μAFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware
Wed 11 May 2022 22:25 - 22:30 at ICSE room 3-even hours - Software Testing 8 Chair(s): Darko Marinov
Fuzzing is one of the most effective approaches to finding software flaws. However, applying it to microcontroller firmware incurs many challenges. For example, rehosting-based solutions cannot accurately model peripheral behaviors and thus cannot be used to fuzz the corresponding driver code. In this work, we present μAFL, a hardware-in-the-loop approach to fuzzing microcontroller firmware. It leverages debugging tools in existing embedded system development to construct an AFL-compatible fuzzing framework. Specifically, we use the debug dongle to bridge the fuzzing environment on the PC and the target firmware on the microcontroller device. To collect code coverage information without costly code instrumentation, μAFL relies on the ARM ETM hardware debugging feature, which transparently collects the instruction trace and streams the results to the PC. However, the raw ETM data is obscure and needs enormous computing resources to recover the actual instruction flow. We therefore propose an alternative representation of code coverage, which retains the same path sensitivity as the original AFL algorithm, but can directly work on the raw ETM data without matching them with disassembled instructions. To further reduce the workload, we use the DWT hardware feature to selectively collect runtime information of interest. We evaluated μAFL on two real evaluation boards from two major vendors: NXP and STMicroelectronics. With our prototype, we discovered ten zero-day bugs in the driver code shipped with the SDK of STMicroelectronics and three zero-day bugs in the SDK of NXP. Eight CVEs have been allocated for them. Considering the wide adoption of vendor SDKs in real products, our results are alarming.
Tue 10 MayDisplayed time zone: Eastern Time (US & Canada) change
Wed 11 MayDisplayed time zone: Eastern Time (US & Canada) change
22:00 - 23:00 | Software Testing 8Technical Track / Journal-First Papers at ICSE room 3-even hours Chair(s): Darko Marinov University of Illinois at Urbana-Champaign | ||
22:00 5mTalk | The secret life of test smells - an empirical study on test smell evolution and maintenance Journal-First Papers Dong Jae Kim Concordia University, Tse-Hsun (Peter) Chen Concordia University, Jinqiu Yang Concordia University Link to publication DOI Media Attached | ||
22:05 5mTalk | Prioritizing Mutants to Guide Mutation Testing Technical Track Samuel Kaufman University of Washington, Ryan Featherman University of Washington, Justin Alvin University of Massachusetts Amherst, Bob Kurtz George Mason University, USA, Paul Ammann George Mason University, USA, René Just University of Washington DOI Pre-print Media Attached | ||
22:10 5mTalk | Automated Testing of Software that Uses Machine Learning APIs Technical Track Chengcheng Wan The University of Chicago, Shicheng Liu University of Chicago, Sophie Xie University of California, Berkeley, Yifan Liu University of Chicago, Henry Hoffmann University of Chicago, Michael Maire University of Chicago, Shan Lu University of Chicago Pre-print Media Attached | ||
22:15 5mTalk | WindRanger: A Directed Greybox Fuzzer driven by DeviationBasic Blocks Technical Track Zhengjie Du Nanjing University, Yuekang Li Nanyang Technological University, Yang Liu Nanyang Technological University, Bing Mao Nanjing University Pre-print Media Attached | ||
22:20 5mTalk | CONFETTI: Amplifying Concolic Guidance for Fuzzers Technical Track James Kukucka George Mason University, Luís Pina University of Illinois at Chicago, Paul Ammann George Mason University, USA, Jonathan Bell Northeastern University Pre-print Media Attached | ||
22:25 5mTalk | μAFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware Technical Track DOI Pre-print Media Attached |