SSL and internet security news

identification

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Bypassing Apple FaceID’s Liveness Detection Feature

Apple’s FaceID has a liveness detection feature, which prevents someone from unlocking a victim’s phone by putting it in front of his face while he’s sleeping. That feature has been hacked:

Researchers on Wednesday during Black Hat USA 2019 demonstrated an attack that allowed them to bypass a victim’s FaceID and log into their phone simply by putting a pair of modified glasses on their face. By merely placing tape carefully over the lenses of a pair glasses and placing them on the victim’s face the researchers demonstrated how they could bypass Apple’s FaceID in a specific scenario. The attack itself is difficult, given the bad actor would need to figure out how to put the glasses on an unconscious victim without waking them up.

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Cardiac Biometric

MIT Technology Review is reporting about an infrared laser device that can identify people by their unique cardiac signature at a distance:

A new device, developed for the Pentagon after US Special Forces requested it, can identify people without seeing their face: instead it detects their unique cardiac signature with an infrared laser. While it works at 200 meters (219 yards), longer distances could be possible with a better laser. “I don’t want to say you could do it from space,” says Steward Remaly, of the Pentagon’s Combatting Terrorism Technical Support Office, “but longer ranges should be possible.”

Contact infrared sensors are often used to automatically record a patient’s pulse. They work by detecting the changes in reflection of infrared light caused by blood flow. By contrast, the new device, called Jetson, uses a technique known as laser vibrometry to detect the surface movement caused by the heartbeat. This works though typical clothing like a shirt and a jacket (though not thicker clothing such as a winter coat).

[…]

Remaly’s team then developed algorithms capable of extracting a cardiac signature from the laser signals. He claims that Jetson can achieve over 95% accuracy under good conditions, and this might be further improved. In practice, it’s likely that Jetson would be used alongside facial recognition or other identification methods.

Wenyao Xu of the State University of New York at Buffalo has also developed a remote cardiac sensor, although it works only up to 20 meters away and uses radar. He believes the cardiac approach is far more robust than facial recognition. “Compared with face, cardiac biometrics are more stable and can reach more than 98% accuracy,” he says.

I have my usual questions about false positives vs false negatives, how stable the biometric is over time, and whether it works better or worse against particular sub-populations. But interesting nonetheless.

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Fingerprinting iPhones

This clever attack allows someone to uniquely identify a phone when you visit a website, based on data from the accelerometer, gyroscope, and magnetometer sensors.

We have developed a new type of fingerprinting attack, the calibration fingerprinting attack. Our attack uses data gathered from the accelerometer, gyroscope and magnetometer sensors found in smartphones to construct a globally unique fingerprint. Overall, our attack has the following advantages:

  • The attack can be launched by any website you visit or any app you use on a vulnerable device without requiring any explicit confirmation or consent from you.
  • The attack takes less than one second to generate a fingerprint.
  • The attack can generate a globally unique fingerprint for iOS devices.
  • The calibration fingerprint never changes, even after a factory reset.
  • The attack provides an effective means to track you as you browse across the web and move between apps on your phone.

* Following our disclosure, Apple has patched this vulnerability in iOS 12.2.

Research paper.

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Using a Fake Hand to Defeat Hand-Vein Biometrics

Nice work:

One attraction of a vein based system over, say, a more traditional fingerprint system is that it may be typically harder for an attacker to learn how a user’s veins are positioned under their skin, rather than lifting a fingerprint from a held object or high quality photograph, for example.

But with that said, Krissler and Albrecht first took photos of their vein patterns. They used a converted SLR camera with the infrared filter removed; this allowed them to see the pattern of the veins under the skin.

“It’s enough to take photos from a distance of five meters, and it might work to go to a press conference and take photos of them,” Krissler explained. In all, the pair took over 2,500 pictures to over 30 days to perfect the process and find an image that worked.

They then used that image to make a wax model of their hands which included the vein detail.

Slashdot thread.

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MD5 and SHA-1 Still Used in 2018

Last week, the Scientific Working Group on Digital Evidence published a draft document — “SWGDE Position on the Use of MD5 and SHA1 Hash Algorithms in Digital and Multimedia Forensics” — where it accepts the use of MD5 and SHA-1 in digital forensics applications:

While SWGDE promotes the adoption of SHA2 and SHA3 by vendors and practitioners, the MD5 and SHA1 algorithms remain acceptable for integrity verification and file identification applications in digital forensics. Because of known limitations of the MD5 and SHA1 algorithms, only SHA2 and SHA3 are appropriate for digital signatures and other security applications.

This is technically correct: the current state of cryptanalysis against MD5 and SHA-1 allows for collisions, but not for pre-images. Still, it’s really bad form to accept these algorithms for any purpose. I’m sure the group is dealing with legacy applications, but I would like it to really push those application vendors to update their hash functions.

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Detecting Lies through Mouse Movements

Interesting research: “The detection of faked identity using unexpected questions and mouse dynamics,” by Merulin Monaro, Luciano Gamberini, and Guiseppe Sartori.

Abstract: The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent’s true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to “build” and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee.

Boing Boing post.

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