CVE-2023-38676 in Paddle
Summary
by MITRE • 01/03/2024
Nullptr in paddle.dot 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-2023-38676 represents a critical null pointer dereference flaw within the PaddlePaddle machine learning framework, specifically affecting versions prior to 2.6.0. This issue resides in the paddle.dot function which is commonly used for computing dot products in neural network operations and mathematical computations. The flaw manifests when the function receives null input parameters or when internal memory allocation fails, leading to an attempt to dereference a null pointer during execution. Such conditions can occur during normal operation when users pass malformed or unexpected input data to the dot product computation routine, or when the underlying tensor memory management encounters allocation failures during complex mathematical operations.
The technical implementation of this vulnerability stems from inadequate input validation and error handling within the paddle.dot function implementation. When processing tensor operations, the function fails to properly validate that input tensors are properly initialized and contain valid memory references before attempting mathematical computations. This null pointer dereference condition can be triggered through various attack vectors including malformed model inputs, incorrect tensor dimension specifications, or when the system encounters memory allocation failures during intensive computational tasks. The vulnerability specifically aligns with CWE-476 which categorizes null pointer dereference conditions as a fundamental programming error that can lead to application crashes and system instability. The flaw operates at the runtime level where the application attempts to access memory locations that have not been properly allocated or initialized, resulting in immediate termination of the process.
The operational impact of this vulnerability extends beyond simple application crashes to potentially enable denial of service attacks against systems relying on PaddlePaddle for machine learning workloads. When exploited, the null pointer dereference causes immediate runtime termination of the application process, disrupting ongoing computational tasks and potentially affecting entire machine learning pipelines. Systems utilizing PaddlePaddle for inference services, training operations, or automated model deployment processes become vulnerable to service interruption attacks that can be executed by malicious actors providing crafted inputs to trigger the null pointer condition. The vulnerability particularly affects production environments where continuous availability of machine learning services is critical, as even a single instance of the flaw can cause complete service disruption. This weakness can be leveraged by attackers to perform denial of service attacks against web applications, cloud services, or edge computing platforms that depend on PaddlePaddle for their computational operations, as outlined in the ATT&CK framework under technique T1499 for network denial of service attacks.
Mitigation strategies for CVE-2023-38676 primarily focus on immediate version upgrading to PaddlePaddle 2.6.0 or later, which includes proper input validation and null pointer checks within the paddle.dot function implementation. Organizations should implement comprehensive patch management procedures to ensure all systems running PaddlePaddle are updated with the latest security fixes. Additional protective measures include input sanitization for all tensor operations, implementing robust error handling mechanisms, and deploying monitoring systems to detect anomalous application behavior that might indicate exploitation attempts. Security teams should also consider implementing application sandboxing or containerization strategies to limit the impact of potential exploitation, while network segmentation can help prevent lateral movement if the vulnerability is exploited in a broader attack chain. The vulnerability demonstrates the importance of proper memory management and input validation in mathematical computing frameworks, aligning with industry best practices for preventing memory safety issues in deep learning software implementations.