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How Apple’s “Find My” Feature Works

Matthew Green intelligently speculates about how Apple’s new “Find My” feature works.

If you haven’t already been inspired by the description above, let me phrase the question you ought to be asking: how is this system going to avoid being a massive privacy nightmare?

Let me count the concerns:

  • If your device is constantly emitting a BLE signal that uniquely identifies it, the whole world is going to have (yet another) way to track you. Marketers already use WiFi and Bluetooth MAC addresses to do this: Find My could create yet another tracking channel.

  • It also exposes the phones who are doing the tracking. These people are now going to be sending their current location to Apple (which they may or may not already be doing). Now they’ll also be potentially sharing this information with strangers who “lose” their devices. That could go badly.

  • Scammers might also run active attacks in which they fake the location of your device. While this seems unlikely, people will always surprise you.

The good news is that Apple claims that their system actually does provide strong privacy, and that it accomplishes this using clever cryptography. But as is typical, they’ve declined to give out the details how they’re going to do it. Andy Greenberg talked me through an incomplete technical description that Apple provided to Wired, so that provides many hints. Unfortunately, what Apple provided still leaves huge gaps. It’s into those gaps that I’m going to fill in my best guess for what Apple is actually doing.

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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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Security Analysis of the LIFX Smart Light Bulb

The security is terrible:

In a very short limited amount of time, three vulnerabilities have been discovered:

  • Wifi credentials of the user have been recovered (stored in plaintext into the flash memory).
  • No security settings. The device is completely open (no secure boot, no debug interface disabled, no flash encryption).
  • Root certificate and RSA private key have been extracted.

Boing Boing post.

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Major Bluetooth Vulnerability

Bluetooth has a serious security vulnerability:

In some implementations, the elliptic curve parameters are not all validated by the cryptographic algorithm implementation, which may allow a remote attacker within wireless range to inject an invalid public key to determine the session key with high probability. Such an attacker can then passively intercept and decrypt all device messages, and/or forge and inject malicious messages.

Paper. Website. Three news articles.

This is serious. Update your software now, and try not to think about all of the Bluetooth applications that can’t be updated.

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WPA3

Everyone is writing about the new WPA3 Wi-Fi security standard, and how it improves security over the current WPA2 standard.

This summary is as good as any other:

The first big new feature in WPA3 is protection against offline, password-guessing attacks. This is where an attacker captures data from your Wi-Fi stream, brings it back to a private computer, and guesses passwords over and over again until they find a match. With WPA3, attackers are only supposed to be able to make a single guess against that offline data before it becomes useless; they’ll instead have to interact with the live Wi-Fi device every time they want to make a guess. (And that’s harder since they need to be physically present, and devices can be set up to protect against repeat guesses.)

WPA3’s other major addition, as highlighted by the Alliance, is forward secrecy. This is a privacy feature that prevents older data from being compromised by a later attack. So if an attacker captures an encrypted Wi-Fi transmission, then cracks the password, they still won’t be able to read the older data — they’d only be able to see new information currently flowing over the network.

Note that we’re just getting the new standard this week. Actual devices that implement the standard are still months away.

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Needless Panic Over a Wi-FI Network Name

A Turkish Airlines flight made an emergency landing because someone named his wireless network (presumably from his smartphone) “bomb on board.”

In 2006, I wrote an essay titled “Refuse to be Terrorized.” (I am also reminded of my 2007 essay, “The War on the Unexpected.” A decade later, it seems that the frequency of incidents like the one above is less, although not zero. Progress, I suppose.

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Amazon Patents Measures to Prevent In-Store Comparison Shopping

Amazon has been issued a patent on security measures that prevents people from comparison shopping while in the store. It’s not a particularly sophisticated patent — it basically detects when you’re using the in-store Wi-Fi to visit a competitor’s site and then blocks access — but it is an indication of how retail has changed in recent years.

What’s interesting is that Amazon is on the other of this arms race. As an on-line retailer, it wants people to walk into stores and then comparison shop on its site. Yes, I know it’s buying Whole Foods, but it’s still predominantly an online retailer. Maybe it patented this to prevent stores from implementing the technology.

It’s probably not nearly that strategic. It’s hard to build a business strategy around a security measure that can be defeated with cellular access.

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Using Wi-Fi to Get 3D Images of Surrounding Location

Interesting research:

The radio signals emitted by a commercial Wi-Fi router can act as a kind of radar, providing images of the transmitter’s environment, according to new experiments. Two researchers in Germany borrowed techniques from the field of holography to demonstrate Wi-Fi imaging. They found that the technique could potentially allow users to peer through walls and could provide images 10 times per second.

News article.

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Using Wi-Fi to Detect Hand Motions and Steal Passwords

This is impressive research: “When CSI Meets Public WiFi: Inferring Your Mobile Phone Password via WiFi Signals“:

Abstract: In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user’s number input. WindTalker presents a novel approach to collect the target’s CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect user’s CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone user’s input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.

That “high successful rate” is 81.7%.

News article.

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