KRover: A symbolic execution engine for dynamic kernel analysis

• Main Authors: PITIGALA ARACHCHILLAGE, Pansilu Madhura Bhashana Pitigalaarachchi, DING, Xuhua, QIU, Haiqing, TU, Haoxin, HONG, Jiaqi, JIANG, Lingxiao
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2023
• 主題: dynamic kernel analysis; symbolic execution; Information Security
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/8469; https://ink.library.smu.edu.sg/context/sis_research/article/9472/viewcontent/3576915.3623198.pdf
• 機構: Singapore Management University

We present KRover, a novel kernel symbolic execution engine catered for dynamic kernel analysis such as vulnerability analysis and exploit generation. Different from existing symbolic execution engines, KRover operates directly upon a live kernel thread's virtual memory and weaves symbolic execution into the target's native executions. KRover is compact as it neither lifts the target binary to an intermediary representation nor uses QEMU or dynamic binary translation. Benchmarked against S2E, our performance experiments show that KRover is up to 50 times faster but with one tenth to one quarter of S2E memory cost. As shown in our four case studies, KRover is noise free, has the best-possible binary intimacy and does not require prior kernel instrumentation. Moreover, a user can develop her kernel analyzer that not only uses KRover as a symbolic execution library but also preserves its independent capabilities of reading/writing/controlling the target runtime. Namely, the resulting analyzer on top of KRover integrates symbolic reasoning and conventional dynamic analysis and reaps the benefits of their reinforcement to each other.