CVE-2021-33652 in MindSpore
Summary
by MITRE • 06/27/2022
When the Reduce operator run operation is executed, if there is a value of 0 in the parameter axis_sizes element, it will cause a division by 0 exception.
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Analysis
by VulDB Data Team • 07/15/2022
The vulnerability described in CVE-2021-33652 represents a critical division by zero error within the Reduce operator execution logic of a machine learning framework or computational library. This flaw occurs specifically when processing tensor operations where the axis_sizes parameter contains a zero value, leading to a mathematical exception that can disrupt normal program execution. The issue manifests during the runtime execution of reduction operations, which are fundamental building blocks in deep learning and data processing pipelines. Such operations are commonly used in neural network training and inference processes, making this vulnerability particularly concerning for systems relying on automated tensor computations. The vulnerability directly impacts the stability and reliability of computational frameworks that handle multi-dimensional data structures, potentially causing system crashes or unexpected behavior in production environments.
This technical flaw falls under the category of arithmetic exceptions and can be classified according to CWE-369 as a division by zero error in a security-sensitive context. The vulnerability exploits a fundamental mathematical operation error where the system fails to validate input parameters before performing division operations. When the axis_sizes element contains a zero value, the reduction algorithm attempts to divide by this zero value, resulting in an arithmetic exception that terminates the execution flow. The operational impact extends beyond simple crashes as this error can be leveraged to cause denial of service conditions in applications that rely heavily on tensor processing. The vulnerability demonstrates poor input validation practices and highlights the importance of defensive programming techniques in security-sensitive code paths. Attackers could potentially craft malicious inputs containing zero values in axis_sizes parameters to trigger these exceptions, leading to system instability or complete service interruption.
The operational consequences of this vulnerability are significant for organizations deploying machine learning frameworks or computational libraries that process large datasets through tensor operations. Systems utilizing this functionality may experience unexpected termination of processes, particularly in high-throughput environments where reduction operations occur frequently. The vulnerability affects the availability and reliability of computational services, potentially impacting critical applications such as real-time analytics, automated decision-making systems, or machine learning model inference engines. In enterprise environments, this could result in production outages, data processing delays, or complete service unavailability during critical operations. The vulnerability also raises concerns about the robustness of error handling mechanisms within computational frameworks, as proper exception handling should prevent such arithmetic errors from propagating to system-level failures. Organizations using affected libraries may need to implement immediate patches or workarounds to prevent exploitation and maintain system stability.
Mitigation strategies for CVE-2021-33652 should focus on implementing comprehensive input validation and error handling within the reduction operator logic. The most effective approach involves adding explicit checks to validate axis_sizes parameters before any division operations are performed, ensuring that zero values are either rejected or handled gracefully through alternative computational paths. Security teams should implement automated testing procedures that specifically target edge cases involving zero values in parameter arrays to identify similar vulnerabilities in other mathematical operations. The implementation of defensive programming techniques, including bounds checking and parameter validation, should be enforced across all mathematical operations that involve division or modulo calculations. Additionally, organizations should consider implementing monitoring and alerting mechanisms to detect unusual patterns in tensor processing that may indicate exploitation attempts. Updates to affected frameworks should be prioritized to ensure that proper exception handling and input validation mechanisms are in place to prevent division by zero errors from causing system instability. The remediation process should also include comprehensive regression testing to verify that the fix does not introduce new behavioral changes or performance regressions in legitimate use cases.