CVE-2023-38677 in Paddle
Summary
by MITRE • 01/03/2024
FPE in paddle.linalg.eig 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-2023-38677 represents a floating point exception within the paddle.linalg.eig function of the PaddlePaddle deep learning framework. This issue affects versions prior to 2.6.0 and manifests as an unhandled exception during eigenvalue computation operations. The flaw specifically occurs in the linear algebra module where the eig function processes matrix operations to compute eigenvalues and eigenvectors. When encountering certain problematic input matrices or edge cases during the computation process, the function fails to properly handle floating point operations, leading to abrupt program termination. This behavior constitutes a denial of service condition that can be exploited by attackers who can craft malicious inputs to trigger the crash.
The technical implementation of this vulnerability stems from inadequate error handling within the eigenvalue decomposition algorithm. The paddle.linalg.eig function likely employs numerical methods such as QR decomposition or similar iterative approaches to compute eigenvalues, where floating point precision issues or invalid matrix conditions can cause division by zero operations or other mathematically undefined scenarios. When these conditions occur without proper exception handling, the runtime environment terminates the process abruptly. This type of flaw aligns with CWE-191, which describes integer underflow conditions, though in this case it manifests as a floating point exception due to improper handling of mathematical operations. The vulnerability can be classified as a software fault that leads to system instability rather than a direct security breach.
The operational impact of CVE-2023-38677 extends beyond simple service disruption to potentially affect production systems that rely on PaddlePaddle for machine learning workloads. Organizations deploying deep learning applications using affected versions may experience unexpected system crashes during model training or inference phases when processing certain datasets. The denial of service condition can be particularly damaging in cloud environments or production systems where continuous availability is critical. Attackers could potentially exploit this vulnerability by submitting specially crafted matrix inputs designed to trigger the floating point exception, leading to repeated service interruptions. This vulnerability also poses risks in automated deployment pipelines where continuous integration systems might fail due to the unexpected crashes during model execution. The impact is further amplified in distributed computing environments where a single node failure could affect entire training or inference workflows.
Mitigation strategies for CVE-2023-38677 primarily focus on upgrading to PaddlePaddle version 2.6.0 or later, where the floating point exception handling has been improved. Organizations should conduct thorough testing of their applications after upgrading to ensure that no regressions have been introduced. Additionally, implementing input validation mechanisms can help reduce the likelihood of triggering the vulnerability through malicious inputs, though this approach is less effective than the official patch. System administrators should monitor for unusual crash patterns or service interruptions that might indicate exploitation attempts. The vulnerability demonstrates the importance of robust error handling in numerical computing libraries, particularly in machine learning frameworks where mathematical operations form the core of functionality. Organizations should also consider implementing process monitoring and restart mechanisms to minimize downtime when such vulnerabilities are encountered in production environments. This issue highlights the need for comprehensive testing of mathematical libraries under edge case conditions and proper exception handling in scientific computing software to prevent denial of service scenarios that could be exploited in adversarial environments.