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Stealing Browsing History Using Your Phone’s Ambient Light Sensor

There has been a flurry of research into using the various sensors on your phone to steal data in surprising ways. Here’s another: using the phone’s ambient light sensor to detect what’s on the screen. It’s a proof of concept, but the paper’s general conclusions are correct:

There is a lesson here that designing specifications and systems from a privacy engineering perspective is a complex process: decisions about exposing sensitive APIs to the web without any protections should not be taken lightly. One danger is that specification authors and browser vendors will base decisions on overly general principles and research results which don’t apply to a particular new feature (similarly to how protections on gyroscope readings might not be sufficient for light sensor data).

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Jumping Air Gaps with Blinking Lights and Drones

Researchers have demonstrated how a malicious piece of software in an air-gapped computer can communicate with a nearby drone using a blinking LED on the computer.

I have mixed feelings about research like this. On the one hand, it’s pretty cool. On the other hand, there’s not really anything new or novel, and it’s kind of a movie-plot threat.

Research paper.

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Twofish Power Analysis Attack

New paper: “A Simple Power Analysis Attack on the Twofish Key Schedule.” This shouldn’t be a surprise; these attacks are devastating if you don’t take steps to mitigate them.

The general issue is if an attacker has physical control of the computer performing the encryption, it is very hard to secure the encryption inside the computer. I wrote a paper about this back in 1999.

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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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Self-Propagating Smart Light Bulb Worm

This is exactly the sort of Internet-of-Things attack that has me worried:

“IoT Goes Nuclear: Creating a ZigBee Chain Reaction” by Eyal Ronen, Colin OFlynn, Adi Shamir and Achi-Or Weingarten.

Abstract: Within the next few years, billions of IoT devices will densely populate our cities. In this paper we describe a new type of threat in which adjacent IoT devices will infect each other with a worm that will spread explosively over large areas in a kind of nuclear chain reaction, provided that the density of compatible IoT devices exceeds a certain critical mass. In particular, we developed and verified such an infection using the popular Philips Hue smart lamps as a platform. The worm spreads by jumping directly from one lamp to its neighbors, using only their built-in ZigBee wireless connectivity and their physical proximity. The attack can start by plugging in a single infected bulb anywhere in the city, and then catastrophically spread everywhere within minutes, enabling the attacker to turn all the city lights on or off, permanently brick them, or exploit them in a massive DDOS attack. To demonstrate the risks involved, we use results from percolation theory to estimate the critical mass of installed devices for a typical city such as Paris whose area is about 105 square kilometers: The chain reaction will fizzle if there are fewer than about 15,000 randomly located smart lights in the whole city, but will spread everywhere when the number exceeds this critical mass (which had almost certainly been surpassed already).

To make such an attack possible, we had to find a way to remotely yank already installed lamps from their current networks, and to perform over-the-air firmware updates. We overcame the first problem by discovering and exploiting a major bug in the implementation of the Touchlink part of the ZigBee Light Link protocol, which is supposed to stop such attempts with a proximity test. To solve the second problem, we developed a new version of a side channel attack to extract the global AES-CCM key that Philips uses to encrypt and authenticate new firmware. We used only readily available equipment costing a few hundred dollars, and managed to find this key without seeing any actual updates. This demonstrates once again how difficult it is to get security right even for a large company that uses standard cryptographic techniques to protect a major product.

EDITED TO ADD: BoingBoing post. Slashdot thread.

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Eavesdropping on Typing Over Voice-Over-IP

Interesting research: “Don’t Skype & Type! Acoustic Eavesdropping in Voice-Over-IP“:

Abstract: Acoustic emanations of computer keyboards represent a serious privacy issue. As demonstrated in prior work, spectral and temporal properties of keystroke sounds might reveal what a user is typing. However, previous attacks assumed relatively strong adversary models that are not very practical in many real-world settings. Such strong models assume: (i) adversary’s physical proximity to the victim, (ii) precise profiling of the victim’s typing style and keyboard, and/or (iii) significant amount of victim’s typed information (and its corresponding sounds) available to the adversary.

In this paper, we investigate a new and practical keyboard acoustic eavesdropping attack, called Skype & Type (S&T), which is based on Voice-over-IP (VoIP). S&T relaxes prior strong adversary assumptions. Our work is motivated by the simple observation that people often engage in secondary activities (including typing) while participating in VoIP calls. VoIP software can acquire acoustic emanations of pressed keystrokes (which might include passwords and other sensitive information) and transmit them to others involved in the call. In fact, we show that very popular VoIP software (Skype) conveys enough audio information to reconstruct the victim’s input ­ keystrokes typed on the remote keyboard. In particular, our results demonstrate
that, given some knowledge on the victim’s typing style and the keyboard, the attacker attains top-5 accuracy of 91:7% in guessing a random key pressed by the victim. (The accuracy goes down to still alarming 41:89% if the attacker is oblivious to both the typing style and the keyboard). Finally, we provide evidence that Skype & Type attack is robust to various VoIP issues (e.g., Internet bandwidth fluctuations and presence of voice over keystrokes), thus confirming feasibility of this attack.

News article.

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Keystroke Recognition from Wi-Fi Distortion

This is interesting research: “Keystroke Recognition Using WiFi Signals.” Basically, the user’s hand positions as they type distorts the Wi-Fi signal in predictable ways.

Abstract: Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals
can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of Channel State Information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal based keystroke recognition system called WiKey. WiKey consists of two Commercial Off-The-Shelf (COTS) WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves more than 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%.

News article.

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