CVE-2021-37640 in TensorFlow
Summary
by MITRE • 08/12/2021
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version.
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Analysis
by VulDB Data Team • 08/17/2021
The vulnerability CVE-2021-37640 affects TensorFlow, a widely used open source machine learning platform that enables developers to build and deploy machine learning models across various environments. This security flaw specifically targets the `tf.raw_ops.SparseReshape` operation within TensorFlow's kernel implementations, representing a critical software defect that can lead to system instability and potential denial of service conditions. The issue stems from insufficient input validation within the tensor reshaping functionality that processes sparse tensor data structures. The vulnerability is classified under CWE-369 as a divide-by-zero error occurring in a security-relevant context, making it particularly dangerous in production environments where TensorFlow is deployed for critical applications.
The technical implementation of this vulnerability occurs in the reshape utility functions within TensorFlow's core kernels, specifically in the `reshape_util.cc` file where the `tf.raw_ops.SparseReshape` operation is implemented. The flaw manifests when the system attempts to reshape sparse tensors without verifying that both the input tensor shape and target shape contain valid dimensions with non-zero element counts. The implementation calls the reshaping functor unconditionally whenever at least one index exists in the input, but fails to validate that the target shape dimensions are non-zero before proceeding with the mathematical operations. The reshape functor itself performs direct division operations on target shape dimensions without proper bounds checking, creating an environment where a division by zero exception can be triggered when the target shape contains zero dimensions.
The operational impact of this vulnerability extends beyond simple system crashes, potentially enabling attackers to exploit the division by zero condition for denial of service attacks against TensorFlow-based applications and services. When the vulnerable code path is executed with malformed input data containing zero dimensions in target shapes, the system will encounter a runtime exception that can terminate processes or cause application instability. This vulnerability affects multiple TensorFlow versions including 2.5.0 and earlier releases, with the fix being incorporated into TensorFlow 2.6.0 and backported to 2.5.1. The security implications are significant given TensorFlow's widespread adoption in production environments, particularly in cloud services, data centers, and AI-powered applications where system reliability and availability are paramount. Attackers who can influence the input to `tf.raw_ops.SparseReshape` operations could potentially cause service disruption through carefully crafted tensor data that triggers the division by zero condition.
Mitigation strategies for this vulnerability should prioritize immediate deployment of patched TensorFlow versions, specifically TensorFlow 2.5.1 or 2.6.0, which contain the necessary code modifications to prevent the division by zero error. Organizations should implement comprehensive input validation procedures for all tensor reshaping operations, particularly when processing external or untrusted data sources. The fix implemented in commit 4923de56ec94fff7770df259ab7f2288a74feb41 addresses the root cause by adding proper dimension validation checks before any mathematical operations are performed on target shapes. Additionally, security teams should monitor for potential indirect exploitation vectors where attackers might craft complex tensor operations that could trigger this condition through legitimate application workflows, implementing proper error handling and logging mechanisms to detect anomalous behavior patterns that could indicate attempted exploitation of this vulnerability.