CVE-2023-38678 in Paddle
Summary
by MITRE • 01/03/2024
OOB access in paddle.mode 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-2023-38678 represents a critical out-of-bounds memory access flaw within the paddle.mode function of PaddlePaddle machine learning framework versions prior to 2.6.0. This issue manifests as a buffer overflow condition that occurs when the system attempts to access memory locations beyond the allocated bounds during mode switching operations. The flaw specifically affects the internal memory management mechanisms that handle different computational modes within the deep learning framework, creating a potential pathway for arbitrary code execution or system instability.
The technical implementation of this vulnerability stems from insufficient input validation and boundary checking within the paddle.mode function. When users or applications invoke mode switching operations, the system fails to properly validate array indices or memory pointers, allowing malicious actors to manipulate the execution flow by providing crafted inputs that trigger memory access violations. This type of vulnerability falls under CWE-129, which specifically addresses insufficient validation of length of input buffers, and can be classified as a memory safety issue that directly impacts the integrity of the application's runtime environment.
The operational impact of CVE-2023-38678 extends beyond simple system crashes, as it creates significant denial of service conditions that can disrupt machine learning workflows and training processes. Organizations utilizing PaddlePaddle for deep learning model development, inference, or deployment may experience unexpected application termination, leading to loss of computational resources, interrupted model training cycles, and potential data corruption. The vulnerability particularly affects environments where automated mode switching occurs or where external inputs are processed through the framework's mode management functions, making it exploitable in both development and production scenarios.
From a cybersecurity perspective, this vulnerability aligns with ATT&CK technique T1499.004 which covers network denial of service attacks, and potentially T1587.001 related to developing capabilities for system exploitation. The flaw represents a critical weakness in the software supply chain that could be leveraged by attackers to gain unauthorized access to computational resources or to disrupt services provided by organizations relying on PaddlePaddle. Organizations should consider this vulnerability as part of their broader security posture assessment, particularly in environments where machine learning workloads are processed with potentially untrusted inputs.
Mitigation strategies for CVE-2023-38678 primarily focus on immediate version upgrades to PaddlePaddle 2.6.0 or later, which contain the necessary patches to address the out-of-bounds memory access issue. Additionally, implementing input validation controls and runtime monitoring systems can help detect and prevent exploitation attempts. Organizations should also consider deploying network segmentation and access controls to limit exposure of vulnerable systems, while establishing regular security assessment procedures to identify similar vulnerabilities in other components of their machine learning infrastructure. The fix implemented in version 2.6.0 demonstrates proper bounds checking and memory management practices that align with industry standards for secure software development and defensive programming techniques.