CVE-2023-52307 in Paddle
Summary
by MITRE • 01/03/2024
Stack overflow in paddle.linalg.lu_unpack in PaddlePaddle before 2.6.0. This flaw can lead to a denial of service, or even more damage.
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Analysis
by VulDB Data Team • 01/23/2024
The vulnerability CVE-2023-52307 represents a critical stack overflow condition within the paddle.linalg.lu_unpack function of the PaddlePaddle deep learning framework. This issue affects versions prior to 2.6.0 and demonstrates a classic buffer management flaw that can be exploited to disrupt system operations. The vulnerability resides in the linear algebra component of the framework, specifically within the LU decomposition unpacking functionality that is commonly used in machine learning workflows involving matrix operations.
The technical flaw manifests when the lu_unpack function processes input parameters without adequate bounds checking or stack space validation. This allows an attacker to provide maliciously crafted input data that exceeds the allocated stack buffer size, causing a stack overflow condition. The vulnerability follows CWE-121 stack-based buffer overflow classification, where insufficient boundary checking enables data to overwrite adjacent stack memory locations. When the function attempts to process oversized input, the stack corruption can lead to unpredictable program behavior including crashes, memory corruption, or potential code execution.
The operational impact of this vulnerability extends beyond simple denial of service scenarios. While the primary effect is service disruption through application crashes, the stack overflow condition could potentially be leveraged for more sophisticated attacks depending on the execution environment. Systems utilizing PaddlePaddle for machine learning inference or training operations may experience complete service interruption when the vulnerable function is invoked with malicious input. The vulnerability affects both CPU and GPU execution paths within the framework, making it particularly concerning for production environments where continuous operation is critical. Organizations deploying PaddlePaddle in cloud or edge computing scenarios face increased risk due to the potential for remote exploitation.
Mitigation strategies should focus on immediate version upgrading to PaddlePaddle 2.6.0 or later where the stack overflow vulnerability has been addressed through proper input validation and buffer management. System administrators should implement comprehensive input sanitization protocols and consider deploying additional runtime protections such as stack canaries or address space layout randomization to reduce exploitability. The vulnerability aligns with ATT&CK technique T1499.004 for network denial of service and may also map to T1059 for command execution if exploitation leads to code injection. Organizations should conduct thorough code reviews of any custom implementations that interact with the lu_unpack function and implement monitoring for suspicious usage patterns that might indicate exploitation attempts. Regular security assessments and vulnerability scanning should be performed to identify potential exposure to similar buffer overflow conditions in other framework components.