(2022) HyperDbg: Reinventing Hardware-Assisted Debugging.
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Abstract
Software analysis, debugging, and reverse engineering have a crucial impact in today's software industry. Efficient and stealthy debuggers are especially relevant for malware analysis. However, existing debugging platforms fail to address a transparent, effective, and high-performance low-level debugger due to their detectable fingerprints, complexity, and implementation restrictions. In this paper, we present StealthDbg, a new hypervisor-assisted debugger for high-performance and stealthy debugging of user and kernel applications. To accomplish this, StealthDbg relies on state-of-the-art hardware features available in today's CPUs, such as VT-x and extended page tables. In contrast to other widely used existing debuggers, we design StealthDbg using a custom hypervisor, making it independent of OS functionality or API. We propose hardware-based instruction-level emulation and OS-level API hooking via extended page tables to increase the stealthiness. Our results of the dynamic analysis of 10,853 malware samples show that StealthDbg's stealthiness allows debugging on average 22% and 26% more samples than WinDbg and x64dbg, respectively. Moreover, in contrast to existing debuggers, StealthDbg is not detected by any of the 13 tested packers and protectors. We improve the performance over other debuggers by deploying a VMX-compatible script engine, eliminating unnecessary context switches. Our experiment on three concrete debugging scenarios shows that compared to WinDbg as the only kernel debugger, StealthDbg performs step-in, conditional breaks, and syscall recording, 2.98x, 1319x, and 2018x faster, respectively. We finally show real-world applications, such as a 0-day analysis, structure reconstruction for reverse engineering, software performance analysis, and code-coverage analysis.
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
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| Divisions: | Michael Schwarz (MS) |
| Conference: | CCS ACM Conference on Computer and Communications Security |
| Depositing User: | Michael Schwarz |
| Date Deposited: | 05 Sep 2022 12:58 |
| Last Modified: | 12 Sep 2022 09:32 |
| Primary Research Area: | NRA3: Threat Detection and Defenses |
| URI: | https://publications.cispa.saarland/id/eprint/3770 |
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