The implementation of tf.sparse.split does not fully validate the input arguments. Hence, a malicious user can trigger a denial of service via a segfault or a heap OOB read:
import tensorflow as tf
data = tf.random.uniform([1, 32, 32], dtype=tf.float32)
axis = [1, 2]
x = tf.sparse.from_dense(data)
result = tf.sparse.split(x,3, axis=axis)
The code assumes axis is a scalar. This is another instance of TFSA-2021-190 (CVE-2021-41206).
We have patched the issue in GitHub commit 61bf91e768173b001d56923600b40d9a95a04ad5 (merging #53695).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported externally via a GitHub issue.
| Software | From | Fixed in |
|---|---|---|
tensorflow
|
- | 2.5.3 |
tensorflow
|
2.6.0 | 2.6.3 |
tensorflow
|
2.7.0 | 2.7.0.x |
tensorflow
|
2.7.0 | 2.7.1 |
tensorflow-cpu
|
- | 2.5.3 |
tensorflow-cpu
|
2.6.0 | 2.6.3 |
tensorflow-cpu
|
2.7.0 | 2.7.0.x |
tensorflow-cpu
|
2.7.0 | 2.7.1 |
tensorflow-gpu
|
- | 2.5.3 |
tensorflow-gpu
|
2.6.0 | 2.6.3 |
tensorflow-gpu
|
2.7.0 | 2.7.0.x |
tensorflow-gpu
|
2.7.0 | 2.7.1 |
A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.
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