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Vulnerabilities in the WPA3 Wi-Fi Security Protocol

Researchers have found several vulnerabilities in the WPA3 Wi-Fi security protocol:

The design flaws we discovered can be divided in two categories. The first category consists of downgrade attacks against WPA3-capable devices, and the second category consists of weaknesses in the Dragonfly handshake of WPA3, which in the Wi-Fi standard is better known as the Simultaneous Authentication of Equals (SAE) handshake. The discovered flaws can be abused to recover the password of the Wi-Fi network, launch resource consumption attacks, and force devices into using weaker security groups. All attacks are against home networks (i.e. WPA3-Personal), where one password is shared among all users.

News article. Research paper: “Dragonblood: A Security Analysis of WPA3’s SAE Handshake“:

Abstract: The WPA3 certification aims to secure Wi-Fi networks, and provides several advantages over its predecessor WPA2, such as protection against offline dictionary attacks and forward secrecy. Unfortunately, we show that WPA3 is affected by several design flaws,and analyze these flaws both theoretically and practically. Most prominently, we show that WPA3’s Simultaneous Authentication of Equals (SAE) handshake, commonly known as Dragonfly, is affected by password partitioning attacks. These attacks resemble dictionary attacks and allow an adversary to recover the password by abusing timing or cache-based side-channel leaks. Our side-channel attacks target the protocol’s password encoding method. For instance, our cache-based attack exploits SAE’s hash-to-curve algorithm. The resulting attacks are efficient and low cost: brute-forcing all 8-character lowercase password requires less than 125$in Amazon EC2 instances. In light of ongoing standardization efforts on hash-to-curve, Password-Authenticated Key Exchanges (PAKEs), and Dragonfly as a TLS handshake, our findings are also of more general interest. Finally, we discuss how to mitigate our attacks in a backwards-compatible manner, and explain how minor changes to the protocol could have prevented most of our attack

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Maliciously Tampering with Medical Imagery

In what I am sure is only a first in many similar demonstrations, researchers are able to add or remove cancer signs from CT scans. The results easily fool radiologists.

I don’t think the medical device industry has thought at all about data integrity and authentication issues. In a world where sensor data of all kinds is undetectably manipulatable, they’re going to have to start.

Research paper. Slashdot thread.

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Adversarial Machine Learning against Tesla’s Autopilot

Researchers have been able to fool Tesla’s autopilot in a variety of ways, including convincing it to drive into oncoming traffic. It requires the placement of stickers on the road.

Abstract: Keen Security Lab has maintained the security research work on Tesla vehicle and shared our research results on Black Hat USA 2017 and 2018 in a row. Based on the ROOT privilege of the APE (Tesla Autopilot ECU, software version 18.6.1), we did some further interesting research work on this module. We analyzed the CAN messaging functions of APE, and successfully got remote control of the steering system in a contact-less way. We used an improved optimization algorithm to generate adversarial examples of the features (autowipers and lane recognition) which make decisions purely based on camera data, and successfully achieved the adversarial example attack in the physical world. In addition, we also found a potential high-risk design weakness of the lane recognition when the vehicle is in Autosteer mode. The whole article is divided into four parts: first a brief introduction of Autopilot, after that we will introduce how to send control commands from APE to control the steering system when the car is driving. In the last two sections, we will introduce the implementation details of the autowipers and lane recognition features, as well as our adversarial example attacking methods in the physical world. In our research, we believe that we made three creative contributions:

  1. We proved that we can remotely gain the root privilege of APE and control the steering system.
  2. We proved that we can disturb the autowipers function by using adversarial examples in the physical world.
  3. We proved that we can mislead the Tesla car into the reverse lane with minor changes on the road.

You can see the stickers in this photo. They’re unobtrusive.

This is machine learning’s big problem, and I think solving it is a lot harder than many believe.

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Recovering Smartphone Typing from Microphone Sounds

Yet another side-channel attack on smartphones: “Hearing your touch: A new acoustic side channel on smartphones,” by Ilia Shumailov, Laurent Simon, Jeff Yan, and Ross Anderson.

Abstract: We present the first acoustic side-channel attack that recovers what users type on the virtual keyboard of their touch-screen smartphone or tablet. When a user taps the screen with a finger, the tap generates a sound wave that propagates on the screen surface and in the air. We found the device’s microphone(s) can recover this wave and “hear” the finger’s touch, and the wave’s distortions are characteristic of the tap’s location on the screen. Hence, by recording audio through the built-in microphone(s), a malicious app can infer text as the user enters it on their device. We evaluate the effectiveness of the attack with 45 participants in a real-world environment on an Android tablet and an Android smartphone. For the tablet, we recover 61% of 200 4-digit PIN-codes within 20 attempts, even if the model is not trained with the victim’s data. For the smartphone, we recover 9 words of size 7-13 letters with 50 attempts in a common side-channel attack benchmark. Our results suggest that it not always sufficient to rely on isolation mechanisms such as TrustZone to protect user input. We propose and discuss hardware, operating-system and application-level mechanisms to block this attack more effectively. Mobile devices may need a richer capability model, a more user-friendly notification system for sensor usage and a more thorough evaluation of the information leaked by the underlying hardware.

Blog post.

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Friday Squid Blogging: New Research on Squid Camouflage

From the New York Times:

Now, a paper published last week in Nature Communications suggests that their chromatophores, previously thought to be mainly pockets of pigment embedded in their skin, are also equipped with tiny reflectors made of proteins. These reflectors aid the squid to produce such a wide array of colors, including iridescent greens and blues, within a second of passing in front of a new background. The research reveals that by using tricks found in other parts of the animal kingdom — like shimmering butterflies and peacocks — squid are able to combine multiple approaches to produce their vivid camouflage.

Researchers studied Doryteuthis pealeii, or the longfin squid.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

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I Was Cited in a Court Decision

An article I co-wrote — my first law journal article — was cited by the Massachusetts Supreme Judicial Court — the state supreme court — in a case on compelled decryption.

Here’s the first, in footnote 1:

We understand the word “password” to be synonymous with other terms that cell phone users may be familiar with, such as Personal Identification Number or “passcode.” Each term refers to the personalized combination of letters or digits that, when manually entered by the user, “unlocks” a cell phone. For simplicity, we use “password” throughout. See generally, Kerr & Schneier, Encryption Workarounds, 106 Geo. L.J. 989, 990, 994, 998 (2018).

And here’s the second, in footnote 5:

We recognize that ordinary cell phone users are likely unfamiliar with the complexities of encryption technology. For instance, although entering a password “unlocks” a cell phone, the password itself is not the “encryption key” that decrypts the cell phone’s contents. See Kerr & Schneier, supra at 995. Rather, “entering the [password] decrypts the [encryption] key, enabling the key to be processed and unlocking the phone. This two-stage process is invisible to the casual user.” Id. Because the technical details of encryption technology do not play a role in our analysis, they are not worth belaboring. Accordingly, we treat the entry of a password as effectively decrypting the contents of a cell phone. For a more detailed discussion of encryption technology, see generally Kerr & Schneier, supra.

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Friday Squid Blogging: A Tracking Device for Squid

Really:

After years of “making do” with the available technology for his squid studies, Mooney created a versatile tag that allows him to research squid behavior. With the help of Kakani Katija, an engineer adapting the tag for jellyfish at California’s Monterey Bay Aquarium Research Institute (MBARI), Mooney’s team is creating a replicable system flexible enough to work across a range of soft-bodied marine animals. As Mooney and Katija refine the tags, they plan to produce an adaptable, open-source package that scientists researching other marine invertebrates can also use.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

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