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Boru Lin

dblp:352/7086 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Systems and software security · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Systems and software security › vulnerability discovery › fuzzing
firmware fuzzing
0.712023
RSFuzzer: Discovering Deep SMI Handler Vulnerabilities in UEFI Firmware with Hybrid Fuzzing · SP 2023
Systems and software security › vulnerability discovery › fuzzing
hybrid fuzzing
0.712023
RSFuzzer: Discovering Deep SMI Handler Vulnerabilities in UEFI Firmware with Hybrid Fuzzing · SP 2023
Systems and software security
vulnerability discovery
0.712023
RSFuzzer: Discovering Deep SMI Handler Vulnerabilities in UEFI Firmware with Hybrid Fuzzing · SP 2023
Embedded and real-time systems › embedded system security
firmware security
0.212023
RSFuzzer: Discovering Deep SMI Handler Vulnerabilities in UEFI Firmware with Hybrid Fuzzing · SP 2023

Methods — techniques the papers use, named apart from their topics

input interface learning · 1.3hybrid greybox fuzzing · 1.3
YearPublicationVenuePosition
2023 RSFuzzer: Discovering Deep SMI Handler Vulnerabilities in UEFI Firmware with Hybrid Fuzzing
abstract
System Management Mode (SMM) is a secure operation mode for x86 processors supported by Unified Extensible Firmware Interface (UEFI) firmware. SMM is designed to provide a secure execution environment to access highly privileged data or control low-level hardware (such as power management). The programs running in SMM are called SMM drivers and System Management Interrupt (SMI) handlers are the most important components of SMM drivers since they are the only components to receive and handle data from outside the SMM execution environment. Although SMM can serve as an extra layer of protection when the operating system is compromised, vulnerabilities in SMM drivers, especially SMI handlers, can invalidate this protection and cause severe damages to the device. Thus, early detection of SMI handler vulnerabilities is important for UEFI firmware security.To this end, researchers have proposed to use hybrid fuzzing techniques for detecting SMI handler vulnerabilities. Particularly, Intel has developed a hybrid fuzzer called Excite and uses it to secure Intel products. Although existing hybrid fuzzing techniques can detect vulnerabilities in SMI handlers, their effectiveness is limited due to two major pitfalls: 1) They can only feed input through the most common input interface to SMI handlers, lacking the ability to utilize other input interfaces. 2) They have no awareness of variables shared by multiple SMI handlers, lacking the ability to explore code segments related to such variables. By addressing the challenges faced by existing works, we propose RSFuzzer, a hybrid greybox fuzzing technique which can learn input interface and format information and detect deeply hidden vulnerabilities which are triggered by invoking multiple SMI handlers. We implemented RSFuzzer and evaluated it on 16 UEFI firmware images provided by six vendors. The experiment results show that RSFuzzer can cover 617% more basic blocks and detect 828% more vulnerabilities on average than the state-of-the-art hybrid fuzzing technique. Moreover, we found and reported 65 0-day vulnerabilities in the evaluated UEFI firmware images and 14 CVE IDs were assigned. Noticeably, 6 of the 0-day vulnerabilities were found in commercial-off-the-shelf (COTS) products from Intel, which might have been tested by Excite before releasing.
Jiawei Yin, Yuekang Li, Boru Lin, Yanyan Zou 0002, Yang Liu 0003, Wei Huo 0005, Jingling Xue
SP5