CVE-2023-25658 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, an out of bounds read is in GRUBlockCellGrad. A fix is included in TensorFlow 2.12.0 and 2.11.1.
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Analysis
by VulDB Data Team • 03/25/2023
The vulnerability CVE-2023-25658 represents a critical out-of-bounds read flaw discovered in TensorFlow's GRUBlockCellGrad component, affecting versions prior to 2.12.0 and 2.11.1. This issue resides within the deep learning framework's recurrent neural network implementations, specifically targeting the gradient computation mechanism used in gated recurrent units. The flaw manifests when processing sequential data through GRU cells, where improper bounds checking allows memory access beyond allocated buffer boundaries. Such vulnerabilities in machine learning frameworks pose significant risks as they can be exploited during model training or inference phases, potentially leading to system instability or unauthorized data access.
The technical nature of this vulnerability aligns with CWE-129, which describes improper validation of array indices, and CWE-131, which addresses incorrect calculation of buffer or array sizes. The GRUBlockCellGrad function processes gradients for gated recurrent units in neural networks, where it performs operations on tensor data structures containing sequential information. When the function receives malformed input parameters or encounters unexpected sequence lengths during gradient computation, it fails to properly validate array access boundaries, leading to memory corruption. This type of vulnerability falls under the ATT&CK technique T1583.001, which involves developing or acquiring tools, as attackers could potentially craft malicious inputs to trigger this memory access violation during model training processes.
The operational impact of CVE-2023-25658 extends beyond simple system crashes, as it can enable more sophisticated attacks when combined with other vulnerabilities in machine learning environments. During model training, attackers who can influence input data or parameters might exploit this vulnerability to cause denial of service conditions, potentially disrupting critical machine learning workflows. In production environments where TensorFlow serves as the backend for AI applications, this flaw could be leveraged to compromise model integrity or access sensitive training data. The vulnerability is particularly concerning in cloud-based machine learning platforms where multiple users share resources, as it could allow privilege escalation or data leakage through memory corruption exploits. Organizations using TensorFlow versions prior to 2.12.0 or 2.11.1 should immediately implement mitigation strategies including version upgrades, input validation controls, and monitoring for anomalous behavior during gradient computation processes. The fix implemented in TensorFlow 2.12.0 and 2.11.1 includes proper bounds checking mechanisms that validate tensor dimensions before performing gradient calculations, ensuring that memory access operations remain within allocated boundaries and preventing unauthorized data access patterns that could expose sensitive model information or training datasets.