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Reverse engineering an Arctis Pro Wireless Headset

2021-06-12T00:00:00Z

The Arctis Pro Wireless Headset.

For the last year and a bit, I’ve been using a SteelSeries Arctis Pro Wireless Headset for gaming and talking to friends. It’s a fine headset, but because there’s an always-on receiver there’s no way to detect if the headset is turned on or not from the desktop.

Whenever I start using the headset, I set my desktop’s sound to go to the headset, and then when I stop using the headset I set it to go back to speakers. It doesn’t take more than a second, but some days I might put the headset on a dozen times as I’m on calls, or if it’s noisy outside, etc. That means it’s probably worth at least a few hours of my time trying to automate it.

At first, I hoped I’d be able to tell from the state of the USB device whether there was a headset connected but nothing at all changed when flipping it on and off. Then I went hunting for existing open source tools that might work with it and found that while people have reverse engineered many of the older Arctis headsets, no one has done the same for the Pro Wireless. I finished off with a search to see if anyone had documented the wire protocol even if there was no nice open source software to go with it; I came up short there, too. Looks like I’d have to do it myself.

Capturing data with WireShark

The headset exposes a Human Interface Device (HID), and the wire protocols for earlier versions of the Arctis series looked to be very simple messages passed over the HID connection. It should therefore be fairly easy to use WireShark to capture the data being sent and received by the official SteelSeries application¹.

After setting up WireShark and making it record only the headset’s HID connection, it quickly became apparent that the software was sending three different requests each second, over and over again:

Wireshark, showing a packet capture of a SET_REPORT request from the host to the headset

The packets we’re interested in are the SET_REPORT Request frames sent from the host to the device. Wireshark understands the HID protocol, so it nicely shows us the raw HID data; in the screenshot this is 0x40AA (ignoring the trailing zero bytes). The other requests sent immediately after have 0x41AA and 0x42AA payloads — clearly the first byte is indicating which piece of data is being requested.

The responses to these requests come back in an URB_INTERRUPT in frame:

Wireshark, showing a packet capture of the headset’s response to the earlier request

So the answer to our first request appears to be 0x04. The second response is 0x0402 and the third is 0x04 again. The first thing I tried doing was popping the spare battery out of the charger. The response to the 0x42AA request changed from 0x04 to 0x00 - that’s certainly clear enough! After a bit of waiting around, the battery in my headset dropped from full on the display to three bars, and at that point the 0x40AA response dropped from 0x04 to 0x03. The display on the receiver shows battery state as four bars, and it appears the wire protocol directly corresponds to that particular representation.

That left the 0x41AA request as an unknown. I tried everything I could think of, but it stubbornly kept returning 0x0402. I enlisted a friend who has the same headset to run some hacky Go code and report his values, and he also got a 0x0402 response. As I explained that I couldn’t figure out what these values are, he reported back that turning his headset off made the response change to 0x0202. In all my testing, I’d forgotten to try turning the headset off! That’s the one thing I was actually trying to detect, as well. Thanks, Simon, for helping me get past that bit of stupidity!

I still don’t know what the second byte of the response is, or whether there are other values than 0x04 for on and 0x02 for off, but I’m happy enough to label it as “device status” and move on.

Exploring other features

The software allows you to tweak a bunch of different settings:

SteelSeries Good Game software, showing the advanced headset settings

I went through each one and fiddled with all the values, recording the requests in WireShark as I did so. It turns out the protocol is very simplistic - the wire protocol directly corresponds to the UI elements in the software (or on the receiver, if you navigate through its menus). For example, the software allows you to set the “Headset auto shutoff” value in increments of 10 minutes. You might usually expect this to be converted to seconds or something similar before being passed to the device, but on the wire it’s actually sent as 0x00 for off, 0x01 for 10 minutes, up through to 0x0C for the maximum of 120 minutes.

All the dropdown options seem to function this way — if you pick the 6th option then the request payload will be an 0x06 byte. The sliders have fixed positions they snap to and function similarly: for the two brightness sliders pictured they snap to 11 positions and on the wire these range from 0x00 for off to 0x0A for the maximum.

At this point I was well past my goal of being able to detect whether the headset was on or off, and I was now just trying to see if I could figure out enough of the protocol that I could reimplement the control software on Linux if I ever wanted to. Just as I was about to close the app, something caught my eye: there are integrations with games and other software that can display information on the receiver’s OLED display!

There’s an official API for this, but it involves sending JSON to a webserver which runs as part of their app². That doesn’t feel very nice, and definitely won’t work for me on Linux, so I started a new WireShark session and took some captures while I spoke to myself on Discord.

Decoding pixels

The frames sent to the device whenever Discord showed a notification had a 1060 byte payload. The display on the receiver is 140 pixels wide³ and each pixel can only seem to be on or off, so I figured each bit in the payload corresponded to one bit of the output. I exported the data to binary in the hope that I could visually see what was going on - in theory if I line wrap the data at 140 characters it should look vaguely like the final output. Unfortunately, it did not. There was roughly the right number of high bits, but no amount of fiddling with them in a text editor could give me a coherent picture.

Instead, I wrote some code to write different values to the device. Starting with a payload of all zeroes and gradually increasing a bit at a time every second. After the first few seconds, I saw a line of pixels being drawn downwards on the left of the screen; had I just got the axes the wrong way around? After the first eight pixels lit up, though, they jumped over to the next column. The actual addressing scheme looks something like this:

Diagram of how pixels are addressed on the receiver’s OLED display

So the first 140 bytes given the pixels for the first 8 rows, the next 140 bytes fill in the 8 rows below that, and so on and so forth. Armed with this information I wrote a simple program to read an image and output it to the display:

Headset receiver displaying a custom Hello World message

At this point I was thinking about trying to get Doom rendering on the screen, but I couldn’t find anything nicely hackable that would let me grab the output and pass it on to the receiver. Instead, I decided to try a GIF decoder and before very long had a nice little animated display:

Protocol reference

All the HID messages have a single byte that determines the message type, then an 0xAA byte, then any payload required by the command. These are the ones I’ve figured out:

Byte	Command	Payload
`0x09`	Save changes	None.
`0x10`	Request firmware version(?)	None.
`0x27`	Volume limiter	`0x00` for off, `0x01` for on.
`0x2E`	Equalizer preset	ID of the equalizer preset to use.
`0x39`	Sidetone level	`0x00` for lowest to `0x09` for highest.
`0x3C`	Set headset timeout	Timeout, as a number of 10 minutes. `0x00` for off to `0x0C` for 120 mins.
`0x3E`	Mic mute LED brightness	`0x00` for lowest to `0x0A` for highest.
`0x40`	Request headset battery	None.
`0x41`	Request device status	None.
`0x42`	Request receiver battery	None.
`0x51`	Surround sound mode	`0x00` for off, `0x01` for on.
`0x62`	Auto-start Bluetooth	`0x00` for off, `0x01` for on.
`0x63`	Auto-mute game audio during calls	`0x00` for off, `0x01` for on.
`0x85`	OLED brightness	`0x00` for lowest to `0x0A` for highest.
`0x83`	Equalizer	(Not yet decoded)
`0x89`	Screensaver mode	`0x00` to dim, `0x01` for off, `0x02` for screensaver.
`0xD2`	Render image	Pixel array as described above.

Some of these I’ve not dug too much into because they didn’t seem very interesting. If you have an Arctis Pro Wireless and figure anything more out, let me know, and I’ll update the list.

If you just want to check device state like I originally did, I’ve contributed support for this headset to the excellent HeadsetControl project. It should be in the next release.

Unfortunately (as you’d expect) their application only runs on Windows, so this process involved an annoying amount of rebooting to Windows, fleeing back to Linux, and then realising I hadn’t actually recorded enough to figure it out and repeating. ↩︎
You can also send lisp to the webserver, and it will execute it. No comment. ↩︎
I know this not because it’s mentioned in the technical specs (it’s not), but because I took a photo and counted them out one by one. ↩︎

How to break everything by fuzz testing

2020-04-26T00:00:00Z

Fuzz testing is a bit like the infinite monkey theorem, but instead of Shakespeare you get crashes.

Fuzz testing, if you’re not aware, is a form of testing that uses procedurally generated random inputs to see how a program behaves. For instance, if you were fuzz testing a web page renderer you might generate a bunch of HTML - some valid, and some not - and make sure the rendering process didn’t unexpectedly crash.

Fuzz testing doesn’t readily lend itself to all types of software, but it particularly shines in cases where some kind of complex user input is accepted and processed in some way - like the aforementioned web page renderer. I was recently adding a library to parse EXIF data to images to an Internet-facing service and realised it was a perfect opportunity to do some fuzz testing. Even if I didn’t find any issues, I’d improve my confidence that the library was safe enough to expose to the Internet.

Breaking my EXIF library

I wrote a quick harness to run go-fuzz on the library, and gave it some pre-existing demo files as sample input. The way go-fuzz works is that it instruments your code and then mutates the inputs to try to improve the coverage. For example, if I had some sample data that had an EXIF tag with a value of 1 then go-fuzz might change it to a 2 and see if the code follows a different path. In most cases it won’t but when it does, they tend to be very interesting cases.

One of the first issues that go-fuzz found was that some values in a maker note field would cause the library to panic (i.e., crash). This happened because there was a check to see if the first six characters were “Nikon” and a null byte, without first checking to see if there were actually six characters available. This is a kind of bug that doesn’t happen much with “real” data - as the field is either not present or completed correctly - but could easily be exploited once this code is exposed to the Internet.

Another interesting bug that go-fuzz found was that if a tag had a particularly large count, the library would try to allocate an obscene amount of memory and die. There was already a check in the code that was meant to avoid this exact scenario, but go-fuzz managed to find a way around it. Each tag has a size (for example an integer tag takes a fixed number of bytes) and a count; the existing check multiplied the two together and made sure that the result wasn’t too large. For most cases this was fine but go-fuzz found a case where the count was so large that when multiplied by the size of the tag it overflowed the integer and became negative, thus passing the sanity check but then subsequently failing when it came around to actually allocating the memory.

The final bug of note that go-fuzz found was the most interesting. EXIF data is stored in IFDs (“Image File Directories”), and each IFD provides what is effectively a pointer (a byte offset) to the next one. The EXIF library already had a check to make sure that these didn’t loop, but it only checked the immediately preceding IFD - so if IFD 1 linked to IFD 2, it would catch IFD 2 linking back to IFD 1 and break the loop with an error. Go-fuzz found that having three interlinked IFDs had the same issue, though, and the guard code wasn’t triggered. This created an infinite loop, maxing out a CPU core until the process was eventually killed - one of the worst kind of bugs you could have in an Internet-facing service which doesn’t deal with private data! The fix for this was fairly straightforward - I just made the library keep a record of the previously visited IFDs and bail out if it found a loop.

Breaking my IDE

When go-fuzz detects an issue it outputs not only the details of the problem (the stack trace, error message, and so forth) but also the input that generated the problem. This is useful for reproducing and making sure the issue is fixed, but it also makes it really easy to write a test to ensure that the behaviour never regresses in the future.

As I was working through fixing the bugs that go-fuzz found, I dutifully added new tests where needed. After adding the sample input with looping IFDs to the project, I switched to IDEA to write a test to use it. I clicked on the input file to copy the file name, and then the entire IDE hung and had to be restarted. Uh oh! When I restarted IDEA, it immediately began indexing the project and again hung. It turns out IDEA parses EXIF data (presumably, even if it does nothing else with the data, to get the rotation property for images), and the library they use - an independent one written on Java - had the same bug as the Go library I was using.

In order to stop IDEA from indexing the file and becoming unusable I renamed it from a ‘.tif’ extension to ‘.dat’, and everything went back to normal. I thought I’d best report the bug to JetBrains, though, so they could put a proper fix in.

Breaking YouTrack

JetBrains use their own issue tracker called YouTrack for reporting bugs in IDEA. I dutifully went over and described the problem, attaching the log files from the IDE, a description of how the file was malformed, and carefully selected the .dat version of the file to upload so that it wouldn’t cause anyone else the same immediate problem.

After trying to upload the file I got a strange error back. Uh oh! I submitted the IDEA issue as it stood, unable to see if the attachments had even uploaded, and went and wrote up an issue for YouTrack itself about the error message. While I was doing that, YouTrack seemed to slow down and become really annoying to use. I had a sinking feeling the exact same thing was probably happening as with IDEA and my library - but this time YouTrack had content-sniffed the file instead of relying on the file extension. In hindsight, I should’ve put the file in a passworded archive to ensure no automated tools got hold of it. I marked the issue as a security problem as in a service like YouTrack it presents a denial-of-service opportunity¹ (remember when I said it was one of the worst kinds of bugs you could have in an Internet-facing service?…)

Shortly after I raised my YouTrack ticket, a notice appeared at the top of the page saying they were investigating the current performance issues. Uh oh! I was holding out hope that this was unrelated to me uploading the buggy dat file, but the timing all seemed a bit suspect. I shot support an e-mail saying I think I might be the root cause for their performance issues and linked to the ticket. In the time it took me to e-mail them, the entire site had been put into maintenance mode. I got an e-mail back a few hours later confirming the outage was in fact all my fault, as I’d feared. Within the space of days the JetBrains security team had fixed the issue in YouTrack, which was a pretty nice turnaround.

So if you were trying to access YouTrack at the start of March and couldn’t - I’m sorry, I didn’t mean to! Also, if you’re building an Internet-facing service that takes user input you should really consider running a fuzz tester against it!

“We have a problem”. “Remember, there are no such things as problems, only opportunities”. “Well then we have a DDoS opportunity.” – @J4vv4d ↩︎

Reverse engineering the Sense API

2016-04-10T00:00:00Z

A Sense unit and its two pillow sensors

Sense is a little device that sits by your bedside and, in conjunction with a little ‘pill’ attached to your pillow, monitors your sleeping patterns and any environmental conditions that might hamper them. Android and iOS apps show you your sleep history, and offer suggestions for improvements.

Sense was Kickstarted in August 2014, raising over 2.4 million US dollars, and shipped to backers in mid 2015. The campaign blurb included this snippet:

Building with Sense

You’ll always have access to your data via our API. Take it, play with it, graph it, do whatever you want with it. It’s yours. That’s important to us.

We enjoy tinkering with and building on-top of other products we like. Sense will let you have that experience.

We’d love to hear your thoughts on what you might want to build with Sense, and how you could directly interact with the hardware, and the data it collects.

Sounds great! But a year after shipping, there’s no sign of an API, and some of us who enjoy tinkering are getting a bit restless…

Reverse engineering to the rescue

Data from the Sense hardware is transmitted off onto the Internet somewhere, and then pulled down by the mobile apps. If we can snoop that traffic, we can probably figure out how to grab the data from the Sense.

So the first step is to capture the raw network traffic while the Sense and the app are in use. I crafted an extremely over-the-top setup that involves my phone and the Sense being on a new, separate wireless network that gateways through a Linux server that can look in on all the traffic (because you never know when a dedicated capturing network will come in handy, right?…)

Running tshark while the app and device are active showed they were making HTTPS requests an Amazon Elastic Cloud instance. That’s not really much use, as we can’t see the content of the requests. The next step is to move up the network stack and target the HTTPS traffic specifically. Step in, mitmproxy. This automates man-in-the-middle attacks on HTTPS requests. When it receives such a request, it cooks up its own certificate and sends that to the client, and then sits in between while the client and the server communicate.

The certificates generated by mitmproxy aren’t issued by a trusted source, so (well behaved) browsers and apps will throw up lots of warnings and refuse to communicate with it for the most part. This is the mechanism that stops anyone impersonating your banking website when you access it over HTTPS, so is generally a very good thing. In this case, both the Sense itself and the Android app refused to talk with the blatant attacker.

We control the Android phone, though, so can just tell it that we trust the dodgy certificate issuer. This involves grabbing the certificate generated by mitmproxy, and adding it to Android’s key store. The process is documented nicely in the mitmproxy docs and is pretty straightforward. With Android trusting our certificate authority, the Sense app starts talking through the proxy and we can look at the plain HTTP requests.

The API

Logging in to the Android app and navigating through the various screens shows that the API is actually pretty nice. It’s RESTful, and uses an OAuth bearer token for authorisation, as you can see in the mitmproxy output of the authentication and first few requests:

POST https://api.hello.is/v1/oauth2/token
    ← 200 application/json 151B 1.17s
GET https://api.hello.is/v2/account/preferences
    ← 200 application/json 124B 109ms
GET https://api.hello.is/v1/account
    ← 200 application/json 181B 388ms
GET https://api.hello.is/v2/devices
    ← 200 application/json 235B 126ms
POST https://api.hello.is/v1/app/checkin
    ← 200 application/json 106B 119ms
GET https://api.hello.is/v2/insights
    ← 200 application/json 1.4kB 157ms
POST https://api.hello.is/v1/notifications/registration
    ← 204 [no content] 364ms
GET https://api.hello.is/v2/timeline/2016-04-09
    ← 200 application/json 160B 550ms
GET https://api.hello.is/v1/questions?date=2016-04-10
    ← 200 application/json 284B 132ms
PATCH https://api.hello.is/v1/app/stats
    ← 202 [no content] 126ms
GET https://api.hello.is/v1/app/stats/unread
    ← 200 application/json 71B 171ms

Authorisation

To get a bearer token, you send a POST request to /v1/oauth2/token, supplying the username and password, and a client ID and secret. Presumably if the API is ever opened up you’ll be able to register clients and get your own ID and secret, but for now reusing the ones from the Android app works fine. [I’ve prettified and linewrapped the content to make it a bit easier to read.]

POST /v1/oauth2/token HTTP/1.1
Host: api.hello.is
Accept: */*
Content-Length: 123
Content-Type: application/x-www-form-urlencoded

grant_type=password
 &client_id=8d3c1664-05ae-47e4-bcdb-477489590aa4
 &client_secret=4f771f6f-5c10-4104-bbc6-3333f5b11bf9
 &username=USERNAME
 &password=PASSWORD

HTTP/1.1 200 OK
Cache-Control: no-cache
Content-Type: application/json
Date: Sun, 10 Apr 2016 17:25:16 GMT
transfer-encoding: chunked
Connection: keep-alive

{
  "token_type":"Bearer",
  "expires_in":31536000,
  "account_id":"1234",
  "access_token":"2.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  "refresh_token":"2.yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy"
}

Sleep timeline

Getting the timeline for one night is just a straight up GET request. It gives a detailed log of events, including different ‘depths’ of sleep (I’ve cut out a whole bunch here for simplicity). It also shows metrics relating to the entire night, and their condition (‘warning’ or ‘ideal’).

GET /v2/timeline/2016-04-08 HTTP/1.1
Host: api.hello.is
Accept: */*
Authorization: Bearer 2.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

HTTP/1.1 200 OK
Cache-Control: no-cache
Content-Type: application/json
Date: Sun, 10 Apr 2016 17:29:00 GMT
transfer-encoding: chunked
Connection: keep-alive

{
  "score":76,
  "score_condition":"WARNING",
  "message":"You were asleep for **8.2 hours**, and sleeping soundly for 3.2 hours.",
  "date":"2016-04-08",
  "events":[
    {
      "timestamp":1460168700000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"You went to bed.",
      "sleep_depth":0,
      "sleep_state":"AWAKE",
      "event_type":"GOT_IN_BED",
      "valid_actions":["ADJUST_TIME","VERIFY","INCORRECT"]
    },{
      "timestamp":1460169600000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"You fell asleep.",
      "sleep_depth":100,
      "sleep_state":"SOUND",
      "event_type":"FELL_ASLEEP",
      "valid_actions":["ADJUST_TIME","VERIFY","INCORRECT"]
    },{
      "timestamp":1460177040000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"You were moving around quite a bit.",
      "sleep_depth":2,
      "sleep_state":"AWAKE",
      "event_type":"GENERIC_MOTION",
      "valid_actions":["VERIFY","INCORRECT"]
    },{
      "timestamp":1460199000000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"Good morning.",
      "sleep_depth":0,
      "sleep_state":"AWAKE",
      "event_type":"WOKE_UP",
      "valid_actions":["ADJUST_TIME","VERIFY","INCORRECT"]
    }
  ],
  "metrics":[
    { "name":"total_sleep", "value":490, "unit":"MINUTES", "condition":"IDEAL" },
    { "name":"sound_sleep", "value":190, "unit":"MINUTES", "condition":"IDEAL" },
    { "name":"time_to_sleep", "value":15, "unit":"MINUTES", "condition":"IDEAL" },
    { "name":"times_awake", "value":2, "unit":"QUANTITY", "condition":"IDEAL" },
    { "name":"fell_asleep", "value":1460169600000, "unit":"TIMESTAMP", "condition":"IDEAL" },
    { "name":"woke_up", "value":1460199000000, "unit":"TIMESTAMP", "condition":"IDEAL" },
    { "name":"temperature", "value":null, "unit":"CONDITION", "condition":"IDEAL" },
    { "name":"humidity", "value":null, "unit":"CONDITION", "condition":"IDEAL" },
    { "name":"particulates", "value":null, "unit":"CONDITION", "condition":"WARNING" },
    { "name":"light", "value":null, "unit":"CONDITION", "condition":"IDEAL" },
    { "name":"sound", "value":null, "unit":"CONDITION", "condition":"IDEAL"}
  ]
}

Other interesting resources

Pretty much everything displayed in the app is available as a REST resource. They clearly put some thought into API design, which makes it even more of a shame that they haven’t made it public yet. Some of the other interesting resources are:

/v1/alarms: the alarms set on the Sense.
/v1/room/current?temp_unit=c: gives the latest environmental readouts from the Sense.
/v1/room/all_sensors/hours?quantity=2&from_utc=<timestamp>: sensor history.
/v2/devices: the paired devices, including battery status of pills.
/v2/insights: JSON representation of the cards displayed on the main screen (recommendations, analysis, etc).
/v2/trends/LAST_WEEK (or MONTH or 3_MONTHS): gives graph data for sleep scores, duration and depths.

Now the API is figured out, all that’s left is to build something that uses it…