利用 Sensor 校正資訊產生 Device Fingerprint 的隱私攻擊

看到「Fingerprinting iPhones」這篇提出的攻擊,標題雖然是提到 iPhone,但實際上攻擊包括了 Android 的手機:

You are affected by this fingerprinting attack if you are using any iOS devices with the iOS version below 12.2, including the latest iPhone XS, iPhone XS Max, and iPhone XR. You are also likely to be affected if you are using a Pixel 2/3 device, although we hypothesise the generated fingerprint has less entropy and is unlikely to be globally unique. A SensorID can be generated by both apps and mobile websites and requires no user interaction.

目前 iPhone 升級到 12.2 之後可以緩解這個問題,Android 看起來還不清楚...

攻擊的方式是透過手機在出場前會使用外部的校正工具,找出手機內 sensor 所偵測到的值與實際值的差異,然後把這些資訊燒到韌體裡,當呼叫 API 時就可以修正給出比較正確的值。

而因為這些校正資訊幾乎每一隻手機都不一樣,而且不會因為重裝而變更 (即使 factory reset),加上還可以跨 app 與 web 追蹤,就成為這次攻擊的目標:

In the context of mobile devices, the main benefit of per-device calibration is that it allows more accurate attitude estimation.

資訊量其實相當大,透過 app 分析可以得到 67 bits entropy,透過網頁也有 42 bits entropy,而且不怎麼會變:

In general, it is difficult to create a unique fingerprint for iOS devices due to strict sandboxing and device homogeneity. However, we demonstrated that our approach can produce globally unique fingerprints for iOS devices from an installed app -- around 67 bits of entropy for the iPhone 6S. Calibration fingerprints generated by a website are less unique (~42 bits of entropy for the iPhone 6S), but they are orthogonal to existing fingerprinting techniques and together they are likely to form a globally unique fingerprint for iOS devices.

We have not observed any change in the SensorID of our test devices in the past half year. Our dataset includes devices running iOS 9/10/11/12. We have tested compass calibration, factory reset, and updating iOS (up until iOS 12.1); the SensorID always stays the same. We have also tried measuring the sensor data at different locations and under different temperatures; we confirm that these factors do not change the SensorID either.

目前提出來的解法是加入隨機值的噪音 (iOS 的作法),不過作者有建議預設應該要關閉 js 存取 sensor 的權限:

To mitigate this calibration fingerprint attack, vendors can add uniformly distributed random noise to ADC outputs before calibration is applied. Alternatively, vendors could round the sensor outputs to the nearest multiple of the nominal gain. Please refer to our paper for more details. In addition, we recommend privacy-focused mobile browsers add an option to disable the access to motion sensors via JavaScript. This could help protect Android devices and iOS devices that no longer receive updates from Apple.

不過當初這群人怎麼會注意到的...

利用手機的 sensor 取得 PIN 碼

把 side-channel information 配合上統計方法就可以達到 74% 的正確率:「Phone Hack Uses Sensors To Steal PINs」。

透過 browser 的 javascript 就可以拉出這些資料,然後利用這些資料去猜你的手機 PIN 碼:

Researchers from U.K.-based Newcastle University created a JavaScript app called PINlogger.js that has the ability to access data generated by the phone’s sensors, including GPS, camera, microphone, accelerometer, magnetometer, proximity, gyroscope, pedometer and NFC protocols.

而且當可以多抓到更多資訊時 (像是第二次輸入) 準確度就更高了:

Using a sample set of 50 PINs, researchers found that their script was able to correctly guess a user’s PIN 74 percent of the time on the first try, which increases to 86 and 94 percent success rates on the second and third attempts.

有些瀏覽器有做一些修正,讓 side-channel information 變少,於是難度變高:

As for Firefox, starting from version 46 (released in April 2016), the browser restricts JavaScript access to motion and orientation sensors. Apple’s Security Updates for iOS 9.3 (released in March 2016), suspended the availability of motion and orientation data when the web view is hidden, according to researchers.

Google 則是沒修:

As for Google, it’s unclear what measures have been taken. “Our concern is confirmed by members in the Google Chromium team, who also believe that the issue remains unresolved,” the report stated. Google did not reply to a request to comment for this report.

這攻擊方式頗不賴... @_@