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With the rapid expansion of the Internet of Things, a vast number of microcontroller-based IoT devices are now susceptible to attacks through the Internet. Vulnerabilities within the firmware are one of the most important attack surfaces. Fuzzing has emerged as one of the most effective techniques for identifying such vulnerabilities. However, when applied to IoT firmware, several challenges arise, including: (1) the inability of firmware to execute properly in the absence of peripherals, (2) the lack of support for exploring input spaces of multiple peripherals, (3) difficulties in instrumenting and gathering feedback, and (4) the absence of a fault detection mechanism. To address these challenges, we have developed and implemented an innovative peripheral-independent hybrid fuzzing tool called . This tool enables testing of microcontroller-based firmware without reliance on specific peripheral hardware. First, a unified virtual peripheral was integrated to model the behaviors of various peripherals, thus enabling the physical devices-agnostic firmware execution. Then, a hybrid event generation approach was used to generate inputs for different peripheral accesses. Furthermore, two-level coverage feedback was collected to optimize the testcase generation. Finally, a plugin-based fault detection mechanism was implemented to identify typical memory corruption vulnerabilities. A Large-scale experimental evaluation has been performed to show ’s effectiveness and efficiency.
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Drifuzz: Harvesting Bugs in Device Drivers from Golden Seeds
Peripheral hardware in modern computers is typically assumed to be secure and not malicious, and device drivers are implemented in a way that trusts inputs from hardware. However, recent vulnerabilities such as Broadpwn have demonstrated that attackers can exploit hosts through vulnerable peripherals, highlighting the importance of securing the OS-peripheral boundary. In this paper, we propose a hardware-free concolic-augmented fuzzer targeting WiFi and Ethernet drivers, and a technique for generating high-quality initial seeds, which we call golden seeds, that allow fuzzing to bypass difficult code constructs during driver initialization. Compared to prior work using symbolic execution or greybox fuzzing, Drifuzz is more successful at automatically finding inputs that allow network interfaces to be fully initialized, and improves fuzzing coverage by 214% (3.1×) in WiFi drivers and 60% (1.6×) for Ethernet drivers. During our experiments with fourteen PCI and USB network drivers, we find twelve previously unknown bugs, two of which were assigned CVEs.
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- PAR ID:
- 10359237
- Date Published:
- Journal Name:
- USENIX Security Symposium
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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