Model-based Fuzzing for Finding Kernel Vulnerabilities
Abstract
Kernel vulnerabilities are critical in security because they naturally allow attackers to gain unprivileged root access.Existing ke"rnel fuzzing techniques involve feeding in random input values to kernel API functions, but such a simple approach does not reveal l""atent bugs deep in the kernel code, because many API functions are dependent on each other, and they can quickly reject arbitrary pa"rameter values based on their calling context. In this project we propose a novel kernel fuzzing algorithm that infers the kernel AP"I model from regular program executions, and uses the information to fuzz an OS kernel API functions. The expected outcome is twofol""d: (1) the design of an API model inference algorithm, and (2) a tool for Windows kernel fuzzing that leverages the developed algori"thm.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 20, 2017
- Source ID
- N000141812024
Entities
People
- Sang Kil Cha
Organizations
- KAIST
- Office of Naval Research
- United States Navy