CVE-2021-37643 in TensorFlow
Summary
by MITRE • 08/12/2021
TensorFlow is an end-to-end open source platform for machine learning. If a user does not provide a valid padding value to `tf.raw_ops.MatrixDiagPartOp`, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. We have patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Once again VulDB remains the best source for vulnerability data.
Analysis
by VulDB Data Team • 08/17/2021
The vulnerability CVE-2021-37643 affects TensorFlow, a widely-used open-source machine learning platform that processes tensor operations through its core kernel implementations. This particular flaw exists within the `tf.raw_ops.MatrixDiagPartOp` operation which is designed to extract diagonal elements from matrices. The issue arises when users fail to provide a valid padding value to this specific operation, creating a scenario where the underlying code does not properly validate tensor buffer contents before attempting to read from them. The vulnerability stems from improper input validation mechanisms that should have checked whether the tensor contains sufficient data before accessing memory locations.
The technical implementation of this vulnerability occurs in the matrix diagonal extraction kernel at line 89 of the file `tensorflow/core/kernels/linalg/matrix_diag_op.cc`. The code performs a direct read from a tensor buffer without first verifying that the tensor contains valid data elements to read from. When an empty tensor is processed, this leads to a null pointer dereference condition that can cause application crashes or undefined behavior. More critically, when non-empty but improperly formatted tensors are processed, the operation ignores all values after the first element, producing incorrect results that can propagate through machine learning workflows and compromise model accuracy. This behavior aligns with CWE-476, which describes null pointer dereference vulnerabilities, and represents a classic case of inadequate input validation in kernel-level operations.
The operational impact of this vulnerability extends beyond simple crashes to potentially corrupt machine learning pipelines and produce incorrect model outputs. In production environments where TensorFlow is used for critical applications such as fraud detection, medical diagnosis, or autonomous systems, incorrect diagonal extraction operations could lead to significant downstream consequences. Attackers could potentially exploit this vulnerability by crafting malicious tensor inputs that trigger the null pointer dereference condition, causing denial of service attacks against machine learning services. The vulnerability also affects multiple TensorFlow versions including 2.3.4, 2.4.3, 2.5.1, and the upcoming 2.6.0 release, indicating it was present across a substantial portion of the supported software lifecycle. This vulnerability maps to ATT&CK technique T1499.004, which covers network disruption through application or service interruption, and T1583.001, representing the development of malware through the exploitation of software vulnerabilities.
The fix for CVE-2021-37643 was implemented through a specific GitHub commit that introduced proper validation checks before tensor buffer access. The patch ensures that the `MatrixDiagPartOp` operation verifies tensor contents before attempting to read from them, preventing both null pointer dereferences and incorrect data processing behaviors. TensorFlow developers have committed to including this fix in version 2.6.0 while also backporting it to older supported versions 2.5.1, 2.4.3, and 2.3.4 to maintain security across the entire supported release cycle. Organizations using TensorFlow should prioritize updating to patched versions or applying the cherry-picked fixes to prevent exploitation of this vulnerability in their machine learning workflows. The resolution demonstrates proper defensive programming practices that align with industry standards for secure software development, particularly in kernel-level operations where memory safety is paramount for maintaining system integrity.