To study the security properties of the Internet of Things (IoT), firmware analysis is crucial. In the past, many works have been focused on analyzing Linux-based firmware. Less known is the security landscape of MCU-based IoT devices, an essential portion of the IoT ecosystem. Existing works on MCU firmware analysis either leverage the companion mobile apps to infer the security properties of the firmware (thus unable to collect low-level properties) or rely on small-scale firmware datasets collected in ad-hoc ways (thus cannot be generalized). To fill this gap, we create a large dataset of MCU firmware for real IoT devices. Our approach statically analyzes how MCU firmware is distributed and then captures the firmware. To reliably recognize the firmware, we develop a firmware signature database, which can match the footprints left in the firmware compilation and packing process. In total, we obtained 8,432 confirmed firmware images (3,692 unique) covering at least 11 chip vendors across 7 known architectures and 2 proprietary architectures. We also conducted a series of static analyses to assess the security properties of this dataset. The result reveals three disconcerting facts: 1) the lack of firmware protection, 2) the existence of N-day vulnerabilities, and 3) the rare adoption of security mitigation.
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Detecting Vulnerabilities in Linux-Based Embedded Firmware with SSE-Based On-Demand Alias Analysis
Although the importance of using static taint analysis to detect taint-style
vulnerabilities in Linux-based embedded firmware is widely
recognized, existing approaches are plagued by following major
limitations: (a) Existing works cannot properly handle indirect call
on the path from attacker-controlled sources to security-sensitive
sinks, resulting in lots of false negatives. (b) They employ heuristics
to identify mediate taint source and it is not accurate enough, which
leads to high false positives.
To address issues, we propose EmTaint, a novel static approach
for accurate and fast detection of taint-style vulnerabilities in Linux-based
embedded firmware. In EmTaint, we first design a structured
symbolic expression-based (SSE-based) on-demand alias analysis
technique. Based on it, we come up with indirect call resolution
and accurate taint analysis scheme. Combined with sanitization
rule checking, EmTaint can eventually discovers a large number of
taint-style vulnerabilities accurately within a limited time. We evaluated
EmTaint against 35 real-world embedded firmware samples
from six popular vendors. The result shows EmTaint discovered
at least 192 vulnerabilities, including 41 n-day vulnerabilities and
151 0-day vulnerabilities. At least 115 CVE/PSV numbers have been
allocated from a subset of the reported vulnerabilities at the time of
writing. Compared with state-of-the-art tools such as KARONTE
and SaTC, EmTaint found significantly more vulnerabilities on the
same dataset in less time.
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- Award ID(s):
- 2019340
- PAR ID:
- 10464279
- Date Published:
- Journal Name:
- Proc. of the ACM SIGSOFT International Symposium on Software Testing and Analysis
- Page Range / eLocation ID:
- 360 to 372
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3597926.3598062
