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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.
There’s a blog post from Google’s Project Zero detailing an attack against Android phones over Wi-Fi. From Ars Technica:
The vulnerability resides in a widely used Wi-Fi chipset manufactured by Broadcom and used in both iOS and Android devices. Apple patched the vulnerability with Monday’s release of iOS 10.3.1. “An attacker within range may be able to execute arbitrary code on the Wi-Fi chip,” Apple’s accompanying advisory warned. In a highly detailed blog post published Tuesday, the Google Project Zero researcher who discovered the flaw said it allowed the execution of malicious code on a fully updated 6P “by Wi-Fi proximity alone, requiring no user interaction.”
Google is in the process of releasing an update in its April security bulletin. The fix is available only to a select number of device models, and even then it can take two weeks or more to be available as an over-the-air update to those who are eligible. Company representatives didn’t respond to an e-mail seeking comment for this post.
The proof-of-concept exploit developed by Project Zero researcher Gal Beniamini uses Wi-Fi frames that contain irregular values. The values, in turn, cause the firmware running on Broadcom’s wireless system-on-chip to overflow its stack. By using the frames to target timers responsible for carrying out regularly occurring events such as performing scans for adjacent networks, Beniamini managed to overwrite specific regions of device memory with arbitrary shellcode. Beniamini’s code does nothing more than write a benign value to a specific memory address. Attackers could obviously exploit the same series of flaws to surreptitiously execute malicious code on vulnerable devices within range of a rogue access point.
Slashdot thread.
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.
Impressive remote hack of the Tesla Model S.
The vulnerability has been fixed.
Remember, a modern car isn’t an automobile with a computer in it. It’s a computer with four wheels and an engine. Actually, it’s a distributed 20-400-computer system with four wheels and an engine.
Another paper on using Wi-Fi for surveillance. This one is on identifying people by their body shape. “FreeSense:Indoor Human Identification with WiFi Signals“:
Abstract: Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages WIFI signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding WIFI signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of WIFI. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform-based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9% to 94.5% when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.
EDITED TO ADD (9/13): Related paper.
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.
This is a story of a very high-tech kidnapping:
FBI court filings unsealed last week showed how Denise Huskins’ kidnappers used anonymous remailers, image sharing sites, Tor, and other people’s Wi-Fi to communicate with the police and the media, scrupulously scrubbing meta data from photos before sending. They tried to use computer spyware and a DropCam to monitor the aftermath of the abduction and had a Parrot radio-controlled drone standing by to pick up the ransom by remote control.
The story also demonstrates just how effective the FBI is tracing cell phone usage these days. They had a blocked call from the kidnappers to the victim’s cell phone. First they used a search warrant to AT&T to get the actual calling number. After learning that it was an AT&T prepaid Tracfone, they called AT&T to find out where the burner was bought, what the serial numbers were, and the location where the calls were made from.
The FBI reached out to Tracfone, which was able to tell the agents that the phone was purchased from a Target store in Pleasant Hill on March 2 at 5:39 pm. Target provided the bureau with a surveillance-cam photo of the buyer: a white male with dark hair and medium build. AT&T turned over records showing the phone had been used within 650 feet of a cell site in South Lake Tahoe.
Here’s the criminal complaint. It borders on surreal. Were it an episode of CSI:Cyber, you would never believe it.
New research: “All Your Biases Belong To Us: Breaking RC4 in WPA-TKIP and TLS,” by Mathy Vanhoef and Frank Piessens:
Abstract: We present new biases in RC4, break the Wi-Fi Protected Access Temporal Key Integrity Protocol (WPA-TKIP), and design a practical plaintext recovery attack against the Transport Layer Security (TLS) protocol. To empirically find new biases in the RC4 keystream we use statistical hypothesis tests. This reveals many new biases in the initial keystream bytes, as well as several new long-term biases. Our fixed-plaintext recovery algorithms are capable of using multiple types of biases, and return a list of plaintext candidates in decreasing likelihood.
To break WPA-TKIP we introduce a method to generate a large number of identical packets. This packet is decrypted by generating its plaintext candidate list, and using redundant packet structure to prune bad candidates. From the decrypted packet we derive the TKIP MIC key, which can be used to inject and decrypt packets. In practice the attack can be executed within an hour. We also attack TLS as used by HTTPS, where we show how to decrypt a secure cookie with a success rate of 94% using 9*227 ciphertexts. This is done by injecting known data around the cookie, abusing this using Mantin’s ABSAB bias, and brute-forcing the cookie by traversing the plaintext candidates. Using our traffic generation technique, we are able to execute the attack in merely 75 hours.
We need to deprecate the algorithm already.
Wow:
The weak passwords—which are hard-coded and can’t be changed—were only one item on a long list of critical defects uncovered by the review. The Wi-Fi network the machines use is encrypted with wired equivalent privacy, an algorithm so weak that it takes as little as 10 minutes for attackers to break a network’s encryption key. The shortcomings of WEP have been so well-known that it was banished in 2004 by the IEEE, the world’s largest association of technical professionals. What’s more, the WINVote runs a version of Windows XP Embedded that hasn’t received a security patch since 2004, making it vulnerable to scores of known exploits that completely hijack the underlying machine. Making matters worse, the machine uses no firewall and exposes several important Internet ports.
It’s the AVS WinVote touchscreen Direct Recording Electronic (DRE). The Virginia Information Technology Agency (VITA) investigated the machine, and found that you could hack this machine from across the street with a smart phone:
So how would someone use these vulnerabilities to change an election?
- Take your laptop to a polling place, and sit outside in the parking lot.
- Use a free sniffer to capture the traffic, and use that to figure out the WEP password (which VITA did for us).
- Connect to the voting machine over WiFi.
- If asked for a password, the administrator password is “admin” (VITA provided that).
- Download the Microsoft Access database using Windows Explorer.
- Use a free tool to extract the hardwired key (“shoup”), which VITA also did for us.
- Use Microsoft Access to add, delete, or change any of the votes in the database.
- Upload the modified copy of the Microsoft Access database back to the voting machine.
- Wait for the election results to be published.
Note that none of the above steps, with the possible exception of figuring out the WEP password, require any technical expertise. In fact, they’re pretty much things that the average office worker does on a daily basis.
More.
Sidebar photo of Bruce Schneier by Joe MacInnis.