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SECURING SYSTEM AND EMBEDDED SOFTWARE VIA FUZZING

thesis
posted on 2022-12-05, 19:37 authored by Kyungtae KimKyungtae Kim

    

System software is a lucrative target for cyber attacks due to its high privilege and large attack surfaces. While fuzzing has been proven effective for decades, recent fuzzers still suffer from limited coverage when dealing with real-world system programs, such as OS kernels, firmware due to their unique interfaces, and large input space, etc. 

In this thesis, we aim to secure various system and embedded software, such as OS kernels, device drivers and firmware, using proposed fuzzing techniques tailored for each system software. First, we present HFL, hybrid fuzzing for the Linux kernel. HFL achieves hybrid kernel fuzzing scheme with a faithful combination of traditional fuzzing and concolic execution. Furthermore, HFL addresses essential challenges in the Linux kernel via three distinct features: 1) converting indirect control transfers to direct transfers, 2) inferring system call dependencies, and 3) identifying nested arguments structures. HFL found 24 previously unknown bugs in different Linux kernels, and achieved higher code coverage than baseline kernel fuzzers. 

While the security of USB host stacks has gotten lots of attention, USB gadget stacks are left behind, leaving their vulnerabilities unfixed. To secure USB gadget stacks, we propose the first USB gadget stack fuzzing, FuzzUSB. As a stateful fuzzer, FuzzUSB extracts USB gadget state machines from USB gadget drivers, and uses them to achieve state-guided fuzzing through multi-channel inputs. FuzzUSB has found total 34 previously-unknown bugs within the Linux and Android kernels, and demonstrated improved bug-finding efficiency with high code coverage. 

As USB Power Delievery (USBPD) is becoming prevalent, but vulnerable to cyber attacks, there is an increasing need for its security. To achieve secure USBPD communications, we propose FuzzPD, the first black-box USBPD fuzzing technique. FuzzPD leverages a dual-role state machine extracted from USBPD specifications. Guided by the dual-role state machine, FuzzPD performs multi-level mutations, not only achieving state-coverage guided mutation for inter-state exploration, but also leveraging input seeding especially for in-state mutation. FuzzPD discovered 45 USBPD bugs in total, ranging from over-charging bugs to memory access violation. 

History

Degree Type

  • Doctor of Philosophy

Department

  • Computer Science

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dave (Jing) Tian

Additional Committee Member 2

Dongyan Xu

Additional Committee Member 3

Antonio Bianchi

Additional Committee Member 4

Berkay Ceilk

Additional Committee Member 5

Sonia Fahmy

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