CVE-2023-38675 in Paddle
Summary
by MITRE • 01/03/2024
FPE in paddle.linalg.matrix_rank in PaddlePaddle before 2.6.0. This flaw can cause a runtime crash and a denial of service.
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Analysis
by VulDB Data Team • 01/23/2024
The vulnerability identified as CVE-2023-38675 represents a floating point exception within the paddle.linalg.matrix_rank function of the PaddlePaddle deep learning framework. This issue affects versions prior to 2.6.0 and manifests as an improper handling of certain matrix inputs that leads to runtime crashes. The flaw specifically occurs during the execution of matrix rank calculations where the system encounters exceptional conditions that are not properly anticipated or managed by the code implementation. Such exceptions typically arise when processing matrices with specific mathematical properties that trigger undefined behavior in the underlying floating point arithmetic operations.
The technical implementation of this vulnerability stems from inadequate input validation and error handling within the matrix_rank function. When the function processes certain edge cases or malformed matrix data structures, it fails to properly check for conditions that could lead to floating point exceptions during computation. This deficiency allows attackers to craft specific inputs that will cause the application to terminate unexpectedly, resulting in a denial of service condition. The vulnerability operates at the level of mathematical computation where matrix operations involve complex floating point calculations, and the failure to handle exceptional cases properly leads to abrupt program termination.
From an operational impact perspective, this vulnerability poses significant risks to systems that rely on PaddlePaddle for machine learning workloads. The denial of service aspect means that applications depending on this functionality could experience complete service interruption, particularly in production environments where continuous operation is critical. The crash condition affects not only individual computations but can potentially bring down entire processing pipelines that depend on matrix rank calculations for various operations including neural network training, data analysis, and mathematical modeling tasks. Organizations using PaddlePaddle in mission-critical applications face potential downtime and operational disruption.
The vulnerability aligns with CWE-191, which addresses integer underflow conditions, and relates to broader categories of improper input validation and exception handling. From an attack perspective, this flaw maps to ATT&CK technique T1499.004, specifically targeting application availability through denial of service mechanisms. The attack surface is particularly relevant in environments where PaddlePaddle is used for automated processing or when the framework is exposed to untrusted input data. Mitigation strategies include immediate upgrade to PaddlePaddle version 2.6.0 or later where the fix has been implemented, along with implementing additional input validation layers and robust error handling mechanisms. Organizations should also consider implementing monitoring solutions to detect unusual crash patterns and establish incident response procedures for handling such availability threats.