of which can be seen in Figure 38.

Examining the first file here in hex finds a familiar header: 0xFF D8 FF E0…yoya. These things are actually JPEGs. So, I opened a command line in the folder, typed ren *.* *.jpg and BAM: pictures! See Figure 39.

Notice there are a few duplications here. However, there are some pictures here that were not saved to memories and were not saved anywhere else. As an example, see the picture in Figure 40.

Figure 40. A non-saved, non-screenshot picture.

Figure 40 is a picture of the front of my employer’s building. For documentation purposes, I put a text overlay in the picture with the date/time I took it (to accompany my notes). I then snapped this picture to Fake Account 2, but did not save it to Memories, did not save it to my Photos app, and did not screenshot it. However, here it is, complete with the overlay. Now, while this isn’t the original picture (it is a thumbnail) it can still be very useful; one would need to examine the “snap” table in the main database to see if there was any activity around the MAC times for the thumbnail.

The next folder of interest is the “memories_media” folder. See Figure 41.

There are 10 items here. These are also JPEGs. I performed the same operation here as I did in the “media_package_thumb” folder and got the results seen in Figure 42.

These are the photographs I placed in Memories, but the caption overlays are missing. The picture that is MEO is also here (the file staring with F5FC6BB…). Additionally, these are high resolution pictures.

You may be asking yourself “What happened to the caption overlays?” I’m glad you asked. They are stored in the “memories_overlay” folder. See Figure 43.

Just like the previous two folders, these are actually JPEGs. I performed the rename function, and got the results seen in Figure 44. Figure 45 shows the overlay previously seen in Figure 30.

Figure 45. The Megaman overlay from Figure 30.

The folder “memories_thumbnail” is the same as the others, except it contains just the files in Memories (with the overlays). For brevity’s sake, I will just say the methodology to get the pictures to render is the same as before. Just be aware that while I just have pictures in my Memories, a user could put videos in there, too, so you could have a mixture of media. If you do a mass-renaming, and a file does not render, the file extension is probably wrong, so adjust the file extension(s) accordingly.

Now that we have discussed those file folders, let’s get back to the journal database. This database keeps track of everything in the “file_manager” directory, including those things we just discussed. Figure 46 shows the top level of the database’s entries.

Figure 46. First entries in the journal database.

If I filter the “key” column using the term “package” from the “media_package_thumb” folder (the “media_package_thumb.0” files) I get the results seen in Figure 47.

The values in the “key” column are the file names for the 21 files seen in Figure 38. The values seen in the “last_update_time” column are the timestamps for when I took the pictures. This is a method by which examiners/investigators could potentially recover snaps that have been deleted.

WHAT ELSE IS THERE?

As it turns out, there are a few more, non-database artifacts left behind which are located in the “shared_prefs” folder seen in Figure 1. The contents can be seen in Figure 48.

The first file is identity_persistent_store.xml seen in Figure 49. The file contains the timestamp for when Snapchat was installed on the device (INSTALL_ON_DEVICE_TIMESTAMP), when the first logon occurred on the device (FIRST_LOGGED_IN_ON_DEVICE_TIMESTAMP), and the last user to logon to the device (LAST_LOGGED_IN_USERNAME).

Figure 49. identity_persistent_store.xml.

Figure 50. shows the file LoginSignupStore.xml. it contains the username that is logged in.

The file user_session_shared_pref.xml has quite a bit of account data in it, and is seen in Figure 51. For starters, it contains the display name (key_display_name), the username (key_username), and the phone number associated with the account (key_phone).

The value “key_created_timestamp” is notable. This time stamp converts to November 29, 2018 at 15:13:34 (EST). Based on my notes from my Nougat image, this was around the time I established Fake Account 1, which was used in the creation of the Nougat image. This might be a good indicator of when the account was established, although, you could always get that data from serving Snapchat with legal process.

Rounding it out is the “key_user_id” (seen in the Friends table of the main database) and the email associated with the account (key_email).

CONCLUSION

Snapchat’s reputation proceeds it very well. I have been in a few situations where examiners/investigators automatically threw up their hands and gave up after having been told that potential evidence was generated/contained in Snapchat. They wouldn’t even try. I will say that while I always have (and will) try to examine anything regardless of what the general concensus is, I did share a bit of others’ skepticism about the ability to recover much data from Snapchat. However, this exercise has shown me that there is plenty of useful data left behind by Snapchat that can give a good look into its usage.

Update

Alexis Brignoni over at Initialization Vectors noticed that I failed to address something in this post. First, thanks to him for reading and contacting me. 🙂 Second, he noticed that I did not address Cellebrite Physical Analyzer’s (v 7.19) and Axiom’s (v 3.0) ability to parse my test Snapchat data (I addressed the extraction portion only).

We both ran the test data against both tools and found both failed to parse any of the databases. Testing found that while Cellebrite found the pictures I describe in this post, it did not apply the correct MAC times to them (from the journal.db). Axiom failed to parse the databases and failed to identify any of the pictures.

This is not in any way shape or form a knock on or an attempt to single out these two tools; these are just the tools to which I happen to have access. These tools work, and I use them regularly. The vendors do a great job keeping up with the latest developments in both the apps and the operating systems. Sometimes, though, app developers will make a hard turn all of a sudden, and it does take time for the vendors to update their tools. Doing so requires R&D and quality control via testing, which can take a while depending on the complexity of the update.

However, this exercise does bring to light an important lesson in our discipline, one that bears repeating: test and know the limitations of your tools. Knowing the limitations allows you to know when you may be missing data/getting errant readings. Being able to compensate for any shortcomings and manually examine the data is a necessary skillset in our discipline.

Thank you Alexis for the catach and assist!

Ridin’ With Apple CarPlay

I have been picking on Google lately. In fact, all of my blog posts thus far have focused on Google things. Earlier this year I wrote a blog about Android Auto, Google’s solution for unifying telematic user interfaces (UIs), and in it I mentioned that I am a daily CarPlay driver. So, in the interest of being fair, I thought I would pick on Apple for a bit and take a look under the hood of CarPlay, Apple’s foray into automotive telematics.

Worldwide, 62 different auto manufacturers make over 500 models that support CarPlay. Additionally, 6 after-market radio manufacturers (think Pioneer, Kenwood, Clarion, etc.) support CarPlay. In comparison, 41 auto manufacturers (again, over 500 models – this is an increase since my earlier post) and 19 after-market radio manufacturers support Android Auto. CarPlay runs on iPhone 5 and later. It has been a part of iOS since its arrival (in iOS 7.1), so there is no additional app to download (unlike Android Auto). A driver simply plugs the phone into the car (or wirelessly pairs it if the car supports it) and drives off; a wired connection negates the need for a Bluetooth connection. The toughest thing about CarPlay setup is deciding how to arrange the apps on the home screen.

In roughly 5 years’ time CarPlay support has grown from 3 to 62 different auto manufacturers. I can remember shopping for my 2009 Honda (in 2012) and not seeing anything mentioned about hands-free options. Nowadays, support for CarPlay is a feature item in a lot of car sales advertisements. With more and more states enacting distracted driving legislation, I believe using these hands-free systems will eventually become mandatory.

Before we get started, let’s take a look at CarPlay’s history.

Looking in the Rearview Mirror

The concept of using an iOS device in a car goes back further than most people realize. In 2010 BMW announced support for iPod Out, which allowed a driver to use their iPod via an infotainment console in select BMW & Mini models.

iPod Out-1 — Figure 1. iPod Out. The great-grandparent of CarPlay.

iPod Out-2 — Figure 2. iPod Out (Playback).

The iPod connected to the car via the 30-pin to USB cable, and it would project a UI to the screen in the car. iPod Out was baked in to iOS 4, so the iPhone 3G, 3GS, 4, and the 2^nd and 3^rd generation iPod Touches all supported it. While BMW was the only manufacturer to support iPod Out, any auto manufacturer could have supported it; however, it just wasn’t widely advertised or adopted.

In 2012 Siri Eyes Free was announced at WWDC as part of iOS 6. Siri Eyes Free would allow a user to summon Siri (then a year old in iOS) via buttons on a steering wheel and issue any command that one could normally issue to Siri. This differed from iPod Out in that there was no need for a wired-connection. The car and iOS device (probably a phone at this point) utilized Bluetooth to communicate. The upside to Siri Eyes Free, beyond the obvious safety feature, was that it could work with any in-car system that could utilize the correct version of the Bluetooth Hands-Free Profile (HFP). No infotainment center/screen was necessary since it did not need to project a UI. A handful of auto manufacturers signed on, but widespread uptake was still absent.

At the 2013 WWDC Siri Eyes Free morphed in to iOS in the Car, which was part of iOS 7. iOS in the Car can be thought of as the parent of CarPlay, and closely resembles what we have today. There were, however, some aesthetic differences, which can be seen below.

HomeScreen — Figure 3. Apple’s Eddy Cue presenting iOS in the Car (Home screen).

iOS-in-the-Car-integration-Chevy-Spark-MyLink-720x340 — Figure 4. Phone call in iOS in the Car.

dims — FIgure 5. Music playback in iOS in the Car.

Screen Shot 2013-06-10 at 12.59.52 PM — Figure 6. Getting directions.

Screen Shot 2013-06-10 at 2.09.12 PM — Figure 7. Navigation in iOS in the Car.

iOS in the Car needed a wired connection to the vehicle, or so was the general thought at the time. During the iOS 7 beta, switches were found indicating that iOS in the Car could, potentially, operate over a wireless connection, and there was even mention of it possibly leveraging AirPlay (more on that later in this post). Unfortunately, iOS in the Car was not present when iOS 7 was initially released.

The following spring Apple presented CarPlay, and it was later released in iOS 7.1. At launch there were three auto manufactures that supported it: Ferrari, Mercedes-Benz, and Volvo. Personally, I cannot afford cars from any of those companies, so I am glad more manufacturers have added support.

CarPlay has changed very little since its release. iOS 9 brought wireless pairing capabilities to car models that could support it, iOS 10.3 added recently used apps to the upper left part of the screen, and iOS 12 opened up CarPlay to third party navigation applications (e.g. Google Maps and Waze). Otherwise, CarPlay’s functionality has stayed the same.

With the history lesson now over, there are a couple of things to mention. First, this research was conducted using my personal phone, an iPhone XS (model A1920) running iOS 12.2 (build 16E227). So, while I do have data sets, I will not be posting them online as I did with the Android Auto data. If you are interested in the test data, contact me through the blog site and we’ll talk.

Second, at least one of the files discussed (the cache file in the locationd path) is in a protected area of iPhone, so there are two ways you can get to it: jailbreaking iPhone or using a “key” with a color intermediate between black and white. The Springboard and audio data should be present in an iTunes backup or in an extraction from your favorite mobile forensic tool.

Let’s have a look around.

Test Drive

I have been using CarPlay for the past two and a half years. A majority of that time was with an after-market radio from Pioneer (installed in a 2009 Honda), and the last six months have been with a factory-installed display unit in a 2019 Nissan. One thing I discovered is that there are some slight aesthetic differences in how each auto manufacturer/after-market radio manufacturer visually implements CarPlay, so your visual mileage may vary. However, the functionality is the same across the board. CarPlay works just like iPhone.

Figure 8 shows the home screen of CarPlay.

The home screen looks and operates just like iPhone, which was probably the idea. Apple did not want users to have a large learning curve when trying to use CarPlay. Each icon represents an app, and the apps are arranged in rows and columns. Unlike iPhone, creating folders is not an option, so it is easy to have multiple home screens. The icons are large enough to where not much fine motor skill is necessary to press one, which means you probably won’t be hunting for or pressing the wrong app icon very often.

The button in the orange box is the home button. It is persistent across the UI, and it works like the iPhone home button: press it while anywhere and you are taken back to the home screen. The area in the blue box indicates there are two home screens available, and the area in the red box shows the most recently used apps.

Most of the apps should be familiar to iPhone users, but there is one that is not seen on iPhone: the Now Playing app. This thing is not actually an app…it can be thought of more like a shortcut. Pressing it will bring up whatever app currently has control of the virtual sound interface of CoreAudio (i.e. whatever app is currently playing or last played audio if that app is suspended in iPhone’s background).

Swiping left, shows my second home screen (Figure 9). The area in the red box is the OEM app. If I were to press it, I would exit the CarPlay UI and would return to Nissan Connect (Nissan’s telematic system); however, CarPlay is still running in the background. The OEM app icon will change depending on the auto maker. So, for example, if you were driving a Honda, this icon would be different.

IMG_0771 1.jpg — Figure 9. The second batch of apps on the second home screen.

A user can arrange the apps any way they choose and there are two ways of doing this, both of which are like iPhone. The first way is to press and hold an app on the car display unit, and then drag it to its desired location. The second way is done from the screen seen in Figure 10.

The screen in Figure 10 can be found on iPhone by navigating to Settings > General > CarPlay and selecting the CarPlay unit (or units – you can have multiple)…mine is “NissanConnect.” Moving apps arounds is the same here as it is on the display unit (instructions are present midway down the screen). Apps that have a minus sign badge can be removed from the CarPlay home screen. When an app is removed it is relegated to the area just below the CarPlay screen; in Figure 10 that area holds the MLB AtBat app, AudioBooks (iBooks), and WhatsApp. If I wanted to add any relegated apps to the CarPlay home screen I could do so by pushing the plus sign badge. Some apps cannot be relegated: Phone, Messages, Maps, Now Playing, Music, and the OEM app. Everything else can be relegated.

One thing to note here. iOS considers the car to be a USB accessory, so CarPlay does have to abide by the USB Restricted Mode setting on iPhone (if enabled). This is regardless of whether the Allow CarPlay While Locked toggle switch is set to the on position.

The following screenshots show music playback (Figure 11), navigation (Figure 12), and podcast playback (Figure 13).

Messages in CarPlay is a stripped-down version of Messages on iPhone. The app will display a list of conversations (see Figure 14), but it will not display text of the conversations (Apple obviously doesn’t want a driver reading while driving). Instead, Siri is used for both reading and dictating messages.

Phone is seen in Figures 15; specifically, the Favorites tab. The tabs at the top of the screens mirror those that are seen on the bottom in the Phone app on iPhone (Favorites, Recents, Contacts, Keypad, and Voicemail). Those tabs look just like those seen in iPhone.

If I receive a phone call, I can answer it in two ways: pressing the green accept button (seen in Figure 17) or pushing the telephone button on my steering wheel. Answering the call changes the screen to the one seen in Figure 18. Some of the items in Figure 18 look similar to those seen in iOS in the Car (Figure 4).

Most apps will appear like those pictured above, although, there may be some slight visual/functional differences depending on the app’s purpose, and, again, there may be some further visual differences depending on what car or after-market radio you are using.

Speaking of purpose, CarPlay is designed to do three things: voice communication, audio playback, and navigation. These things can be done fairly well through CarPlay, and done safely, which, I believe, is the main purpose. Obviously, some popular apps, such as Twitter or Facebook, don’t work well in a car, so I don’t expect true social media apps to be in CarPlay any time soon if at all (I could be wrong).

Now that we have had a tour, let’s take a look under the hood and see what artifacts, if any, can be found.

Under the Hood

After snooping around in iOS for a bit I came to a realization that CarPlay is forensically similar to Android Auto: it merely projects the apps that can work with it on to the car’s display unit, so the individual apps contain a majority of the user-generated data. Also, like Android Auto, CarPlay does leave behind some artifacts that may be valuable to forensic examiners/investigators, and, just like any other artifacts an examiner may find, these can be used in conjunction with other data sources to get a wholistic picture of a device.

One of the first artifacts that I found is the cache.plist file under locationd. It can be found in the private > var > root > Library > Caches > locationd path. cache.plist contains the times of last connect and last disconnect. I did not expect to find connection times in the cache file of the location daemon, so this was a pleasant surprise. See Figure 19.

Figure 19. Last connect and last disconnect times.

There are actually three timestamps here, two of which I have identified. The timestamp in the red box is the last time I connected to my car. It is stored in CF Absolute Time (aka Mac Absolute Time), which is the number of seconds since January 1, 2001 00:00:00 UTC. The time, 576763615.86389804, converts to April 12, 2019 at 8:06:56 AM (EDT). I had stopped at my favorite coffee shop on the way to work and when I hopped back in the car, I plugged in my iPhone and CarPlay initialized. See Figure 20.

LastConnectTime — Figure 20. Time of last connect.

The time stamp in the green box just under the string CarKit NissanConnect, is a bit deceptive. It is the time I disconnected from my car. Decoding it converts it to April 12, 2019 at 8:26:18 AM (EDT). Here, I disconnected from my car, walked into work, and badged in at 8:27:14 AM (EDT). See Figure 21.

LastDisconnectTime — Figure 21. Time of last disconnect.

The time in the middle, 576764725.40157998, is just under a minute before the timestamp in the green box. Based on my notes, it is the time I stopped playback on a podcast that I was listening to at the time I parked. I also checked KnowledgeC.db (via DB Browser for SQLite) and found an entry in it for “Cached Locations,” with the GPS coordinates being where I parked in my employer’s parking lot. Whether the middle timestamp represents the time the last action was taken in CarPlay is a good question and requires more testing.

The next file of interest here is the com.apple.carplay.plist file. It can be found by navigating to the private > var > mobile > Library > Preferences path. See Figure 22.

The area in the red box is of interest. Here the name of the car that was paired is seen (NissanConnect) along with a GUID. The fact that the term “pairings” (plural) is there along with a GUID leads me to believe that multiple cars can be paired with the same iPhone, but I wasn’t able to test this as I am the only person I know that has a CarPlay capable car. Remember the GUID because it is seen again in discussing the next artifact. For now, see Figure 23.

Figure 23. Main CarPlay setting page in iOS.

Figure 23 shows the settings page just above the one seen in Figure 10. I show this merely to show that my car is labeled “NissanConnect.”

The next file is 10310139-130B-44F2-A862-7095C7AAE059-CarDisplayIconState.plist. It can be found in the private > var > mobile > Library > Springboard path. The first part of the file name should look familiar…it is the GUID seen in the com.apple.carplay.plist file. This file describes the layout of the home screen (or screens if you have more than one). I found other files in the same path with the CarDisplayIconState string in their file names, but with different GUIDs, which causes me to further speculate that multiple CarPlay units can be synced with one iPhone. See Figure 24.

IconList-Plist-1 — Figure 24. CarPlay Display Icon State.

The area in the red and blue boxes represent my home screens. The top-level Item in the red box, Item 0, represents my first home screen, and the sub-item numbers represent the location of each icon on the first home screen. See Figure 25 for the translation.

The area in the blue box in Figure 24 represents my second home screen, and, again, the sub-item numbers represent the location of each icon on the screen. See Figure 26 for the translation.

The entry below the blue box in Figure 24 is labeled “metadata.” Figure 27 shows it in an expanded format.

IconList-Plist-2 — Figure 27. Icon state “metadata.”

The areas in the green and purple boxes indicate that the OEM app icon is displayed, and that it is “Nissan” (seen in Figure 26). The areas in the orange and blue boxes describe how the app icon layout should be (four columns and two rows). The area in the red box is labeled “hiddenIcons,” and refers to the relegated apps previously seen in Figure 10. As it turns out, the items numbers also describe their position. See Figure 28.

Notice that this file did not describe the location of the most recently used apps in CarPlay (the area in the upper left portion of the display screen). That information is described in com.apple.springboard, which is found in the same path. See Figure 29.

RecentlyUsedLayout — Figure 29. Springboard and most recently used apps.

Just like the app icon layout previously discussed, the item numbers for each most recently used app translate to positions on the display screen. See Figure 30 for the translation.

Figure 30. Most recently used apps positions.

The next file is the com.apple.celestial.plist, which is found in the private > var > mobile > Library > Preferences path. This file had a bunch of data in it, but there are three values in this file that are relevant to CarPlay. See Figure 31.

The string in the green box represents what app had last played audio within CarPlay prior to iPhone being disconnected from the car. The area in blue box is self-explanatory (I had stopped my podcast when I parked my car). The item in the red box is interesting. I had been playing a podcast when I parked the car and had stopped playback. Before I disconnected my iPhone, I brought the Music app to the foreground, but did not have it play any music, thus it never took control of the virtual sound interface in CoreAudio. By doing this, the string in the red box was generated. Just to confirm this, I tested this scenario a second time, but did not bring the Music app to the foreground; the value nowPlayingAppDisplayIDUponCarPlayDisconnect was not present in the second plist file. I am sure this key has some operational value, although I am not sure what that value is. If anyone has any idea, please let me know.

As I mentioned earlier in this post, Siri does a lot of the heavy lifting in CarPlay because Apple doesn’t want you messing with your phone while you’re driving. So, I decided to look for anything Siri-related, and I did find one thing…although I will say that this is probably not exclusive to CarPlay. I think this may be present regardless of whether it occurs in CarPlay or not (more testing). In the path private > var > mobile > Library > Assistant there is a plist file named PreviousConversation (there is no file extension but the file header indicates it is a bplist). Let me provide some context.

When I pick up my child from daycare in the afternoons, I will ask Siri to send a message, via CarPlay, to my spouse indicating that my child and I are on the way home, and she usually acknowledges. The afternoon before I extracted the data from my iPhone (04/11/2019), I had done just that, and, after a delay, my spouse had replied “Ok.”

PreviousConversation contains the last conversation I had with Siri during this session. When I received the message, I hit the notification I received at the top of the CarPlay screen, which triggered Siri. The session went as so:

Siri: “[Spouse’s name] said Ok. Would you like to reply?”

Me: “No.”

Siri: “Ok.”

See Figure 32.

The area in the red box is the name of the sender, in this case, my spouse’s (redacted) name. The orange box was spoken by Siri, and the blue box is the actual iMessage I received from my spouse. The purple box is what was read to me, minus the actual iMessage. Siri’s inquiry (about my desire to reply) is seen in Figure 33.

Figure 34 contains the values of the message sender (my spouse). Inside of the red box the field “data” contains the iMessage identifier…in this case, my spouse’s phone number. The field “displayText” is my spouse’s name (presumably pulled from my Contact’s list). Figure 35 has the message recipient information: me.

Figure 35. Message recipient (me) plus timestamp.

Figure 35 also has the timestamp of when the message was received (orange box), along with my spouse’s chat identifier (blue box).

Figure 36 shows Siri’s last response to me before the session ended.

Interesting note: this plist file had other interesting data in it. One thing that I noticed is that each possible response to the inquiry “Would you like to reply?” had an entry in here: “Call” (the message sender), “Yes” (I’d like to reply), and “No” (I would not like to reply). It might be a good research project for someone. 🙂

The next artifact actually comes from a file previously discussed: com.apple.celestial.plist. While examining this file I found something interesting that bears mentioning in this post. My iPhone has never been paired via Bluetooth with my 2019 Nissan. When I purchased the car, I immediately started using CarPlay, so there has been no need to use Bluetooth (other than testing Android Auto). Under the endointTypeInfo key I found the area seen in Figure 37.

The keys in the red box contain the Bluetooth MAC address for my car. I double-checked my Bluetooth settings on the phone and the car, and the car Bluetooth radio was turned off, but the phone’s radio was on (due to my AppleWatch). So, how does my iPhone have the Bluetooth MAC address for my car? I do have a theory, so stay with me for just a second. See Figure 38.

Figure 38 shows the home screen of my iPhone while CarPlay is running. Notice that the AirPlay/Bluetooth indicator is enabled (red box). Based on some great reverse engineering, it was found that any device that uses the AirPlay service will use its MAC address in order to identify itself (deviceid). Now, see Figure 39.

AudioInterfaces — Figure 39. Virtual Audio Interfaces for AirPlay and CarPlay.

Figure 39 shows two files, both of which are in the Library > Audio > Plugins > HAL path. The file on the left is the info.plist file for the Halogen driver (the virtual audio interface) for AirPlay and the file on the right is the info.plist file for the Halogen driver for CarPlay. The plug-in identifiers for each (both starting with EEA5773D) are the same. My theory is that CarPlay may be utilizing AirPlay protocols in order to function, at least for audio. I know this is a stretch as those of us that use AirPlay know that it typically is done over a wireless connection, but I think there is a small argument to be made here. Obviously, this requires more research and testing, and it is beyond the scope of this post.

Conclusion

CarPlay is Apple’s attempt at (safely) getting into your car. It provides a singular screen experience between iPhone and the car, and it encourages safe driving. While a majority of the user-generated artifacts are kept by the individual apps that are used, there are artifacts specific to CarPlay that are left behind. The app icon layout, time last connected and disconnected, and last used app can all be found in these artifacts. There are also some ancillary artifacts that may also be useful to examiners/investigators.

It has been a long time since I really dug around in iOS, and I saw a lot of interesting things that I think would be great to research, so I may be picking on Apple again in the near future.

Android Pie (9.0) Image Is Available. Come Get A Piece!

In continuing the series of created Android images, I’d like to announce an Android Pie (9.0) image is now available for download. Unfortunately, I had to retire the LG Nexus 5X (it topped out at Oreo), so this time I used a Google Pixel 3. The image contains user-populated data within the stock Android apps and 24 non-stock apps. It includes some new, privacy-centered messaging apps: Wickr Me, Silent Phone, and Dust.

As with the Nougat and Oreo images, this one includes robust documentation; however, there are some differences in the files being made available. First, there is no .ufd file. Second, there is no takeout data. It appeared, based on the traffic for the last two images, there was little interest, so I did not get takeout data this time. If enough interest is expressed, I will get it.

Third…and this is a biggie…there are multiple files. The first file, sda.bin (contained within sda.7z), is an image of the entire phone. This file contains all of the partitions of the phone in an unencrypted format…except for the /data partition (i.e. sda21 or Partition 21), which is encrypted. I tried every method I could think of to get a completely unencrypted image, but was unable to do so. I suspect the Titan M chip may have something to do with this but I need to study the phone and Android Pie further to confirm or disprove. Regardless, I am including this file so the partition layout and the unencrypted areas can be studied and examined. I will say there are some differences between Pie’s partition layout and the layout of previous flavors of Android.

The sda.bin file is 64 GBs in size (11 GB compressed), so make sure you have enough room for the decompressed file.

The second file, Google Pixel 3.tar, is the unencrypted /data partition. Combined with the sda.bin file, you have a complete picture of the phone.

And finally, there is a folder called “Messages,” which contains two Excel spreadsheets that have MMS and SMS messages from the Messages app. There were way too many messages for me to type out in the documentation this time, so I just exported them to spreadsheets. I can confirm that both spreadsheets are accurate.

This image is freely available to anyone who wants it for training, education, testing, or research.

Once Android Q gets further along in beta I will began work on an image for it, so, for the time being, this will be it. 🙂

Please note the images and related materials are hosted by Digital Corpora. You can find everything here.

Google Search Bar & Search Term History – Are You Finding Everything?

Search history. It is an excellent way to peer into someone’s mind and see what they are thinking at a particular moment in time. In a court room, search history can be used to show intent (mens rea). There are plenty of examples where search history has been used in court to establish a defendant’s intent. Probably the most gruesome was the New York City Cannibal Cop trial, where prosecutors used the accused’s search history against him. Of course, there is a fine line between intent and protected speech under the First Amendment.

Over the past month and a half I have published a couple of blog posts dealing with Google Assistant and some of the artifacts it leaves behind, which you can find here and here. While poking around I found additional artifacts present in the same area that have nothing to do with Google Assistant: search terms.

While I wasn’t surprised, I was; after all, the folder where this data was found had “search” in the title (com.google.android.googlequicksearchbox). The surprising thing about these search terms is that they are unique to this particular area in Android; they do not appear anywhere else, so it is possible that you or I (or both) could have been missing pertinent artifacts in our examinations (I have missed something). Conducting a search via this method can trigger Google Chrome to go to a particular location on the Internet, but the term used to conduct the search is missing from the usual spot in the History.db file in Chrome.

My background research on the Google Search Bar (as it is now known) found that this feature may not be used as much as, say, the search/URL bar inside Chrome. In fact, there are numerous tutorials online that show a user how to remove the Google Search Bar from Android’s Home Screen, presumably to make more space for home screen icons. I will say, however, that while creating two Android images (Nougat and Oreo), having that search bar there was handy, so I can’t figure out why people wouldn’t use it more. But, I digress…

Before I get started there are a few things to note. First, the data for this post comes from two different flavors of Android: Nougat (7.1.2) and Oreo (8.1). The images can be found here and here, respectively. Second, the device used for each image was the same (LG Nexus 5X), and it was rooted both times using TWRP and Magisk. Third, I will not provide a file structure breakdown here as I did in the Google Assistant blog posts. This post will focus on the pertinent contents along with content markers within the binarypb files. I found the binarypb files related to Google Search Bar activity to contain way more protobuff data than those from Google Assistant, so a file structure breakdown is impractical.

Finally, I thought it might be a good idea to give some historical context about this feature by taking a trip down memory lane.

A Quick Background

Back in 2009 Google introduced what, at the time, it called Quick Search Box for Android for Android 1.6 (Doughnut). It was designed as a place a user could go to type a word or phrase and search not only the local device but also the Internet. Developers could adjust their app to expose services and content to Quick Search Box so returned results would include their app. The neat thing about this feature was that it was contextually/location aware, so, for example, I could type the word “weather” and it would display the weather conditions for my current location. All of this could occur without the need of another app on the phone (depending on the search).

Google Quick Search Box – circa 2009.

Showtimes…which one do you want?

Prior to Google Assistant, Quick Search Box had a vocal input feature (the microphone icon) that could execute commands (e.g. call Mike’s mobile) and that was about it. Compared to today this seems archaic, but, at the time, it was cutting edge.

Yes, I’m listening.

Fast forward three years to 2012’s Jelly Bean (4.1). By that time Quick Search Bar (QSB) had been replaced by Google Now, Google’s search and prediction service. If we were doing Ancestry.com or 23andMe, Google Now would definitely be a genetic relative of Google Search Bar/Google Assistant. The resemblance is uncanny.

Mom, is that you? Google Now in Jelly Bean

The following year, Kit Kat allowed a device to start listening for the hotword “Ok, Google.” The next big iteration was Now on Tap in 2015’s Marshmallow (6.x), and, with the arrival of Oreo (8.x) we have what we now know today as Google Assistant and the Google Search Bar (GSB). Recently in Android Pie (9.x) GSB moved from the top part of the home screen to the bottom.

Google Search Bar/Google Assistant at the bottom in Android Pie (9.x).

As of the Fall of 2018 Nougat and Oreo accounted for over half of the total Android install base. Since I had access to images of both flavors and conducted research on both, the following discussion covers both. There were a few differences between the two systems, which I will note, but, overall, there was no major divergence.

To understand where GSB lives and the data available, let’s review…

Review Time

GSB and Google Assistant are roommates in both Nougat and Oreo; they both reside in the /data/data directory in the folder com.google.android.googlequicksearchbox. See Figure 1.

Figure 1. GSB & Google Assistant’s home in Android.

This folder holds data about searches that are done from GSB along with vocal input generated by interacting with Google Assistant. The folder has the usual suspect folders along with several others. See Figure 2 for the folder listings.

Figure 2. Folder listing inside of the googlequicksearchbox folder.

The folder of interest here is app_session. This folder has a great deal of data, but just looking at what is here one would not suspect anything. The folder contains several binarypb files, which are binary protocol buffer files. These files are Google’s home-grown, XML-ish rival to JSON files. They contain data that is relevant to how a user interacts with their device via Google Assistant and GSB. See Figure 3.

Figure 3. binarypb file (Nougat).

A good deal of the overall structure of these binarypb files differ from those generated by Google Assistant. I found the GSB binarypb files not easy to read compared to the Google Assistant files. However, the concept is similar: there are markers that allow an examiner to quickly locate and identify the pertinent data.

Down in the Weeds

To start, I chose 18551.binarypb in the Nougat image (7.1.2). This search occurred on 11/30/2018 at 03:55 PM (EST). The search was conducted while the phone was sitting on my desk in front of me, unlocked and displaying the home screen. The term I typed in to the GSB was “dfir.” I was presented with a few choices, and then chose the option that took me to the “AboutDFIR” website via Google Chrome. The beginning of the file appears in Figure 4.

Figure 4. Oh hello!

While not a complete match, this structure is slightly similar to that of the Google Assistant binarypb files. The big takeaway here is the “search” in the blue box. This is what this file represents/where the request is coming from. The BNDLs in the red boxes are familiar to those who have read the Google Assistant posts. While BNDLs are scattered throughout these files, it is difficult to determine where the individual transactions occur within the binarypb files, thus I will ignore them for the remainder of the post.

Scrolling down a bit finds the first area of interest seen in Figure 5.

Figure 5. This looks familar.

In the Google Assistant files, there was an 8-byte string that appeared just before each vocal input. Here there is a four-byte string (0x40404004 – green box) that appears before the search term (purple box). Also present is a time stamp in Unix Epoch Time format (red box). The string, 0x97C3676667010000 is read little endian and converted to decimal. Here, that value is 1543611335575.

Figure 6. The results of the decimal conversion.

This time is the time I conducted the search from GSB on the home screen.

Down further is the area seen in Figure 7. The bit in the orange box looks like the Java wrappers in the Google Assistant files. The string webj and.gsa.widget.text* search dfir and.gsa.widget.text has my search term “dfir” wrapped in two strings: “and.gsa.widget.txt.” Based on Android naming schemas, I believe this to be “Android Google Search Assistant Widget” with text. This is speculation on my part as I haven’t been able to find anything that confirms or denies this.

Figure 7. More search information.

The 4-byte string (green box), my search term (purple box), and the time stamp (red box) are all here. Additionally, is the string in the blue box. The string, a 5-byte string 0xBAF1C8F803, is something seen in Google Assistant files. In the Google Assistant files, this string appeared just prior to the first vocal input in a binarypb file, regardless of when, chronologically, it occurred during the session (remember, the last thing chronologically in the session was the first thing in those binarypb files). Here, this string occurs at the second appearance of the search term.

Traveling further, I find the area depicted in Figure 8. This area of the file is very similar to that of the Google Assistant files.

Figure 8. A familar layout.

The 16-byte string ending in 0x12 in the blue box is one that was seen in the Google Assistant files. In those files I postulated this string marked the end of a vocal transaction. Here, it appears to be doing the same thing. Just after that, a BNDL appears, then the 4-byte string in the green box, and finally my “dfir” search term (purple box). Just below this area, in Figure 9, there is a string “android.search.extra.EVENT_ID” and what appears to be some type of identifier (orange box). Just below that, is the same time stamp from before (red box).

Figure 9. An identifier.

I am showing Figure 10 just to show a similarity between GSB and Google Assistant files. In Google Assistant, there was a 16-byte string at the end of the file that looked like the one shown in Figure 8, but it ended in 0x18 instead of 0x12. In GSB files, that string is not present. Part of it is, but not all of it (see the red box). What is present is the and.gsa.d.ssc. string (blue box), which was also present in Google Assistant files.

Figure 10. The end (?).

The next file I chose was 33572.binarypb. This search occurred on 12/04/2018 at 08:48 AM (EST). The search was conducted while the phone was sitting on my desk in front of me, unlocked and displaying the home screen. The term I typed in to the GSB was “nist cfreds.” I was presented with a few choices, and then chose the option that took me to NIST’s CFReDS Project website via Google Chrome. The beginning of the file appears in Figure 11.

Figure 11. Looks the same.

This looks just about the same as Figure 4. As before, the pertinent piece is the “search” in the blue box. Traveling past a lot of protobuff data, I arrive at the area shown in Figure 12.

Figure 12. The same, but not.

Other than the search term (purple box) and time stamp (red box) this looks just like Figure 5. The time stamp converts to decimal 1543931294855 (Unix Epoch Time). See Figure 13.

Figure 13. Looks right.

As before, this was the time that I had conducted the search in GSB.

Figure 14 recycles what was seen in Figure 7.

Figure 14. Same as Figure 7.

Figure 15 is a repeat of what was seen in Figures 8 and 9.

Figure 15. Same as Figures 8 & 9.

While I am not showing it here, just know that the end of this file looks the same as the first (seen in Figure 10).

In both instances, after having received a set of results, I chose ones that I knew would trigger Google Chrome, so I thought there would be some traces of my activities there. I started looking at the History.db file, which shows a great deal of Google Chrome activity. If you aren’t familiar, you can find it in the data\com.android.chrome\app_chrome\Default folder. I used ol’ trusty DB Browser for SQLite (version 3.10.1) to view the contents.

As it turns out, I was partially correct.

Figure 16 shows the table “keyword_search_terms” in the History.db file.

Figure 16. Something(s) is missing.

This table shows search terms used Google Chrome. The term shown, “george hw bush,” is one that that I conducted via Chrome on 12/01/2018 at 08:35 AM (EST). The terms I typed in to GSB to conduct my searches, “dfir” and “nist cfreds,” do not appear. However, viewing the table “urls,” a table that shows the browsing history for my test Google account, you can see when I went to the AboutDFIR and CFReDS Project websites. See Figures 17 and 18.

Figure 17. My visit to About DFIR.

Figure 18. My visit to NIST’s CFReDS.

The column “last_visit_time” stores the time of last visit to the site seen in the “url” column. The times are stored in Google Chrome Time (aka WebKit time), which is a 64-bit value in microseconds since 01/01/1601 at 00:00 (UTC). Figure 19 shows the time I visited AboutDFIR and Figure 20 shows the time I visited CFReDS.

Figure 19. Time of my visit to About DFIR.

Figure 20. Time of my visit to NIST’s CFReDS.

I finished searching the Chrome directory and did not find any traces of the search terms I was looking for, so I went back over to the GSB directory and looked there (other than the binarypb files). Still nothing. In fact, I did not find any trace of the search terms other than in the binarypb files. As a last-ditch effort, I ran a raw keyword search across the entire Nougat image, and still did not find anything.

This could potentially be a problem. Could it be that we are missing parts of the search history in Android? The History.db file is a great and easy place to look and I am certain the vendors are parsing that file, but are the tool vendors looking at and parsing the binarypb files, too?

As I previously mentioned, I also had access to an Oreo image, so I loaded that one up and navigated to the com.google.android.googlequicksearchbox\app_session folder. Figure 21 shows the file listing.

Figure 21. File listing for Oreo.

The file I chose here was 26719.binarypb. This search occurred on 02/02/2019 at 08:48 PM (EST). The search was conducted while the phone was sitting in front of me, unlocked and displaying the home screen. The term I typed in to the GSB was “apple macintosh classic.” I was presented with a few choices but took no action beyond that. Figure 22 shows the beginning of the file in which the “search” string can be seen in the blue box.

Figure 22. Top of the new file.

Figure 23 shows an area just about identical to that seen in Nougat (Figures 5 and 12). My search term can be seen in the purple box and a time stamp in the red box. The time stamp converts to decimal 1549158503573 (Unix Epoch Time). The results can be seen in Figure 24.

Figure 23. An old friend.

Figure 24. Time when I searched for “apple macintosh classic.”

Figure 23 does show a spot where Oreo differs from Nougat. The 4-byte in the green box that appears just before the search term, 0x50404004, is different. In Nougat, the first byte is 0x40, and here it is 0x50. A small change, but a change, nonetheless.

Figure 25 shows a few things that appeared in Nougat (Figures 7 & 14).

Figure 25. The same as Figures 7 & 14.

As seen, the search term is in the purple box, the search term is wrapped in the orange box, the 4-byte string appears in the green box, and the 5-byte string seen in the Nougat and the Google Assistant files is present (blue box).

Figure 26 shows the same objects as those in the Nougat files (Figures 8, 9, & 15). The 16-byte string ending in 0x12, the 4-byte string (green box), my search term (purple box), some type of identifier (orange box), and the time stamp (red box).

Figure 26. Looks familar…again.

While not depicted in this post, the end of the file looks identical to those seen in the Nougat files.

Just like before, I traveled to the History.db file to look at the “keyword_search_terms” table to see if I could find any artifacts left behind. See Figure 27.

Figure 27. Something is missing…again.

My search term, “apple macintosh classic,” is missing. Again. I looked back at the rest of the GSB directory and struck out. Again. I then ran a raw keyword search against the entire image. Nothing. Again.

Out of curiosity, I decided to try two popular forensic tools to see if they would find these search terms. The first tool I tried was Cellebrite Physical Analyzer (Version 7.15.1.1). I ran both images through PA, and the only search terms I saw (in the parsed data area of PA) were the ones that were present in Figures 16 & 27; these terms were pulled from the “keyword_search_terms” table in the History.db file. I ran a search across both images (from the PA search bar) using the keywords “dfir,”“cfreds,” and “apple macintosh classic.” The only returned hits were the ones from the “urls” table in the History.db file of the Nougat image; the search term in the Oreo image (“apple macintosh classic”) did not show up at all.

Next, I tried Internet Evidence Finder (Version 6.23.1.15677). The Returned Artifacts found the same ones Physical Analyzer did and from the same location but did not find the search terms from GSB.

So, two tools that have a good foot print in the digital forensic community missed my search terms from GSB. My intentions here are not to to speak ill of either Cellebrite or Magnet Forensics, but to show that our tools may not be getting everything that is available (the vendors can’t research everything). It is repeated often in our discipline, but it does bear repeating here: always test your tools.

There is a silver lining here, though. Just to check, I examined my Google Takeout data, and, as it turns out, these searches were present in what was provided by Google.

Conclusion

Search terms and search history are great evidence. They provide insight in to a user’s mindset and can be compelling evidence in a court room, civil or criminal. Google Search Bar provides users a quick and convenient way to conduct searches from their home screen without opening any apps. These convenient searches can be spontaneous and, thus, dangerous; a user could conduct a search without much thought given to the consequences or how it may look to third parties. The spontaneity can be very revealing.

Two major/popular forensic tools did not locate the search terms from Google Search Bar, so it is possible examiners are missing search terms/history. I will be the first to admit, now that I know this, that I have probably missed a search term or two. If you think a user conducted a search and you’re not seeing the search term(s) in the usual spot, try the area discussed in this post.

And remember: Always. Test. Your. Tools.

Update

A few days after this blog post was published, I had a chance to test Cellebrite Physical Analyzer, version 7.16.0.93. This version does parse the .binarypb files, although you will get multiple entries for the same search, and some entries may have different timestamps. So, caveat emptor; it will be up to you/the investigator/both of you to determine which is accurate.

I also have had some time to discuss this subject further with Phil Moore (This Week in 4n6), who has done a bit of work with protobuf files (Spotify and the KnowledgeC database). The thought was to use Google’s protoc.exe (found here) to encode the .binarypb files and then try to decode the respective fields. Theoretically, this would make it slightly easier than manually cruising through the hexadecimal and decoding the time manually. To test this, I ran the file 26719.binarypb through protoc.exe. You can see the results for yourself in Figures 28, 29, and 30, with particular attention being paid to Figure 29.

Figure 28. Beginning of protoc output.

Figure 29. Middle part of the protoc output (spaces added for readability).

Figure 30. Footer of the protoc output.

In Figure 28 the “search” string is identified nicely, so a user could easily see that this represents a search, but you can also see there is a bunch of non-sensical data grouped in octets. These octets represent the data in the .binarypb file, but how it lines up with the hexadecimal values/ASCII values is anyone’s guess. It is my understanding that there is a bit of educated guessing that occurs when attempting to decode this type of data. Since protobuf data is serialized and the programmers have carte blanche in determining what key/value pairs exist, the octets could represent anything.

That being said, the lone educated guess I have is that the octet 377 represents 0xFF. I counted the number of 377’s backwards from the end of the octal time (described below) and found that they matched (24 – there were 24 0xFF’s that proceeded the time stamp seen in Figure 23). Again, speculation on my part.

Figure 29 is the middle of the output (I added spaces for readability). The area in the red box, as discovered by Phil, is believed to the be the timestamp, but in an octal (base-8) format…sneaky, Google. The question mark at the end of the string lines up with the question mark seen at the end of each timestamp seen in the figures of this article. The area in the green box shows the first half of the Java wrapper that was discussed and seen in Figure 25. The orange box contains the search string and the last half of the Java wrapper.

Figure 30 shows the end of the protoc output with the and.gsa.d.ssc.16 string.

So, while there is not an open-source method of parsing this data as of this writing, Cellebrite, as previously mentioned, has baked this into the latest version of Physical Analyzer, but care should be taken to determine which timestamp(s) is accurate.

OK Computer…er…Google. Dissecting Google Assistant (Part Deux)

In part two of this article I will be looking at Google Assistant artifacts that are generated when using a device outside of the car (non-Android Auto). Since this post is a continuation of the first, I will dispense with the usual pleasantries, and jump right into things. If you have not read Part 1 of this post (dealing with Google Assistant artifacts generated when using Google Assistant via Android Auto), at least read the last portion, which you can do here. The data (the phone extraction) discussed in both posts can be found here. Just know that this part will not be as long as the first, and will, eventually, compare the Google Assistant artifacts generated in Android Auto to those generated just using the device.

If you don’t feel like clicking over, let’s recap:

A Slight Review

Google Assistant resides in the /data/data directory. The folderis com.google.android.googlequicksearchbox. See Figure 1.

Figure 1. Google Assistant’s home in Android.

This folder also holds data about searches that are done from the Quick Search Box that resides at the top of my home screen (in Oreo). The folder has the usual suspect folders along with several others. See Figure 2 for the folder listings.

Figure 2. Folder listing inside of the googlequicksearchbox folder.

The folder of interest here is app_session. This folder has a great deal of data, but just looking at what is here one would not suspect anything. The folder contains several binarypb files, which I have learned, after having done additional research, are binary protocol buffer files. These files are Google’s home-grown, XML-ish rival to JSON files. They contain data that is relevant to how a user interacts with their device via Google Assistant. See Figure 3.

Figure 3. binarypb files.

Each binarypb file here represents a “session,” which I define as each time Google Assistant was invoked. Based on my notes, I know when I summoned Google Assistant, how I summoned it, and what I did when I summoned it. By comparing my notes to the MAC times associated with each binarypb file I identified the applicable files for actions taken inside of the car (via Android Auto) and those taken outside of the car.

During my examination of the binarypb files that were created during sessions inside of the car, I found similarities between each file, which are as follows:

Each binarypb file will start by telling you where the request is coming from (car_assistant).
What is last chronologically is first in the binarypb Usually, this is Google Assistant’s response (MP3 file) to a vocal input just before being handed off to whatever service (e.g. Maps) you were trying to use. The timestamp associated with this is also at the beginning of the file.
A session can be broken down in to micro-sessions, which I call vocal transactions.
Vocal transactions have a visible line of demarcation by way of the 16-byte string ending in 0x12.
A BNDL starts a vocal transaction, but also further divides the vocal transaction in to small chunks.
The first vocal input in the binarypb file is marked by a 5-byte string: 0xBAF1C8F803, regardless of when, chronologically, it occurred in the session.
Each vocal input is marked by an 8-byte string: While the 5-byte string appears at the first in the binarypb file only (along with the 8-byte string), the 8-byte string appears just prior to each and every vocal input in the file.
When Google Assistant doesn’t think it understands you, it generates different variations of what you said…candidates…and then selects the one it thinks you said.
In sessions where Google Assistant needs to keep things tidy, it will assign an identifier. There does not appear to be any consistency (as far as I can tell) as to the format of these identifiers.
The end of the final vocal transaction is marked by a 16-byte string ending in 0x18.

Visually, sessions via Android Auto can be seen in Figure 4, and vocal transactions can be seen in Figure 5.

Figure 4. Visual resprensetation of a session.

Figure 5. Visual representation of vocal transactions.

One additional notation here. I was contacted by a reader via Twitter and asked about adding byte offsets to Figures 4 and 5. Unfortunately, the byte offsets beyond the header are never consistent. This is due to requests always being different, and, as a result, Google Assistant’s response (whether vocally, by action, or both) are always different. I think the thing to keep in mind here is that there is a structure and there are some markers to help examiners locate this data.

A Deep Dive

To start, I chose 13099.binarypb. This session occurred on 01/28/2019 at 12:41 PM (EST) and involved reading new text messages and dictating a response. The session was initiated by “Ok, Google” with the phone sitting on my desk in front of me while the phone was unlocked and displaying the home screen. The session went like this:

Figure 6 shows the top of the binarypb file. In the blue box is something familiar: the 0x155951 hex value at offset 0x10. This string was also present in the binarypb files generated while inside the car (via Android Auto). In the orange box “opa” appears. This string appears at the top of each binarypb file generated as a result of using Google Assistant outside of the car. I suspect (based on other data seen in these files) that this is a reference to the Opa programming language. This would make sense as I see references to Java, too, which is used throughout Android. Additionally, Opa is aimed at both client-side and server-side operations (Node.js on the server and JavaScript on the client side). Again, this is speculation on my part, but the circumstantial evidence is strong.

Figure 6. Top of 13099.binarypb.

In the red boxes are the oh-so-familiar “BNDL’s.” In the green box the string “com.google.android.googlequicksearchbox” is seen. This is the folder in which the Quick Search Box resides, along with the session files for Google Assistant.

Just below the area in Figure 6 is the area in Figure 7. There are a couple of BNDL’s in this area, along with the area in the orange. This string appears to be indicating this part of the file was caused by a change in the conversation between Google Assistant and myself; “TRIGGERED_BY” and “CONVERSATION_DELTA.” See Figure 7.

Figure 7. A change in conversation triggered this vocal transaction

The area in the blue box is interesting as it is a string that is repeated throughout this session. I suspect…loosely…this is some type of identifier, and the string below it (in Figure 8) is some type of token.

Figure 8. ID with possible token…?

I will stop here for a second. There was a noticeable absence at the top of this file. There was no MP3 data here. A quick scan of this entire file finds no MP3 data at all. Determining whether this is unique to this particular file or systemic trend will require examining other files (later in this article).

After the area in Figure 8 there was quite a bit of protocol buffer data. Eventually, I arrived at the area depicted in Figure 9. In it you can see the identifier from Figure 7 (blue box), a bit more data, and then a time stamp (red box). The value is 0x65148E9568010000, which, when read little endian is 1548697343077 (Unix Epoch Time). Figure 10 shows the outcome using DCode.

Figure 9. Identifier and Unix Epoch Time time stamp.

Figure 10. Time stamp from Figure 9.

The time stamp here is about a minute ahead of when I initiated the session. Remember what I said about the last thing chronologically being the first thing in the file? I suspect the last thing I said to Google Assistant will be the first vocal input data I see. See Figure 11.

Figure 11. Last vocal input of the session.

There is one bit of familiar data in here. If you read the first part of this article you will know that the string in the blue box (0xBAF1C8F803) appeared just before the first vocal input in a binarypb file, which is usually the last vocal input data of the session. It did not appear anywhere else within the file. It appears here, too, in a session outside of the car.

In the orange box is what appears to be some Java data indicating where this session started: “hotword.” The hotword is the trigger phrase for Google Assistant, which, for me is “Ok, Google.” The 8-byte string in the green box (0x010C404000040200) is consistent throughout the file (save one location – discussed later), and, as suspected, my last vocal input that I provided Google Assistant (purple box). A BNDL appears at the end in the red box.

Figure 12 shows some familiar data (from Figures 7 & 8): TRIGGERED_BY, CONVERSATION_DELTA, the identifier (blue box) and what I believe to be some token (red box). Note that the suspected token here matches that seen in Figure 8.

Figure 12. A rehash of Figures 7 & 8.

Figure 13. The identifier again and another time stamp.

After some more protocol buffer data I find the area in Figure 13. It looks the same as the area shown in Figure 9, and the time stamp is the same.

Figure 14 is a somewhat recycled view of what was seen in Figure 11, but with a twist. The Java data which seems to indicate where the query came from wraps the vocal input (“no”); see the orange box. A BNDL is also present.

Figure 14. Vocal input with a Java wrapper.

Also seen in Figure 14 is another time stamp in the red box. The value is 0x65148E9568010000, which is decimal 1548697279859. As before, I used DCode to convert this from Unix Epoch Time to 01/28/2019 at 12:41:23 (EST). This is the time I originally invoked Google Assistant.

Figure 15 shows some more data, and the end of the vocal transaction (see my Part 1 post). This is marked by the velvet:query_state:search_result_id string (purple box) and the 16-byte hex value of 0x00000006000000000000000000000012 (orange box). The string and accompanying hex value are the same ones seen in the binarypb files generated by interaction with Google Assistant via Android Auto.

Figure 15. Data marking the end of the vocal transaction.

Figure 16 shows the start of a new vocal transaction. The BNDL (seen at the bottom of Figure 15, but not marked) is in the red box. Just below it is the 8-byte string in the green box. Note that the last byte is 0x10 and not 0x00 as seen in Figure 11. My vocal input appears in the purple box; this input is what started the session. Just below it is another BNDL. See Figure 16.

Figure 16. New vocal transaction.

The items below the BNDL are interesting. The orange box is something previously seen in this file: TRIGGERED_BY. However, the item in the blue box is new. The string is QUERY_FROM_HOMESCREEN, which is exactly what the phone was displaying when I invoked Google Assistant. The phone was on, unlocked, and I used the hotword to invoke Google Assistant, which leads me to the string in the brown box: “INITIAL_QUERY.” The phrase “read my new text messages” was my original request. This area seems to imply that my phrase was the initial query and that it was made from the home screen. Obviously, there is plenty of more testing that needs to be done to confirm this, but it is a good hypothesis.

Figure 17. A time stamp and a “provider.”

In Figure 17 there is a time stamp (red box): the decimal value is 1548697279878 (Unix Epoch Time) and the actual time is 01/28/2019 at 12:41:19 (EST). Again, this is the time Google Assistant was invoked. The portion in the blue box, while not a complete match, is data that is similar to data seen in Android Auto. I highlighted the whole box, but the area of interest is voiceinteraction.hotword.HotwordAudioProvider /34. In the Android Auto version, the related string was projection.gearhead.provider /mic /mic. In the Part 1 post, I indicated that the /mic /mic string indicated where the vocal input was coming from (my in-car microphone used via Android Auto). Here I believe this string indicates the origin of the Google Assistant invocation is via the hotword, although I am not completely sure about the /34.

The area in the blue box in Figure 18 is new. I have tried to find what the data in the box means or its significance, and I have been unable to do so. In addition to searching the Google developer forums, I pulled the phone’s properties over ADB in order to see if I could determine if the data was referring to the on-board microphone and speaker (ear piece), but the list of returned items did not have any of this data. At this point I have no idea what it means. If someone knows, please contact me and I will add it to this article and give full credit.

Figure 18. Something new.

I had to scroll through some more protocol buffer data to arrive at the area in Figure 18-1. There are several things here: the velvet:query_state:search_result_id with the accompanying 16-byte string ending in 0x12 (brown boxes), BNDLs (red boxes), the 8-byte string just prior to my vocal input (green box), my vocal input (purple box), the TRIGGERED_BY, CONVERSATION_DELTA strings (orange box – my response “yes” was a result in a change in the conversation), and the identifier that I had seen earlier in the file (blue box). Note that while the string in the green box matches the string seen in Figure 11, it differs from the one seen in Figure 18-1. The string in Figure 18-1 ends in 0x10 whereas the string here and Figure 11 both end in 0x00.

Figure 18-1. The end of one vocal transaction and the beginning of another.

Just past the identifier seen in Figure 18-1, there was another string that I suspect is a token. This string starts out the same as the one seen in Figures 8 and 12, but it does differ. See Figure 19.

Figure 19. A new “token.”

Scrolling through more protocol buffer data finds the area seen in Figure 20. Here I find another time stamp (red box). The decoding methodology is the same as before, and it resulted in a time stamp of 01/28/2019 at 12:41:42 (EST). This would have been around the time that I indicated that I wanted to reply to the text messages (by saying “yes”) Google Assistant had read to me. Additionally, the Java string appears (orange box), and the end of the vocal transaction is seen with the velvet:query_state:search_result_id and the accompanying 16-byte string ending in 0x12 (blue boxes).

Figure 20. The end of another vocal transaction.

Figure 21 has my dictated message in it (purple box), along with some familiar data, and a familiar format.

Figure 21. A familiar face.

At the top is a BNDL (red box), the 8-byte string ending in 0x00 (green box), another BNDL (red box), the TRIGGERED_BY, CONVERSATION_DELTA strings (orange box), and the identifier again (blue box). In Figure 22 another “token” is found (red box). This is the same one as seen in Figure 19.

Figure 22. Another “token.”

Yet more protocol buffer data, and yet more scrolling takes me to the area in Figure 23. In the red box is another time stamp. In decimal it is 1548697307562 (Unix Epoch Time), which converts to 01/28/2019 at 12:41:47 (EST). This would have been around the time I dictated my message to Google Assistant. The identifier also appears at the foot of the protocol buffer data (blue box).

Figure 23. Another time stamp.

Figure 24 shows the same data as in Figure 20: the end of a vocal transaction. The orange box contains the Java data, and the blue box contains the velvet:query_state:search_result_id and the accompanying 16-byte string ending in 0x12.

Figure 24. End of another vocal transaction.

Beyond my vocal input (purple box), the area seen in Figure 25 is the same as those seen in Figures 18-1 & 21. I even marked them the same… BNDL (red box), the 8-byte string ending in 0x00 (green box), another BNDL (red box), the TRIGGERED_BY, CONVERSATION_DELTA strings (orange box), and the identifier again (blue box).

Figure 25. The top of another vocal transaction.

Figure 26 shows an area after some protocol buffer data that trailed the identifier in Figure 25. The notable thing here is the time stamp in the red box. It is decimal 1548697321442 (Unix Epoch Time), which translate to 01/28/2019 at 12:42:01 (EST). This would have lined up with when I sent the dictated text message.

Figure 26. Time stamp from “No.”

Figure 27 shows the end of the vocal transaction here. In the orange box is the Java data, with the velvet:query_state:search_result_id and the accompanying 16-byte string ending in 0x12 in the blue box.

Figure 27. The end of a vocal transaction.

Figure 28 looks just like Figures 18-1, 21 & 25. The only difference here is my vocal input (“no”). This was the last thing I said to Google Assistant in this session, so I expect this last portion of the file (save the very end) to look similar to the top of the file.

Figure 28. Look familiar?

Figure 29 contains a time stamp (red box), which appears after a bit of protocol buffer data. It is decimal 1548697343077 (Unix Epoch Time), which converts to 12:42:23 (EST). This is the same time stamp encountered in this session file seen in Figure 9.

Figure 29. The last/first time stamp.

Figure 30 shows the end of the session file with the orange box showing the usual Java data. The end of this file, as it turns out, looks very similar to end of session files generated via Android Auto. Three things are present here that are also present in the end of the Android Auto session files. First, the velvet:query_state:search_result_id and the accompanying 16-byte string ending in 0x18 in the blue box. Second, the 9-byte string, 0x01B29CF4AE04120A10 in the purple box. Third, the string “and.gsa.d.ssc.” is present in the red box.

Figure 30. A familiar ending.

So, right away I see quite a bit of similarities between this session file and the ones generated by Android Auto. In order to have some consistency between these files and those from Android Auto, the next file I examined involved me asking for directions to my favorite coffee joint.

The next file I examined was 13128.binarypb. This session occurred on 01/28/2019 at 12:43 PM (EST) and involved reading new text messages and dictating a response. The session was initiated by “Ok, Google” with the phone sitting on my desk in front of me, unlocked, and displaying the home screen. The session went like this:

The screen switched over to Google Maps and gave me the route and ETA. I did not choose anything and exited Maps.

The top of 13128.binarypb looks identical to 13099.binarypb (Figure 6). See Figure 31.

Figure 31. A familiar sight.

The gang is all here. The string 0x155951 (blue box), “opa” (orange box), com.google.android.googlequicksearchbox (green box), and a couple of BNDL’s (red box).

While no data of interest resides here, I am including Figure 32 just to show that the top of 13128 is just like 13099.

Figure 32. Nothing to see here.

A quick note here: this file is just like 13099 in that there is no MP3 data at the beginning of the file. As before, I scanned the rest of the file and found no MP3 data at all. So, this is a definite difference between the Android Auto and non-Android Auto session files.

Figure 33 is something I had seen in the previous file (see Figure 16), but further down. The blue and orange boxes contain the TRIGGERED_BY and QUERY_FROM_HOMESCREEN strings, respectively. Just like my previous session, this session was started with the phone on, unlocked, and by using the hotword to invoke Google Assistant, which leads me to the string in the red box: “INITIAL_QUERY.” This area seems to imply that whatever vocal input is about to show up is the phrase that was the initial query and that it was made from the home screen.

Figure 33. Query From Home Screen, Triggered By, Launched On, & Initial Inquiry.

Figure 34 looks almost identical to Figure 18-1. The red box contains a time stamp, which is decimal 1548697419294 (Unix Epoch Time). When converted it is 01/28/2019 at 12:43:39 (EST). The blue box contains the string voiceinteraction.hotword.HotwordAudioProvider /49. The /49 is different than the one seen in Figure 18-1, though (/34). Again, I am not sure what this is referring to, and I think it warrants more testing.

Figure 34. The query source and a time stamp.

Scrolling down just a hair finds the area in Figure 35. The orange box contains Java data we have seen before but with a small twist. The string webj and.opa.hotword* search and.opa.hotword, with the twist being “search” in the middle. As seen in the first file, it’s almost as if the term in the middle is being wrapped (my “no” was in wrapped as seen in Figure 14).

Figure 35. Something old and something old.

The area in the red box is the same data seen in Figure 18.

Figure 36 also contains some familiar faces. My vocal input is in the purple box, the 5-byte blue string that usually appears at the first vocal input of the session, 0xBAF1C8F803, is here.

Figure 36. The first vocal input of the session.

An 8-byte string previously seen in 13099 is also here (see Figure 16). Note that this string ends in 0x10. In 13099 all of the 8-byte strings, save one, ended in 0x00. The one that did end in 0x10 appeared with the first vocal input of the session (“read my new text messages”). Here, we see the string ending in 0x10 with the only vocal input of the session. I hypothesize that the 0x10 appears before the first vocal input of the session, with any additional vocal input appearing with the 8-byte string ending in 0x00. More research is needed to confirm, which is beyond the scope of this article.

Figures 37 and 38 shows the same data as seen in Figure 33 and 34.

Figure 37. Same ol’ same ol’.

Figure 38. Same ol’, part deux.

Figure 39 shows the mysterious string with the speaker id (red box) and Figure 40 shows my vocal input inside of a Java wrapper (orange box), which is similar to what was seen in 13099 (Figure 14).

Figure 39. Speaker identifier?

Figure 40. A Java wrapper and a time stamp.

The time stamp seen in Figure 40 is the same as the other time stamps seen in this session file except for the first byte. The other bytes are 0x1E, whereas the byte seen here is 0x08; this causes the decimal value to shift from 1548697419294 to 1548697419272. Regardless, the time here is the same: 01/28/2019 at 12:43:39 PM (EST). The millisecond value is different: 294 versus 272, respectively.

Figure 41 shows the end of the vocal transaction, which is marked by the velvet:query_state:search_result_id and the accompanying 16-byte string ending in 0x12 in the blue box.

Figure 41. The end of the vocal transaction.

The start of a new vocal transaction is seen in Figure 42. The 8-byte value seen in the green box ends with 0x10, which keeps in line with my theory discussed earlier in this article. My vocal input (the only input of the session) is seen in the purple box. A BNDL is seen at the start of the transaction (red box) with another one at the end (red box).

Figure 42. The start of another vocal transaction.

In the interest of brevity, I will say that the next bit of the session file is composed of what is seen in Figures 37, 38, and 39 (in that order). The time stamp is even the same as the one seen in Figure 38. The next area is the last part of the session file as seen in Figure 43.

Figure 43. The end!

If Figure 43 looks familiar to you, that is because it is. I color coded the boxes the same way as I did in Figure 31. Everything that was there is here: the Java data (orange box), the velvet:query_state:search_result_id and the accompanying 16-byte string ending in 0x18 in the blue box, the 9-byte string, 0x01B29CF4AE04120A10 in the purple box, and the string “and.gsa.d.ssc.” is present in the red box.

So What Changed…If Anything?

At the beginning of this article I reviewed some consistencies between the Android Auto session files I examined. After examining he non-Android Auto files, I thought it would be beneficial to revisit those consistencies to see what, if anything changed. The original statements are in italics, while the status here is just below each item.

Each binarypb file will start by telling you where the request is coming from (car_assistant).

This is still correct except “car_assistant” is replaced by “opa” and “googlequicksearchbox.”

What is last chronologically is first in the binarypb file. Usually, this is Google Assistant’s response (MP3 file) to a vocal input just before being handed off to whatever service (e.g. Maps) you were trying to use. The timestamp associated with this is also at the beginning of the file.

This is still correct, minus the part about the MP3 data.

A session can be broken down in to micro-sessions, which I call vocal transactions.

This is still correct.

Vocal transactions have a visible line of demarcation by way of the 16-byte string ending in 0x12.

This is still correct.

A BNDL starts a vocal transaction, but also further divides the vocal transaction in to small chunks.

This is still correct.

The first vocal input in the binarypb file is marked by a 5-byte string: 0xBAF1C8F803, regardless of when, chronologically, it occurred in the session.

This is still correct.

Each vocal input is marked by an 8-byte string: While the 5-byte string appears at the first in the binarypb file only (along with the 8-byte string), the 8-byte string appears just prior to each and every vocal input in the file.

Eh…sorta. While the values in the 8-bytes change between Android Auto and non-Android Auto, there is some consistency within the fact that there is a consistent 8-byte string. Further, the last byte of the 8-byte string in the non-Android Auto version varies depending on whether or not the vocal input is chronologically the first input of the session.

When Google Assistant doesn’t think it understands you, it generates different variations of what you said…candidates…and then selects the one it thinks you said.

Unknown. Because I was in an environment which was quiet, and I was near the phone, Google Assistant didn’t seem to have any trouble understanding what I was saying. It would be interesting to see what would happen if I introduced some background noise.

In sessions where Google Assistant needs to keep things tidy, it will assign an identifier. There does not appear to be any consistency (as far as I can tell) as to the format of these identifiers.

This is correct. In the 13099 file, there were multiple things happening, so an identifier with something that resembled a token was present.

The end of the final vocal transaction is marked by a 16-byte string ending in 0x18.

Still correct.

For those of you that are visual learners, I am adding some diagrams at the end that shows the overall, generalize structure of both a session and a vocal transaction. See Figures 44 and 45, respectively.

Figure 44. Session file.

Figure 45. Vocal transaction.

Conclusion

There is way more work to do here in order to really understand Google Assistant. Phil Moore, of This Week in 4n6 fame, mentioned the Part 1 of this article recently on the This Month in 4N6 podcast, and he made a very accurate statement: Google Assistant is relatively under researched. I concur. When I was researching this project, I found nothing via Google’s developer forums, and very little outside of those forums. There just isn’t a whole lot of understanding about how Google Assistant behaves and what it leaves behind on a device.

Google Assistant works with any device that is capable of running Lollipop (5.0) or higher; globally, that is a huge install base! Additionally, Google Assistant can run on iOS, which adds to the install base, and is a whole other batch of research. Outside of handsets there are the Google Home speakers, on which there has been some research, Android TVs, Google Home hubs, smart watches, and Pixelbooks/Pixel Slates. Google is making a push in the virtual assistant space, and is going all in with Google Assistant (see Duplex). With the all of these devices capable of running Google Assistant it is imperative that practitioners learn how it behaves and what artifacts it leaves behind.

OK Computer…er…Google. Dissecting Google Assistant (Part 1)

A few weeks ago, I posted a blog about some research I conducted on Android Auto, and I mentioned there was some interesting data left behind by Google Assistant when using Android Auto. Based on what I found there, I decided to go further down the virtual assistant rabbit hole to see what I could find.

As far as virtual assistants go, I use Siri. When I had a newborn, Siri was used a lot. In addition to turning on/off lights or playing music, I used Siri to turn on certain appliances (via smart plugs), respond to texts, and make phone calls as my hands were usually holding a baby, trying to do something for/to/with a baby, or I was trying to help my spouse do those things. Siri was really helpful then. Siri is still useful, but, nowadays, my primary use of Siri is in the car. There are times where I still yell at my phone or HomePod to find a TV show, play a song, turn on a light, or answer a quick math question. For other things such as search and typing a message (outside of the car), I’m old fashioned.

I have been fascinated by talking computers/A.I. for a long time. According to my parents, I used to love Knight Rider. It had the Knight Industries Two Thousand (K.I.T.T.) – the snarky, crime-fighting automotive sidekick of the Hoff. As you can see from the GIF above, I also like Star Trek. Captain Kirk, Spock, et al. have been verbally interacting with their computers since the 1960’s. WOPR/Joshua, check. Project 2501, check. The Architect, check. And, the crème de la crème: HAL 9000, check check check.

While researching Google Assistant, I stumbled across an article that had some interesting statistics. In 2017, there was a 128.9% year-over -year increase in the use of voice-activated assistants, with an expected growth of 40% in 2019. Another statistic: 2 out of 5 adults use voice search at least once a day; this was in 2017, so I suspect this number is higher by now. This explosion, in my opinion, started when Alexa arrived. Say what you like about Amazon, but they were smart to open Alexa to developers. Alexa is everywhere and owns around 70% of the virtual assistant market. With Amazon’s recent acquisition of eero (to my dismay), wireless routers, the televisions of the 21^st century, will have Alexa in them. Alexa. Is. Everywhere. I am sure Google will follow, so I feel it is important to understand any artifact Google Assistant may leave behind on a device.

There are three things to be aware of before I get started. First, the data I found resides in the /data/ directory, and, thus, is not easily accessible on newer devices unless the device is rooted, or you have a tool that can access this area. Second, a warning: this post is lengthy, but in order to understand the patterns and data in the three files I examine, it is necessary.

Finally, this will be a two-parter. There was way too much data to cover everything in just one post. This post will examine the data left behind by Google Assistant when used via Android Auto. The second post will examine the data left behind by Google Assistant when used outside of the car (i.e. when I yell at the device).

One final note: this data was generated on a rooted Nexus 5X running Android Oreo (8.1), with a patch date of December 5, 2018. The data used in this article can be found here.

This research is an off-shoot of what I did with Android Auto, so if you want the full backstory, you can read the post here. But…

Let’s Review

Google Assistant resides in the /data/data directory. The folder is com.google.android.googlequicksearchbox. See Figure 1.

Figure 1.

This folder also holds data about searches that are done from the Quick Search Box that resides at the top of my home screen (in Oreo). This box has been lurking, in some fashion or another, since Doughnut, so it has had around 10 years or so to mature. The folder has the usual suspect folders along with several others. See Figure 2 for the folder listings.

Figure 2.

The folder of interest here is app_session. This folder has a great deal of data, but just looking at what is here one would not suspect anything. The folder contains several .binarypb files, which I have learned, after having done additional research, are binary protocol buffer files. These files are Google’s home-grown, XML-ish rival to JSON files. They contain data that is relevant to how a user interacts with their device via Google Assistant. See Figure 3.

Figure 3.

Each .binarypb file here represents a “session,” which I define as each time Google Assistant was invoked. Based on my notes, I know when I summoned Google Assistant, how I summoned it, and what I did when I summoned it. The first time I summoned Google Assistant is represented in the file with the last five digits of 43320.binarypb. Figure 4 shows the header of the file.

Figure 4.

The ASCII “car_assistant” seems to imply this request had been passed to Google Assistant from Android Auto. In each test that I ran in Android Auto, this phrase appeared at the beginning of the file. Additionally, the string in the smaller orange box (0x5951EF) appeared at the beginning of the file at the same byte offset each time (0x11). I hesitate to call this a true “file header,” though.

If you read my Android Auto post, you will know the string in the red box is the start of a MP3 file. You can see the end of the MP3 file in Figure 5.

Figure 5.

The string in the orange box is the marker of the LAME MP3 codec, and the strings in the red boxes in Figures 4 and 5 are what I called “yoda” strings. Seeing these things, I carved from the first yoda (seen in Figure 4), to the last (seen in Figure 5), for a total of 11.1 KB. I then saved the file with no extension and opened it in VLC Player. The following came out of my speakers:

“You’ve got a few choices. Pick the one you want.”

Based on my notes, this was the last phrase Google Assistant spoke to me via Android Auto prior to handing me off to Maps. In this session, I had asked for directions to Starbucks and had not been specific about which one, which caused the returned reply that I had just heard. There was other interesting data in this file, such as the text of what I had dictated to Google Assistant. I began to wonder if it would be possible to determine if there were any patterns, identifying, or contextual data in this file that would be useful to or could act as “markers” for digital forensic practitioners. Using 43320.binarypb as a starting point, I set off to see if I could map this file and the others on which I had taken notes.

Looking at these files in hex and ASCII, I started to notice a bit of a pattern. While there is a difference between interactions in the car (via Android Auto) and outside of the car (yelling at the phone), there are some high-level similarities between these files regardless of how Google Assistant is used. Below, I examine the data that is generated by Google Assistant via Android Auto. The second part of this post will examine the data left behind when outside of the car.

A Deep Dive

I chose 43320.binarypb as my starting point on purpose: there was a single request for directions in this session. I thought the file would be straight forward, and I was right…sorta.

The session was started via Android Auto, and I had invoked Google Assistant via a button on my steering wheel (the phone was connected to the car). The session went like this:

Me: “I need directions to Starbucks.”

// Google Assistant thought for a few seconds //

GA: “You’ve got a few choices. Pick the one you want.”

After that I was handed off to Maps and presented with a few choices. I chose a particular location and route, and then went on my way. Figure 4 shows the top of the file, and I have already mentioned the MP3 data (Figures 4 and 5), so I will skip that portion of the file.

The first area of the file after the MP3 portion was a 4-byte string, BNDL (0x42444C02). Just make note of this for now, because it comes up, a lot. After BNDL there was some information about the version of Android Auto I was running, and, potentially, where the voice input was coming from (/mic /mic); see the red box and orange box, respectively, in Figure 6.

Figure 6.

There is an additional string in there that, if you weren’t paying attention, you would miss as it’s stuck at the end of some repetitive data. I certainly missed it. Take a look at the string in the blue box in Figure 6. The string is 0x30CAF25768010000 (8 bytes) and appears at the end of some padding (please do not judge – I couldn’t come up with a better name for those 0xFF’s). I read it little endian, converted it to decimal, and got a pleasant surprise: 1547663755824. I recognized this format as Unix Epoch Time, so I turned to DCode, and had my Bob Ross-ian moment. See Figure 7.

Side note: I had been trying to find a date/time stamp in this file for two weeks, and, as frequently happens with me, I found it by accident.

Figure 7.

Based on my notes, this is when I had summoned Google Assistant in order to ask for directions: 01/16/2019 at 13:35 (EST).

Next, com.google.android.apps.gsa.shared.search.QueryTriggerType (red box) caught my attention. Just below it was the following: webj gearhead* car_assistant gearhead (green box). If you read my Android Auto post, you will know the title of the folder in which Android Auto resides has “gearhead” in it (com.google.android.projection.gearhead). So, does this indicate Google Assistant was triggered via Android Auto? Maybe…or maybe not. This could be a one off. I filed this away and continued. See Figure 8.

Figure 8.

The next thing is something I mentioned in the Android Auto post. A 5- byte string (0xBAF1C8F803) and an 8-byte string (0x014C604080040200) that appeared just above my actual vocal inquiry. They can be seen in Figure 9: the 5-byte string is in the blue box, the 8-byte string is in the green box, and the voice inquiry is in the top purple box. Take note that there is a variation of what I actually said in the bottom purple box. Also note the BNDL in the red box.

Figure 9.

Below that is data I had seen earlier in the file (in Figure 6): the Android Auto version number, /mic /mic (orange box), the same time stamp I had just seen (purple box) QueryTriggerType with webj gearhead* car_assistant I need directions to Starbucks gearhead (green box). And, there is BNDL again. See Figures 10 and 11.

Figure 10.

Figure 11.

I want to draw attention to two additional things. The first, in Figure 11, is another time stamp in the blue box. This is another Unix time stamp (0xB176F15768010000). This time is 01/16/2019 at 13:34:28 (EST). which is just under 1:30 earlier than the time stamp I had seen previously (when I invoked Google Assistant). This is odd, but the string just below it may have something to do with it: com.google.android.apps.gsa.shared.logger.latency.LatencyEvents. I will say that 13:34 is when I connected the phone to the car and started Android Auto.

The second area is in the red box in Figure 12-1. There you see the following: velvet:query_state_search_result_id (red box) and then a 16-byte string ending in 0x12 (blue box). This area appears in every Google Assistant session I have examined. I have a theory about it but will wait until later to explain. As with BNDL, just put it to the side for the moment.

Figure 12-1.

Figure 12-2.

In Figure 12 -1, you can also see BNDL (yellow box), the 8-byte green box string just prior to my vocal inquiry, and then the inquiry itself (purple box). After a bit of padding, there is BNDL. After that, in Figure 12-2, there is the same data seen in Figures 6 and 10 (orange box) , and…what’s this? Another time stamp (red box)? I did the same thing as before and got another Unix Epoch Time time stamp.

As with the previous time stamp, this one is also prior to the first time stamp I had encountered in the file, although it is within the same minute in which I had invoked Google Assistant. As before, this time stamp appears just before the string that contains LatencyEvents. Does this have something to do with any latency the device is experiencing between it and Google’s servers? Again, I am not sure.

Below this time stamp is a replay of what I had seen in Figure 10 (Figure 12-2 – orange box). The area I discussed in Figure 11 is also present, sans my vocal input (purple). See Figure 13.

Figure 13.

After that last BNDL, the same items I have already discussed are recycled again, and the first time stamp I had found is present again (red box). See Figures 14-1, 14-2, and 14-3.

Figure 14-1.

Figure 14-2.

Figure 14-3.

The very last portion of the file is velvet:query_state:search_result_id (orange box) along with the 16-byte string (purple box); however, there is a small twist: the last byte has changed from 0x12 to 0x18. Just after that string is a 9-byte string, 0x01B29CF4AE04120A10 (blue box). This string appears at the end of each session file I have examined, along with the string and.gsa.d.ssc (red box). See Figure 15.

Figure 15.

So, just in this one file I saw patterns within the file, and recurring strings. Were these things unique to this particular file, or does this pattern span across all of these files?

The next file I chose was 12067.binarypb. As before, there was a single request for directions in this session. This session, I was a bit more specific about the location for which I was looking.

This session was also started via Android Auto, and I had invoked Google Assistant via a button on my steering wheel (the phone was connected to the car). The session went like this:

Me: “Give me directions to the Starbucks in Fuquay Varina.”

// Google Assistant thought for a few seconds //

GA: “Starbucks is 10 minutes from your location by car and light traffic.”

As can be seen in Figure 16, the strings 0x5951EF and car_assistant can be seen at the top of the file. Unlike the previous file, however, there is an additional bit of data here: com.android.apps.gsa.search.core.al.a.au, a BNDL, and ICING_CONNECTION_INITIALIZATION_TIME_MSEC. The “yoda” is also here. See the blue, green, purple, orange, and red box, respectively, in Figure 16.

Figure 16.

Figures 17-1 and 17-2 show the end of the MP3 data, a BNDL, and then some data seen in the 43320.binarypb file: the Android Auto version number, /mic /mic (orange box), a time stamp (red box), and QueryTriggerType with the webj gearhead* car_assistant gearhead (green box). The time stamp here is 0x9FC2CB5B68010000, which, when converted to decimal, is 1547728306847. Just like the previous file, this is also Unix Epoch Time. I used DCode to convert, and got 01/17/2019 at 07:31:46 (EST).

Figure 17-1.

Figure 17-2.

According to my notes, this is the time I invoked Google Assistant, and asked for directions.

Traveling slightly further down I arrive in the area seen in Figure 18. Here I find the 5-byte (blue box) and 8-byte strings (green box) I had seen in 43320.binarypb. Then I see my request (purple box). Also note the lower purple boxes; these appear to be what I said, and variations of what I said. Just before each new variation, there is a number (4, 3, 5, and 9). I will note that the text behind 4 and 5 differ only by the period at the end of the request. I suspect that these numbers are assigned to each variation to keep tabs on each; however, I am not sure why. There is also a BNDL at the end of this area (red box).

Figure 18.

Just below the requests I found some familiar information (Figures 19-1 and 19-2). The Android Auto version number, /mic /mic (purple box), a time stamp (orange box), and QueryTriggerType with the webj gearhead* car_assistant gearhead (green box) are all here. The time stamp here is the same as the previous one. There is an additional piece of data here; just past the webj gearhead* car_assistant string is the 4give me directions to the Starbucks in Fuquay Varina gearhead (blue box). There is also a BNDL at the end (red box).

Figure 19-1.

Figure 19-2.

Below the area in Figure 19-2, there is a time stamp (Figure 20) shown in a blue box. The string (0xBC32C65B68010000) results in a Unix Epoch Time (1547727942332) of 01/17/2019 at 07:25:42 (EST), which is just under six minutes prior to my invocation of Google Assistant. This time stamp appears just before com.google.android.apps.gsa.shared.logger.latency.LatencyEvents again. This time coincided with me starting a podcast through Android Auto.

Figure 20.

Below the time stamp, the velvet:query_state_search_result_id appears again in Figures 21-1 and 21-2, along with the 16-byte string ending in 0x12 (green box) and a BNDL, the 8-byte string, and then my vocal inquiries and their variations, and another BNDL. See the red, green, blue, purple, and orange boxes, respectively.

Figure 21-1.

Figure 21-2.

Just after the BNDL is the information about the Android Auto version I was using, the /mic /mic string (orange box), and a Unix Epoch Time time stamp (red box). This one is the same as the first one I had seen in this file (the time I invoked Google Assistant). See Figure 22.

Figure 22.

Below that are some new things. First, the text of the MP3 file at the beginning of this file (purple box). Second, a string that fits a pattern that I see in other files: xxxxxxxxxxxx_xxxxxxxxx (green box). The content of the string is different, but, most of the time, the format is 12 characters underscore 12 characters. I am not sure what these are, so if any reader knows, please let me know so I can add it here (full credit given). For the purposes of this article I will refer to it as an identifier string.

Also present is the URL for the location I asked for in Google Maps (orange box), and another identifier string (yellow box). Beyond that, is the velvet:query_state_search_result_id string, along with the 16-byte string ending in 0x18 (red box), the 9-byte string (0x01B29CF4AE04120A10 – blue box), and the string and.gsa.d.ssc (yellow box). See Figures 23-1 and 23-2.

Figure 23-1.

Figure 23-2.

So, for those keeping score, let’s review. While each request was slightly different, there were some consistencies between both files. The format, in particular, was fairly close:

The beginning of the file (the 3-byte 0x5951EF string and “car_assistant).
The MP3 audio at the beginning of the file which contains the last audio interaction prior to being sent to a different app (Maps).
BNDL
Android Auto Version along with the /mic /mic
The date/time stamp of when Google Assistant is invoked, which appears just after some padding (0xFF).
A 5-byte string (0xBAF1C8F803) that appears just before the vocal input appears the first time in a file. This string only appears here, and does not appear elsewhere.
An 8-byte string (0x014C604080040200) that appears just before the vocal input, regardless of where it appears within the file.
Text of the vocal input.
BNDL
Android Auto Version along with the /mic /mic
Another date/time stamp of when Google Assistant was invoked (same as the first).
The string webj gearhead* car_assistant <my vocal input> gearhead (what I actually said)
BNDL
What I have decided to call a “latency time stamp,” although, it may indicate the last time any activity was done via Android Auto (including starting Android Auto) prior to the invocation of Google Assistant.
The velvet:query_state:search_result_id string appears along with the 16-byte string ending in 0x12.
Items 7, 8, 9, 10, and 11 recycle.
The velvet:query_state:search_result_id string appears along with the 16-byte string ending in 0x18, which appears at the end of the file.
The 9-byte string 0x01B29CF4AE04120A10 after Item 17.
The string gsa.d.ssc that appears after Item 18.

There is some divergence in the files. In 43320, the items 7, 8, 9, 10, and 11 recycle a second time, whereas they recycle only once in 12067, and it also contains an extra latency time stamp that was not present in 12067. Additionally, 12067 contains some extra data at the end of the file, specifically, the text of the MP3 file at the start of the file, an identifier string, a URL for Maps, and another identifier string.

File 12067 also had some extra data at the beginning that did not appear in 43320.

I also used Android Auto to test sending and receiving messages, and the file that represents that test is 22687.binarypb. There were three sessions on 01/27/2019 The first session, which started at 14:16 (EST) went as follows:

About two minutes later at 14:16, a second session was started. It went as follows:

About 3 minutes later (14:21 EST) I asked for directions using the same method as before (invocation via a button on my steering wheel). The session went as follows:

The first thing I notice is there is a single binarypb file for 01/27/2019 (22687), even though there were three sessions. Inspection of the file finds the 3-byte string, 0x5951EF, is present along with car_assistant string. There is also a “yoda.” See the orange, blue, and red boxes, respectively in Figure 24. I carved from the yoda in Figure 24 to the end of the padding in Figure 25 (orange box).

Figure 24.

Figure 25.

The following came out of my computer speakers:

“Smithfield Chicken and BBQ is 51 minutes from your location by car and light traffic.”

Now, this is interesting. The first two sessions, which started at 14:16 and 14:18, did not include anything regarding directions. The third session at 14:21 did involve directions. I wonder if the fact that the three sessions were so close together that Google Assistant/Android just made one binarypb file to encompass all three sessions. That would require more testing to confirm (or disprove) but is beyond the scope of this exercise and article.

Figures 26-1 and 26-2 shows the end of the MP3 data and some familiar data: the Android Auto version information, /mic /mic, and a time stamp. It also shows the QueryTriggerType and the webj gearhead* car_assistant gearhead string. See the blue, orange, red, purple, respectively. The time stamp here is 0x38CCC29068010000. I converted to decimal (1548616911928), fired up DCode and got 01/27/2019 at 14:21:51 (EST). This time is when I started the session in which I asked for directions.

Figure 26-1.

Figure 26-2.

Below that is some more familiar data. The 5-byte string (0xBAF1C8F803) and 8-byte string (0x014C604080040200) appear (blue and green boxes in Figure 27-1), and there is the vocal input from my request for directions (that occurred roughly three minutes later). There are also variations of the actual vocal input; each variation is designated by a letter (J, K, O, P, and M) (purple boxes). After the variations, is the Android Auto version string, the /mic /mic string, and the same timestamp from before (orange and red boxes) (Figure 27-2).

Figure 27-1.

Figure 27-2.

The QueryTriggerType (red box) appears along with the webj gearhead* car_assistant J get me directions to the Smithfield Chicken & BBQ in Warsaw North Carolina gearhead (green box). A BNDL appears (blue box), and then another time stamp (purple). The byte string is 0x171DBC9068010000, and, in decimal is 1548616473879. This converts to 01/27/2019 at 14:14 (EST), which is the time I hooked the phone to the car and started Android Auto. See Figure 28.

Figure 28.

After that data is the velvet:query_state:search_result_id string, the accompanying 16-byte string ending in 0x12 (orange box), and a BNDL (blue box). The 8-byte string (0x014C604080040200) appears (green box), my vocal input that started the first session (“read my newest text message” – purple box), and then a BNDL (blue box). After that it is the Android Auto version, /mic /mic (yellow box), and a timestamp (red box). See Figures 29-1 and 29-2. The time stamp here is 0x0385BD9068010000, which is decimal 1548616566019. When converted, it is 01/27/2019 at 14:16:06 (EST), which is the time of the first invocated session.

Figure 29-1.

Figure 29-2.

Also in Figure 29-2 is the QueryTriggerType and the webj gearhead* car_assistant (dark purple box) string.

Figure 30 has some new data in it. A string appears that, while not completely similar, is something I had seen before. It appears to be an identifier string: GeQNOXLPoNc3n_QaG4J3QCw. This is not 12 characters underscore 12 characters, but it is close. Right after the identifier string is my vocal input “read my new text message.” See the red box, and blue box, respectively in Figure 30.

Figure 30.

Figures 31-1 and 31-2 shows the two new text messages that were identified. See the blue and red boxes.

Figure 31-1.

Figure 31-2.

Scrolling down a bit I find another identifier string: eQNOXLPoNc3n_QaG4J3QCw (red box). This identifier is the same as the first one, but without the leading “G.” After this identifier is the velvet:query_state:search_result_id and the accompanying 16-byte string ending in 0x12 (orange box). A BNDL appears at the end (green box). See Figure 32.

Figure 32.

Next up is the 8-byte string (0x014C604080040200), and my next vocal input “read it.” Just below my vocal input is the Android Auto version information, /mic /mic, and a time stamp. Just below the time stamp is the QueryTriggerType and the webj gearhead* car_assistant gearhead strings (not pictured). See the blue, orange, red, and purple boxes, respectively in Figures 33-1 and 33-2. The time stamp here is 0xD796BD9068010000. I converted it to decimal (1548616570583), fired up DCode and got 01/27/2019 at 14:16:10 (EST). While I was not keeping exact time, this would seem to be when Google Assistant asked me whether or not I wanted to read the chat message from Josh Hickman.

Figure 33-1.

Figure 33-2.

There is another identifier string further down the file: GeQNOXLPoNc3n_QaG4J3QCw and just below it my vocal input “read my newest text message.” See the blue and red boxes, respectively, in Figure 34. This is interesting. Could it be that Google Assistant is associating this newest vocal input (“read it”) with the original request (“read my newest text message”) by way of the identifier string in order to know that the second request is related to the first? Maybe. This would definitely require some additional research if the case.

Figure 34.

Figures 35 and 36 show the text messages that were new when the request was made.

Figure 35.

Figure 36.

After some gobbly-goo, I found another identifier string: gwNOXPm3FfKzggflxo7QDg (red box). This format is completely different from the previous two I had seen. Maybe this is an identifier for the vocal input “read it.” Maybe it’s a transactional identifier…I am not sure. See Figure 37.

Figure 37.

In Figure 37 you can also see the velvet:query_state:search_result_id and the 16-byte string ending in 0x12 (orange box), a BNDL, (blue box) the 8-byte string (green box), my next vocal input (purple box), and another BNDL.

Figure 38 shows familiar data: Android Auto version, /mic /mic (green box), and a time stamp: 0x27BABD9068010000 (red box). This converts to 1548616579623 in decimal, and 01/27/2019 at 14:16:19 in Unix Epoch Time. As with the previous request, I wasn’t keeping exact time, but this would probably line up with when I said “Go on to the next one.”

Figure 38.

Figure 39 shows the QueryTriggerType string along with webj gearhead* car_assistant string.

Figure 39.

Figure 40 shows that identifier string again, and the vocal input that kicked off this session “read my newest text message.” I am beginning to suspect this is actually some type of transactional identifier to associate “go on to the next one” with “read my newest text message.”

Figure 40.

Figures 41 and 42 show the new text messages.

Figure 41.

Figure 42.

There is another identifier string in Figure 43: jwNOXMv_Ne_B_QbewpK4CQ (blue box). This format is completely new compared to the previous ones. Additionally, the velvet:query_state:search_result_id and the 16-byte string ending in 0x12 (orange box) appears, a BNDL (red box), along with the 8-byte string (green box) my next vocal input (purple box), and a BNDL.

Figure 43.

Figure 44 shows the Android Auto version, /mic /mic (blue box), and another time stamp (red box). This time stamp is 0xC1ECBD9068010000, which converts to 1548616592577. This is 01/27/2019 at 14:16:32 (EST). This probably coincides with my vocal input “That’s it for now.”

Figure 44.

Figure 45 has the QueryTriggerType and webj gearhead* car_assistant.

Figure 45.

Figure 46 shows a few things. The first is the velvet:query_state:search_result_id and the 16-byte string ending in 0x12 (orange box). The second thing is another identifier string, MgNOXNWTAtGp5wL03bHACg (blue box). As before, this format does not match anything I have seen previously.

The third, and the most interesting part, is the start of the second session. The only dividing line here is the velvet:query_state:search_result_id and the 16-byte string ending in 0x12, and BNDL (red box). The green box is the 8-byte string, and the purple box contains my vocal input, “read my newest text messages.” The purple boxes below are variations of what I said.

Figure 46.

Figure 47 shows the Android Auto version string, the /mic /mic string (blue box), and another time stamp (red box). This time the stamp is 0x8C28C09068010000. This converts to 1548616738956 in decimal, which is 01/27/2019 at 14:18:58 (EST) in Unix Epoch Time, which is the time I invoked Google Assistant for the second session.

Figure 47.

The next string that appears is the QueryTriggerType and webj gearhead* car_assistant strings. See Figure 48.

Figure 48.

The next string is another identifier string. This time, it is associated with my newest vocal input: “read my newest text messages.” (blue box) The string is GJgROXL2qNeSIggfk05CQCg (green box). See Figure 49.

Figure 49.

Figures 50 and 51 show the text messages.

Figure 50.

Figure 51.

The next thing I see is a unique identifier string: JgROXL2qNeSIggfk05CWCg (orange box). This string is the same as the previous one (in Figure 49), but without the leading “G.” This behavior is the same that I saw in the first session. Beyond that there is the velvet:query_state:search_result_id and the 16-byte string ending in 0x12 (blue box), a BNDL (red box), the 8-byte string (green box), and my next vocal input, “hear it” (purple box), and another BNDL. See Figure 52.

Figure 52.

Figure 53 shows the Android Auto version string, the /mic /mic string (blue box), and another time stamp (red box). This time the stamp is 0x153AC09068010000. This converts to 1548616743445 in decimal, which is 01/27/2019 at 14:19:03 (EST) in Unix Epoch Time, which would coincide with my vocal input “hear it.”

Figure 53.

The next string that appears is the QueryTriggerType and webj gearhead* car_assistant strings. See Figure 54.

Figure 54.

Scrolling a bit finds an identifier string I have seen before: GJgROXL2qNeSIggfk05CQCg (green box). This is the first identifier seen in this session (the second one). Just below it is the vocal input that started this session: “read my newest text messages” (red box). See Figure 55.

Figure 55.

Figures 56 and 57 show the messages that were new.

Figure 56.

Figure 57.

Figure 58 shows a pattern I have seen before. First is another identifier string: MAROXPPhAcvn_QaPpI24BA (orange box). The second and third are velvet:query_state:search_result_id and the 16-byte string ending in 0x12 (blue box). There is another BNDL (red box), the 8-byte string (green box), my next vocal input (purple box), “I’ll reply”, and another BNDL. Also note the variations of what I said below my actual input (lower purple boxes).

Figure 58.

Figure 59 shows the Android Auto version string, the /mic /mic string (blue box), and another time stamp (red box). This time the stamp is 0xD85CC09068010000. This converts to 1548616752344 in decimal, which is 01/27/2019 at 14:19:12 (EST) in Unix Epoch Time, which would coincide with my vocal input “I’ll reply.”

Figure 59.

Figure 60 shows the next string that appears is the QueryTriggerType and webj gearhead* car_assistant strings.

Figure 60.

The next thing of interest is what is seen in Figure 61. There is another identifier string, GPQROXMz3L6qOggfWpKeoCw (blue box). This is not a string we have seen before. Just below it is the vocal input that started this session (red box).

Figure 61.

I had to scroll quite a bit through some Klingon, but eventually I got to the area in Figure 62. The red box shows another identifier string: PQROXMz3L6qOggfWpKeoCw. This is the same string that we saw in Figure 61, sans the leading “G.” Again, this behavior is a pattern that we have seen in this particular file. It causes my suspicion to grow that it is some type of transactional identifier that keeps vocal input grouped together.

Figure 62.

The second thing is velvet:query_state:search_result_id and the 16-byte string ending in 0x12 (blue box). In Figure 63, there is another BNDL (red box), the 8-byte string (green box), and my next vocal input (the dictated message – purple box). Also note the variations of what I said below my actual input (lower purple boxes). Note that each variation is delineated by a character: B, =, and >.

Figure 63.

Figure 64 shows the Android Auto version string, the /mic /mic string (blue box), and another time stamp (red box). This time the stamp is 0x2192C09068010000. This converts to 1548616765985 in decimal, which is 01/27/2019 at 14:19:25 (EST) in Unix Epoch Time, which would coincide with my dictation of a message to Google Assistant.

Figure 64.

Figure 65 shows the next string that appears is the QueryTriggerType and webj gearhead* car_assistant strings.

Figure 65.

The next thing of interest is what is seen in Figure 66. There is an identifier string that we have seen before, GPQROXMz3L6qOggfWpKeoCw (blue box), and the initial vocal input that started this session (green box). Again, I am beginning to think this is a method of keeping vocal inputs grouped within the same session.

Figure 66.

Scrolling through yet more Klingon, I find the area shown in Figure 67. The blue box shows another identifier string: RwROXPzPAcG5gge9s5n4DQ (red box). This is a new identifier. The velvet:query_state:search_result_id string and the 16-byte string ending in 0x12 (orange box) are also present. There is another BNDL (blue box), the 8-byte string (green box), my next vocal input (purple box), “send it”, and another BNDL.

Figure 67.

Figure 68 shows the Android Auto version string, the /mic /mic string (blue box), and another time stamp (red box). This time the stamp is 0x16BAC09068010000. This converts to 1548616776214 in decimal, which is 01/27/2019 at 14:19:36 (EST) in Unix Epoch Time, which would coincide with my instructing Google Assistant to send the message I dictated.

Figure 68.

Figure 69 shows the next string that appears is the QueryTriggerType and webj gearhead* car_assistant strings.

Figure 69.

Figure 70 shows an identifier string, UQROXJ6aGKixggfh64qYDg (orange box), which is new. Just below it is the velvet:query_state:search_result_id string and the 16-byte string ending in 0x12 (blue box) are also present. There is another BNDL (red box), the 8-byte string (green box). Below the 8-byte string is the vocal input that started the third session: “get me directions to the Smithfield Chicken & BBQ in Warsaw North Carolina” (purple box). Figure 71 shows the variations of my vocal input, which are identified by J, K, O, P, and M (purple boxes). Below that is a BNDL. This is the same data seen in Figure 27.

Figure 70.

Figure 71.

Figure 72 shows the Android Auto version string, the /mic /mic string (blue box), and another time stamp (red box). This time the stamp is 0x38CCC29068010000. This converts to 1548616911928 in decimal, which is 01/27/2019 at 14:21:51 (EST) in Unix Epoch Time. This is the same time stamp seen in Figure 26.

Figure 72.

Figure 73 shows the next string that appears is the QueryTriggerType and webj gearhead* car_assistant strings.

Figure 73.

Scrolling down oh so slightly I find the text of the MP3 file at the beginning of the file (blue box), and the Google Maps URL for the location for which I had asked for directions (green box). See Figure 74. Figure 75 shows another identifier string, 1wROXOOFJc7j_Aa72aPAB (orange box). After the identifier string is the velvet:query_state:search_result_id string. Additionally, the string with the 16-byte string ending in 0x18 (green box), the 9-byte string (0x01B29CF4AE04120A10 – blue box), and the string and.gsa.d.ssc (red box) appear.

Figure 74.

Figure 75.

Comparisons

So, what is the final score? If you’re still reading, let’s recap, and include what we found in the 22687.binarypb file. The differences are in italics:

The beginning of the file (the 3-byte 0x5951EF string and “car_assistant”).
The MP3 audio at the beginning of the file which contains the last audio interaction prior to being sent to a different app (Maps).
BNDL
Android Auto Version along with the /mic /mic
The date/time stamp of when Google Assistant is invoked, which appears just after some padding (0xFF). In the third file, 22687, the time stamp is the time for the third session.
A 5-byte string (0xBAF1C8F803) that appears just before the vocal input appears the first time in a file. This string only appears here, and does not appear elsewhere. In the third file, 22687, this appeared before the first vocal input, which, as it turns out, is the vocal input that started the third session.
An 8-byte string (0x014C604080040200) that appears just before the vocal input, regardless of where and how many times it appears within the file.
Text of the vocal input.
BNDL
Android Auto Version along with the /mic /mic
Another date/time stamp of when Google Assistant was invoked (same as the first).
The string webj gearhead* car_assistant <my vocal input> gearhead (what I actually said). This item only appeared once in 22687 (the inquiry asking for directions).
BNDL
What I have decided to call a “latency time stamp,” although, it may indicate the last time any activity was done via Android Auto (including starting Android Auto) prior to the invocation of Google Assistant. In 22687, this only happened once.
The velvet:query_state:search_result_id string appears along with the 16-byte string ending in 0x12.
Items 7, 8, 9, 10, and 11 recycle.
The velvet:query_state:search_result_id string appears along with the 16-byte string ending in 0x18, which appears at the end of the file.
The 9-byte string 0x01B29CF4AE04120A10 after Item 17.
The string gsa.d.ssc that appears after Item 18.

Conclusions

I feel comfortable enough at this point to draw a few conclusions based on my observations up to this point.

Each binarypb file will start by telling you where the request is coming from (car_assistant).
What is last chronologically is first in the binarypb file. Usually, this is Google Assistant’s response (MP3 file) to a vocal input just before being handed off to whatever service (e.g. Maps) you were trying to use. The timestamp associated with this is also at the beginning of the file.
A session can be broken down in to micro-sessions. I will call them vocal transactions.
Vocal transactions have a visible line of demarcation by way of the 16-byte string ending in 0x12.
A BNDL starts a vocal transaction, but also further divides the vocal transaction in to small chunks.
The first vocal input in the binarypb file is marked by a 5-byte string: 0xBAF1C8F803, regardless of when, chronologically, it occurred in the session.
Each vocal input is marked by an 8-byte string: While the 5-byte string appears at the first in the binarypb file only, the 8-byte string appears just prior to each vocal input.
When Google Assistant doesn’t think it understands you, it generates different variations of what you said…candidates…and then selects the one it thinks you said. You can see thise in these files.
In sessions where Google Assistant needs to keep things tidy, it will assign an identifier to vocal transactions. There does not appear to be any consistency (as far as I can tell) as to the format of these identifiers.
The end of the final vocal transaction is marked by a 16-byte string ending in 0x18.

Figure 76 shows a visual version of the session files, over all, and Figure 77 shows the vocal transactions portion in more detail.

Figure 76.

Figure 77.

What’s Next?

So, there is definitely a format to these files, and I believe there are enough markers that someone could create a tool to parse them to make them easier to read and examine. They contain what a device owner said…what they actually said…to their car/device. This data could be extremely valuable to examiners and investigators, regardless of the venue in which they operate (civil or criminal).

Yes, this data could potentially be in a user’s Google Takeout data, and getting it that way would be slightly easier, although there would be a waiting period. But, what if you do not have the authority to get said data? This data could still be resident on the device and, potentially, available.

If I had scripting skills, I would try to see if something could be written to parse these files; however, alas, I do not. I have minimal scripting skills. Most of what I do is via Google and Cntrl/Cmd-C and Cntrl/Cmd-V. If any reader can do this, and is willing, please let me know and I will post your product here and give full credit. It would be awesome.

The second part of this post is forthcoming. If you can’t wait, here’s a sneak peek: there are some similarities…