SyzParam: Introducing Runtime Parameters into Kernel Driver Fuzzing (2501.10002v1)

Abstract: This paper introduces a novel fuzzing framework, SyzParam which incorporates runtime parameters into the fuzzing process. Achieving this objective requires addressing several key challenges, including valid value extraction, inter-device relation construction, and fuzz engine integration. By inspecting the data structures and functions associated with the LKDM, our tool can extract runtime parameters across various drivers through static analysis. Additionally, SyzParam collects inter-device relations and identifies associations between runtime parameters and drivers. Furthermore, SyzParam proposes a novel mutation strategy, which leverages these relations and prioritizes parameter modification during related driver execution. Our evaluation demonstrates that SyzParam outperforms existing fuzzing works in driver code coverage and bug-detection capabilities. To date, we have identified 30 unique bugs in the latest kernel upstreams, with 20 confirmed and 14 patched into the mainline kernel, including 9 CVEs.

• The paper introduces a novel framework that incorporates runtime parameters into kernel driver fuzzing, significantly expanding the test input space.
• It employs static analysis and taint techniques to extract, validate, and map device attributes to prioritize effective mutations in drivers.
• Empirical results show improved edge coverage and the discovery of 30 bugs, including 9 CVEs, underscoring its impact on kernel security.

Overview of Incorporating Runtime Parameters into Kernel Driver Fuzzing

This paper presents an innovative framework for kernel driver fuzzing that incorporates runtime parameters into the fuzzing process. Its primary focus is on addressing limitations in existing methodologies that have predominantly leveraged system calls and hardware interactions to expose driver vulnerabilities. The paper highlights the significant impact of previously underutilized runtime parameters, specifically device attributes and kernel module parameters, on driver execution and system security. The research identifies multiple code paths controlled by these parameters, paths previously overlooked by conventional fuzzing techniques.

The proposed framework introduces a novel fuzzing tool that integrates runtime parameters through static and dynamic analyses, effectively expanding the input space accessible for fuzzing. Key challenges addressed include the extraction of valid parameter values, establishing relationships between runtime parameters and drivers, and integrating parameter manipulation into the fuzz engine for efficient and reproducible fuzzing. These complexities are resolved through a comprehensive static analysis phase, identifying store functions associated with runtime parameters and utilizing taint analysis to trace control flows affected by user-modifiable data.

Key Contributions

1. Parameter Extraction and Value Validation: Static analysis is employed to identify struct device_attribute variables and their associated store functions from the Linux kernel source. This identification phase is critical in ensuring the integrity and efficacy of parameter-driven fuzzing as it discerns valid input values likely to pass kernel validation checks.
2. Relationship Mapping: The research identifies and maps inter-device relationships and parameter-to-driver associations via a constructed device relation tree. This mapping provides a systematic way to prioritize parameter modifications that are likely to yield new driver execution paths and surface hidden vulnerabilities.
3. Engine Integration and Mutation Strategy: The framework implements a mutation strategy within Syzkaller's existing fuzzing loop, embedding parameter modifications to synergize with traditional system call-driven driver execution. This dual-action approach enhances the likelihood of bug discovery by replaying complex interaction scenarios potentially overlooked by prior methods.

Empirical Evaluation and Results

The framework's efficacy is substantiated through comprehensive evaluations, highlighting a marked improvement in edge coverage and bug detection capabilities across a selection of Linux kernel drivers. The framework uncovered 30 unique bugs, with a notable subset of these discoveries having been confirmed and patched in the mainline kernel, inclusive of nine CVEs. This indicates a substantial stride in enhancing the visibility and reliability of Linux drivers by uncovering execution paths obscured in typical fuzzing processes.

Additionally, the framework's combination with existing methodologies demonstrated complementary benefits, evidencing enhanced cumulative coverage and vulnerability detection beyond the standalone capabilities of preceding works such as SyzGen++ and SyzDescribe.

Implications and Future Directions

The incorporation of runtime parameters into fuzzing practices represents a significant paradigm shift in vulnerability discovery within monolithic kernel architectures. By moving beyond the limitations of pre-defined syscalls, this research unlocks previously inaccessible portions of the driver codebase, underscoring the latent risks posed by device interrelationships and runtime configurations.

Looking ahead, the approach set forth in this paper can be expanded to address dynamic contexts and runtime states, such as power management and performance counters, which could offer additional layers of insight into kernel behavior under varying operational conditions. Furthermore, cross-architecture applicability remains an open avenue for refining fuzzing heuristics to accommodate diverse device ecosystems in heterogeneous environments.

In summary, this paper delineates a methodical advance in kernel Fuzzing, accentuating the critical role of runtime parameters and presenting a robust framework that sets a benchmark for future exploration in kernel security research.