Paper Info
Title
Karonte: Detecting Insecure Multi-binary Interactions in Embedded Firmware
Abstract
Low-power, single-purpose embedded devices (e.g., routers and IoT devices) have become ubiquitous. While they automate and simplify many aspects of users' lives, recent large-scale attacks have shown that their sheer number poses a severe threat to the Internet infrastructure. Unfortunately, the software on these systems is hardware-dependent, and typically executes in unique, minimal environments with non-standard configurations, making security analysis particularly challenging. Many of the existing devices implement their functionality through the use of multiple binaries. This multi-binary service implementation renders current static and dynamic analysis techniques either ineffective or inefficient, as they are unable to identify and adequately model the communication between the various executables. In this paper, we present Karonte, a static analysis approach capable of analyzing embedded-device firmware by modeling and tracking multi-binary interactions. Our approach propagates taint information between binaries to detect insecure interactions and identify vulnerabilities. We first evaluated Karonte on 53 firmware samples from various vendors, showing that our prototype tool can successfully track and constrain multi-binary interactions. This led to the discovery of 46 zero-day bugs. Then, we performed a large-scale experiment on 899 different samples, showing that Karonte scales well with firmware samples of different size and complexity.
Year
DOI
Venue
2020
10.1109/SP40000.2020.00036
2020 IEEE Symposium on Security and Privacy (SP)
Keywords
DocType
ISSN
IoT devices,large-scale attacks,severe threat,Internet infrastructure,security analysis,dynamic analysis,static analysis,embedded-device firmware,insecure interactions,Karonte scales,insecure multibinary interactions,single-purpose embedded devices
Conference
1081-6011
ISBN
Citations 
PageRank 
978-1-7281-3498-7
5
0.44
References 
Authors
19
8
Name
Order
Citations
PageRank
Nilo Redini1183.21
Aravind Machiry234016.35
Ruoyu Wang328216.23
Chad Spensky4396.10
Andrea Continella5598.18
Yan Shoshitaishvili635826.98
Christopher Kruegel78799516.05
Giovanni Vigna87121507.72