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Narmeen Shafqat

Publications and source records attributed to Narmeen Shafqat.

2 recordsLinked to original sources

Experience Report on the Challenges and Opportunities in Securing Smartphones Against Zero-Click Attacks

Zero-click attacks require no user interaction and typically exploit zero-day (i.e., unpatched) vulnerabilities in instant chat applications (such as WhatsApp and iMessage) to gain root access to the victim's smartphone and exfiltrate sensitive data. In this paper, we report our experiences in attempting to secure smartphones against zero-click attacks. We approached the problem by first enumerating several properties we believed were necessary to prevent zero-click attacks against smartphones. Then, we created a security design that satisfies all the identified properties, and attempted to build it using off-the-shelf components. Our key idea was to shift the attack surface from the user's smartphone to a sandboxed virtual smartphone ecosystem where each chat application runs in isolation. Our performance and usability evaluations of the system we built highlighted several shortcomings and the fundamental challenges in securing modern smartphones against zero-click attacks. In this experience report, we discuss the lessons we learned, and share insights on the missing components necessary to achieve foolproof security against zero-click attacks for modern mobile devices.

cs.CR↗

ZLeaks: Passive Inference Attacks on Zigbee based Smart Homes

Zigbee is an energy-efficient wireless IoT protocol that is increasingly being deployed in smart home settings. In this work, we analyze the privacy guarantees of Zigbee protocol. Specifically, we present ZLeaks, a tool that passively identifies in-home devices or events from the encrypted Zigbee traffic by 1) inferring a single application layer (APL) command in the event's traffic, and 2) exploiting the device's periodic reporting pattern and interval. This enables an attacker to infer user's habits or determine if the smart home is vulnerable to unauthorized entry. We evaluated ZLeaks' efficacy on 19 unique Zigbee devices across several categories and 5 popular smart hubs in three different scenarios; controlled RF shield, living smart-home IoT lab, and third-party Zigbee captures. We were able to i) identify unknown events and devices (without a-priori device signatures) using command inference approach with 83.6% accuracy, ii) automatically extract device's reporting signatures, iii) determine known devices using the reporting signatures with 99.8% accuracy, and iv) identify APL commands in a public capture with 91.2% accuracy. In short, we highlight the trade-off between designing a low-power, low-cost wireless network and achieving privacy guarantees. We have also released ZLeaks tool for the benefit of the research community.

cs.CR↗