CVE-2023-25659 in TensorFlow
Summary
by MITRE • 03/25/2023
TensorFlow is an open source platform for machine learning. Prior to versions 2.12.0 and 2.11.1, if the parameter `indices` for `DynamicStitch` does not match the shape of the parameter `data`, it can trigger an stack OOB read. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.
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Analysis
by VulDB Data Team • 03/25/2023
The vulnerability identified as CVE-2023-25659 affects TensorFlow, a widely-used open-source machine learning platform that serves as the foundation for numerous artificial intelligence applications across various industries. This security flaw specifically resides within the DynamicStitch operation, which is a fundamental component for stitching together tensors along a specified axis. The vulnerability manifests when the indices parameter fails to align with the expected shape of the data parameter, creating a condition that allows for out-of-bounds memory access. This particular issue represents a critical security concern as it can potentially be exploited to execute arbitrary code or cause system instability, particularly in environments where TensorFlow is deployed for processing sensitive data or in production systems where reliability is paramount.
The technical implementation of this vulnerability stems from inadequate input validation within the DynamicStitch function's parameter handling mechanism. When TensorFlow processes the indices and data parameters, it performs shape checking to ensure compatibility between the two inputs. However, in versions prior to 2.12.0 and 2.11.1, this validation process contains a flaw that allows an attacker to craft malicious inputs where the indices parameter's dimensions do not match the data parameter's expected structure. This mismatch creates a scenario where the system attempts to access memory locations beyond the allocated stack buffer, resulting in a stack-based out-of-bounds read condition. The vulnerability is classified as a CWE-129 weakness, which specifically addresses issues related to insufficient input validation and improper handling of array indices that can lead to memory corruption vulnerabilities.
The operational impact of this vulnerability extends beyond simple code execution concerns, as it can significantly compromise the integrity and availability of machine learning systems that rely on TensorFlow for their core operations. Attackers could potentially exploit this flaw to gain unauthorized access to sensitive data processed through TensorFlow pipelines, particularly in scenarios where the platform handles confidential information such as medical records, financial data, or personal user information. The vulnerability affects systems running TensorFlow versions 2.10.x and earlier, making it particularly concerning for organizations that have not yet upgraded their machine learning infrastructure. Additionally, the flaw's potential for causing system crashes or unpredictable behavior means that production environments relying on TensorFlow could experience service disruptions, data corruption, or complete system failures that could have severe financial and operational consequences.
Mitigation strategies for CVE-2023-25659 primarily focus on immediate version upgrades to TensorFlow 2.12.0 or 2.11.1, which contain the necessary patches to address the stack buffer overflow condition. Organizations should prioritize updating their TensorFlow installations across all environments, including development, testing, and production systems, to eliminate exposure to this vulnerability. Beyond the immediate upgrade, system administrators should implement comprehensive monitoring and logging mechanisms to detect any potential exploitation attempts, particularly in environments where TensorFlow is exposed to untrusted inputs or data sources. Security teams should also conduct thorough vulnerability assessments of their machine learning pipelines to identify any other potential entry points that could be exploited in conjunction with this vulnerability. The fix implemented in the patched versions addresses the core issue by strengthening parameter validation and ensuring proper bounds checking before any memory access operations are performed, thereby preventing the out-of-bounds read conditions that could be leveraged for malicious purposes. This remediation aligns with the ATT&CK framework's mitigation strategies for memory corruption vulnerabilities, emphasizing the importance of input validation and proper bounds checking in preventing exploitation of similar weaknesses in machine learning frameworks.