Hypervisor Kernel Data Structure Identification via Pointer Graphs
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Solution Overview
Problem
Identifying and protecting kernel data structures within computer systems is challenging due to their undocumented nature and variability across operating systems and versions, making them attractive targets for attackers seeking to compromise system security.
Innovation Solution
A hypervisor uses a combination of static and dynamic analysis techniques to identify kernel data structures by constructing a pointer reachability graph, validating memory locations, and generating a kernel data structure layout map, thereby enabling effective protection from tampering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If kernel data structures are systematically identified and protected, then system security is improved, but device complexity increases due to the need for static and dynamic analysis techniques
Solution Approach 1:
The identification process is divided into distinct phases: static analysis phase (constructing pointer reachability graphs from binary images) and dynamic analysis phase (validating graphs during runtime). This segmentation allows each phase to focus on specific tasks, improving overall system reliability while managing complexity through structured decomposition of the protection mechanism.
Solution Approach 2:
Pointer reachability graphs are constructed in advance during the static analysis phase from binary images, before the system needs to protect kernel data structures. This preliminary construction of analysis data structures enables faster runtime validation and reduces the computational burden during critical security operations, balancing security effectiveness with system complexity.
2Measurement precision
If static and dynamic analysis techniques are used to identify kernel data structures, then identification accuracy is improved, but loss of time increases due to comprehensive analysis requirements
Solution Approach 1:
The pointer reachability graphs are constructed in advance during static analysis from binary images, performing the computationally intensive work before runtime. This preliminary action ensures high identification accuracy through thorough static analysis while reducing runtime overhead, as the graphs are pre-computed and only need validation during execution.
Solution Approach 2:
The system uses its own execution flow and memory access patterns to validate the pointer reachability graphs during runtime. By leveraging the natural execution of the virtual machine and observing actual memory accesses, the system self-validates the statically constructed graphs, improving accuracy without requiring external verification tools that would increase analysis time.
3Measurement precision
If pointer reachability graphs are validated and reduced, then kernel data structure identification accuracy is improved, but device complexity increases due to validation and reduction processes
Solution Approach 1:
During dynamic analysis, the system validates the pointer reachability graphs by comparing them against actual runtime memory access patterns. This feedback mechanism identifies discrepancies between the static model and real execution, allowing the system to refine and reduce the graphs to more accurately represent actual kernel data structure access paths, thereby improving identification accuracy while managing complexity through iterative refinement.
Solution Approach 2:
The validation and reduction processes extract only the essential and accurate portions of the pointer reachability graphs constructed during static analysis. By removing redundant or incorrect paths identified during dynamic validation, the system retains only the critical data structure relationships, improving precision while reducing the complexity of the final identification model.
Data Source
AI summary
Execution state information corresponding to an instantiated virtual machine are retrieved. A score to indicate a target memory location is able to be determined based at least in part on a source memory location is computed based at least in part on the execution state information. The score and the target memory location are indicated.