5.5
CVE-2021-37645
- EPSS 0.01%
- Published 12.08.2021 21:15:07
- Last modified 21.11.2024 06:15:35
- Source security-advisories@github.com
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TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L126) uses the `axis` value as the size argument to `absl::InlinedVector` constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, and TensorFlow 2.4.3, as these are also affected and still in supported range.
Data is provided by the National Vulnerability Database (NVD)
Google ≫ Tensorflow Version >= 2.3.0 < 2.3.4
Google ≫ Tensorflow Version >= 2.4.0 < 2.4.3
Google ≫ Tensorflow Version2.5.0
Google ≫ Tensorflow Version2.6.0 Updaterc0
Google ≫ Tensorflow Version2.6.0 Updaterc1
Google ≫ Tensorflow Version2.6.0 Updaterc2
Zu dieser CVE wurde keine CISA KEV oder CERT.AT-Warnung gefunden.
| Type | Source | Score | Percentile |
|---|---|---|---|
| EPSS | FIRST.org | 0.01% | 0.011 |
| Source | Base Score | Exploit Score | Impact Score | Vector string |
|---|---|---|---|---|
| nvd@nist.gov | 5.5 | 1.8 | 3.6 |
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
|
| nvd@nist.gov | 2.1 | 3.9 | 2.9 |
AV:L/AC:L/Au:N/C:N/I:N/A:P
|
| security-advisories@github.com | 5.5 | 1.8 | 3.6 |
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
|
CWE-681 Incorrect Conversion between Numeric Types
When converting from one data type to another, such as long to integer, data can be omitted or translated in a way that produces unexpected values. If the resulting values are used in a sensitive context, then dangerous behaviors may occur.