Brute-Forcing a Fingerprint Reader
It’s neither hard nor expensive:
Unlike password authentication, which requires a direct match between what is inputted and what’s stored in a database, fingerprint authentication determines a match using a reference threshold. As a result, a successful fingerprint brute-force attack requires only that an inputted image provides an acceptable approximation of an image in the fingerprint database. BrutePrint manipulates the false acceptance rate (FAR) to increase the threshold so fewer approximate images are accepted.
BrutePrint acts as an adversary in the middle between the fingerprint sensor and the trusted execution environment and exploits vulnerabilities that allow for unlimited guesses.
In a BrutePrint attack, the adversary removes the back cover of the device and attaches the $15 circuit board that has the fingerprint database loaded in the flash storage. The adversary then must convert the database into a fingerprint dictionary that’s formatted to work with the specific sensor used by the targeted phone. The process uses a neural-style transfer when converting the database into the usable dictionary. This process increases the chances of a match.
With the fingerprint dictionary in place, the adversary device is now in a position to input each entry into the targeted phone. Normally, a protection known as attempt limiting effectively locks a phone after a set number of failed login attempts are reached. BrutePrint can fully bypass this limit in the eight tested Android models, meaning the adversary device can try an infinite number of guesses. (On the two iPhones, the attack can expand the number of guesses to 15, three times higher than the five permitted.)
The bypasses result from exploiting what the researchers said are two zero-day vulnerabilities in the smartphone fingerprint authentication framework of virtually all smartphones. The vulnerabilities—one known as CAMF (cancel-after-match fail) and the other MAL (match-after-lock)—result from logic bugs in the authentication framework. CAMF exploits invalidate the checksum of transmitted fingerprint data, and MAL exploits infer matching results through side-channel attacks.
Depending on the model, the attack takes between 40 minutes and 14 hours.
Also:
The ability of BrutePrint to successfully hijack fingerprints stored on Android devices but not iPhones is the result of one simple design difference: iOS encrypts the data, and Android does not.
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J. C. Checco • May 30, 2023 9:22 AM
The issue with the implementation of fingerprint readers on smartphones is one of duplicity: because the capture area is so small, the reading devices stores multiple partial measurements. Thus, one fingerprint signature may actually multiple acceptable templates stored. This usability feature was exploited by New York University Tandon School of Engineering (https://engineering.nyu.edu/news/machine-learning-masters-fingerprint-fool-biometric-systems). I personally am a fan of the fingerprint slide scanner since it reads a larger print and leaves no latent residue to capture.