Battling against Protocol Fuzzing: Protecting Networked Embedded Devices from Dynamic Fuzzers

Author:

Liu Puzhuo1,Zheng Yaowen2,Sun Chengnian3,Li Hong4,Li Zhi1,Sun Limin1

Affiliation:

1. Beijing Key Laboratory of IOT Information Security Technology, Institute of Information Engineering, CAS; School of Cyber Security, University of Chinese Academy of Sciences, China

2. Nanyang Technological University, Singapore

3. Cheriton School of Computer Science, University of Waterloo, Canada

4. Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences, China

Abstract

N etworked E mbedded D evices (NEDs) are increasingly targeted by cyberattacks, mainly due to their widespread use in our daily lives. Vulnerabilities in NEDs are the root causes of these cyberattacks. Although deployed NEDs go through thorough code audits, there can still be considerable exploitable vulnerabilities. Existing mitigation measures like code encryption and obfuscation adopted by vendors can resist static analysis on deployed NEDs, but are ineffective against protocol fuzzing. Attackers can easily apply protocol fuzzing to discover vulnerabilities and compromise deployed NEDs. Unfortunately, prior anti-fuzzing techniques are impractical as they significantly slow down NEDs, hampering NED availability. To address this issue, we propose Armor—the first anti-fuzzing technique specifically designed for NEDs. First, we design three adversarial primitives—delay, fake coverage, and forged exception—to break the fundamental mechanisms on which fuzzing relies to effectively find vulnerabilities. Second, based on our observation that inputs from normal users consistent with the protocol specification and certain program paths are rarely executed with normal inputs, we design static and dynamic strategies to decide whether to activate the adversarial primitives. Extensive evaluations show that Armor incurs negligible time overhead and effectively reduces the code coverage (e.g., line coverage by 22%-61%) for fuzzing, significantly outperforming the state-of-the-art.

Publisher

Association for Computing Machinery (ACM)

Subject

Software

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