CVE-2023-52302 in Paddle
Summary
by MITRE • 01/03/2024
Nullptr in paddle.nextafter 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-2023-52302 affects PaddlePaddle, an open-source deep learning framework widely used for building and training machine learning models. This issue resides within the paddle.nextafter function which is part of the mathematical operations library used in numerical computations. The flaw manifests as a null pointer dereference when the function encounters certain input parameters that result in unexpected null value handling during computation. Such vulnerabilities are particularly concerning in machine learning frameworks where numerical stability and robust error handling are paramount for maintaining system integrity.
The technical implementation of this vulnerability stems from inadequate input validation and error handling within the paddle.nextafter function. When processing specific floating-point values or edge cases in numerical computations, the function fails to properly check for null pointers before dereferencing them. This null pointer dereference occurs during runtime execution when the framework attempts to perform mathematical operations that require proper memory allocation and pointer validation. The vulnerability is classified under CWE-476 as a null pointer dereference, which represents a fundamental programming error that can lead to application crashes and system instability. The specific nature of this flaw demonstrates poor defensive programming practices in the mathematical computation module of the framework.
The operational impact of CVE-2023-52302 extends beyond simple application crashes to potentially enable denial of service attacks against systems relying on PaddlePaddle for machine learning workloads. When exploited, this vulnerability can cause runtime crashes that may affect entire training processes or inference pipelines, particularly in production environments where continuous operation is critical. Attackers could potentially craft malicious inputs that trigger this null pointer dereference, leading to service disruption and potential data loss. The vulnerability affects systems running PaddlePaddle versions prior to 2.6.0, making it relevant to organizations that have not yet updated their machine learning infrastructure. This type of vulnerability aligns with ATT&CK technique T1499.004 which involves network denial of service attacks, as the denial of service aspect can be leveraged to disrupt machine learning operations and computational services.
Organizations should prioritize immediate mitigation by upgrading to PaddlePaddle version 2.6.0 or later, which includes proper null pointer checks and improved error handling for the affected function. Additionally, implementing input validation mechanisms and robust error handling within applications using PaddlePaddle can provide additional defense-in-depth measures. System administrators should monitor for unusual crash patterns or service disruptions that might indicate exploitation attempts. The vulnerability highlights the importance of thorough testing for edge cases in mathematical libraries and underscores the need for comprehensive security reviews of numerical computation functions. Regular security updates and patch management processes should be enforced to prevent similar vulnerabilities from affecting machine learning infrastructure. Organizations using PaddlePaddle in production environments must also consider implementing automated monitoring and alerting systems to detect potential exploitation attempts and ensure rapid response to security incidents.