Using Hover to Compromise the Confidentiality of User Input on Android

We show that the new hover (floating touch) technology, available in a number of today's smartphone models, can be abused by any Android application running with a common SYSTEM_ALERT_WINDOW permission to record all touchscreen input into other applications. Leveraging this attack, a malicious application running on the system is therefore able to profile user's behavior, capture sensitive input such as passwords and PINs as well as record all user's social interactions. To evaluate our attack we implemented Hoover, a proof-of-concept malicious application that runs in the system background and records all input to foreground applications. We evaluated Hoover with 40 users, across two different Android devices and two input methods, stylus and finger. In the case of touchscreen input by finger, Hoover estimated the positions of users' clicks within an error of 100 pixels and keyboard input with an accuracy of 79%. Hoover captured users' input by stylus even more accurately, estimating users' clicks within 2 pixels and keyboard input with an accuracy of 98%. We discuss ways of mitigating this attack and show that this cannot be done by simply restricting access to permissions or imposing additional cognitive load on the users since this would significantly constrain the intended use of the hover technology.Comment: 11 page

Detecting Mobile Application Spoofing Attacks by Leveraging User Visual Similarity Perception

Mobile application spoofing is an attack where a malicious mobile application mimics the visual appearance of another one. If such an attack is successful, the integrity of what the user sees as well as the confidentiality of what she inputs into the system can be violated by the adversary. A common example of mobile application spoofing is a phishing attack where the adversary tricks the user into revealing her password to a malicious application that resembles the legitimate one. In this work, we propose a novel approach for addressing mobile application spoofing attacks by leveraging the visual similarity of application screens. We use deception rate as a novel metric for measuring how many users would confuse a spoofing application for the genuine one. We conducted a large-scale online study where participants evaluated spoofing samples of popular mobile applications. We used the study results to design and implement a prototype spoofing detection system, tailored to the estimation of deception rate for mobile application login screens

Hacking in the Blind: (Almost) Invisible Runtime User Interface Attacks

We describe novel, adaptive user interface attacks, where the adversary attaches a small device to the interface that connects user input peripherals to the target system. The device executes the attack when the authorized user is performing safety-, or security-critical operations, by modifying or blocking user input, or injecting new events. Although the adversary fully controls the user input channel, to succeed he needs to overcome a number of challenges, including the inability to directly observe the state of the user interface and avoiding being detected by the legitimate user. We present new techniques that allow the adversary to do user interface state estimation and fingerprinting, and thus attack a new range of scenarios that previous UI attacks do not apply to. We evaluate our attacks on two different types of platforms: e-banking on general-purpose PCs, and dedicated medical terminals. Our evaluation shows that such attacks can be implemented efficiently, are hard for the users to detect, and would lead to serious violations of input integrity

Security of User Interfaces: Attacks and Countermeasures

User interfaces (UIs) are the means through which we interact with computer systems, and users perform both simple, as well as critical task through such user interfaces. For example, users visit their daily news portals, but also perform e-banking payments through user interfaces. Medical doctors use them to operate safety-critical devices such as respirators, implanted medical device programmers, etc. Given that safety- and security-critical tasks are performed through such user interfaces, it is important to secure them against attacks. Therefore, the goal of this thesis is to (1) better understand the security problems of modern user interfaces, and (2) propose novel defenses against damaging user interface attacks. There is a plethora of known user interface attack approaches that launch attacks from, e.g., a malicious application running on the target device, or from malicious peripherals (e.g., a mouse or a keyboard). Such attacks can, for example, infer user input or inject malicious input into the system. However, they commonly suffer from accuracy issues or limited attack applicability. Different systems for detecting user interface attacks were also proposed. However, they are commonly vulnerable to evasion through simple obfuscation attacks. In this thesis, we address these shortcomings and make the following contributions. First, we propose two new user interface attacks that are accurate, hard to detect, and enable previously unreachable attack scenarios. Second, we propose two new systems for detecting a particularly damaging and effective user interface attack --- phishing. Our systems are based on visual similarity and are resilient to obfuscation

On Limitations of Friendly Jamming for Confidentiality

Wireless communication provides unique security challenges, but also enables novel ways to defend against attacks. In the past few years, a number of works discussed the use of friendly jamming to protect the confidentiality of the communicated data as well as to enable message authentication and access control. In this work, we analytically and experimentally evaluate the confidentiality that can be achieved by the use of friendly jamming, given an attacker with multiple receiving antennas. We construct a MIMO-based attack that allows the attacker to recover data protected by friendly jamming and refine the conditions for which this attack is most effective. Our attack shows that friendly jamming cannot provide strong confidentiality guarantees in all settings. We further test our attack in a setting where friendly jamming is used to protect the communication to medical implants

Deniable upload and download via passive participation

Downloading or uploading controversial information can put users at risk, making them hesitant to access or share such information. While anonymous communication networks (ACNs) are designed to hide communication meta-data, already connecting to an ACN can raise suspicion. In order to enable plausible deniability while providing or accessing controversial information, we design CoverUp: a system that enables users to asynchronously upload and download data. The key idea is to involve visitors from a collaborating website. This website serves a JavaScript snippet, which, after user's consent produces cover traffic for the controversial site / content. This cover traffic is indistinguishable from the traffic of participants interested in the controversial content; hence, they can deny that they actually up- or downloaded any data. CoverUp provides a feed-receiver that achieves a downlink rate of 10 to 50 Kbit/s. The indistinguishability guarantee of the feed-receiver holds against strong global network-level attackers who control everything except for the user's machine. We extend CoverUp to a full upload and download system with a rate of 10 up to 50 Kbit/s. In this case, we additionally need the integrity of the JavaScript snippet, for which we introduce a trusted party. The analysis of our prototype shows a very small timing leakage, even after half a year of continual observation. Finally, as passive participation raises ethical and legal concerns for the collaborating websites and the visitors of the collaborating website, we discuss these concerns and describe how they can be addressed