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Title: Exploiting Memory Corruption Vulnerabilities in Connman for IoT Devices
In the recent past, there has been a rapid increase in attacks on consumer Internet-of-Things (IoT) devices. Several attacks currently focus on easy targets for exploitation, such as weak configurations (weak default passwords). However, with governments, industries, and organizations proposing new laws and regulations to reduce and prevent such easy targets in the IoT space, attackers will move to more subtle exploits in these devices. Memory corruption vulnerabilities are a significant class of vulnerabilities in software security through which attackers can gain control of the entire system. Numerous memory corruption vulnerabilities have been found in IoT firmware already deployed in the consumer market. This paper presents an approach for exploiting stack-based buffer-overflow attacks in IoT firmware, to hijack the device remotely. To show the feasibility of this approach, we demonstrate exploiting a common network software application, Connman, used widely in IoT firmware such as Samsung smart TVs. A series of experiments are reported on, including: crashing and executing arbitrary code in the targeted software application in a controlled environment, adopting the attacks in uncontrolled environments (with standard software defenses such as W⊕X and ASLR enabled), and installing publicly available IoT firmware that uses this software application on a Raspberry Pi. The presented exploits demonstrate the ease in which an adversary can control IoT devices.  more » « less
Award ID(s):
1757884
NSF-PAR ID:
10135009
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
Page Range / eLocation ID:
247 to 255
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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