Upstream information
Description
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. 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 | 3.6 |
| Vector | AV:L/AC:L/Au:N/C:P/I:N/A:P |
| Access Vector | Local |
| Access Complexity | Low |
| Authentication | None |
| Confidentiality Impact | Partial |
| Integrity Impact | None |
| Availability Impact | Partial |
| National Vulnerability Database | |
|---|---|
| Base Score | 7.1 |
| Vector | CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H |
| Attack Vector | Local |
| Attack Complexity | Low |
| Privileges Required | Low |
| User Interaction | None |
| Scope | Unchanged |
| Confidentiality Impact | High |
| 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 16:29:51 2024