Upstream information
Description
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.SUSE information
Overall state of this security issue: Does not affect SUSE products
This issue is currently rated as having important severity.
| National Vulnerability Database | |
|---|---|
| Base Score | 2.1 |
| Vector | AV:L/AC:L/Au:N/C:N/I:N/A:P |
| Access Vector | Local |
| Access Complexity | Low |
| Authentication | None |
| Confidentiality Impact | None |
| Integrity Impact | None |
| Availability Impact | Partial |
| National Vulnerability Database | |
|---|---|
| Base Score | 5.5 |
| Vector | CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H |
| Attack Vector | Local |
| Attack Complexity | Low |
| Privileges Required | Low |
| User Interaction | None |
| Scope | Unchanged |
| Confidentiality Impact | None |
| Integrity Impact | None |
| Availability Impact | High |
| CVSSv3 Version | 3.1 |
SUSE Timeline for this CVE
CVE page created: Mon May 17 08:58:28 2021CVE page last modified: Sat Mar 30 16:29:56 2024