CVE-2022-21726
published 2022-02-03CVE-2022-21726: Tensorflow is an Open Source Machine Learning Framework. The implementation of `Dequantize` does not fully validate the value of `axis` and can result in heap…
PriorityP343high8.8CVSS 3.1
AVNACLPRLUINSUCHIHAH
EPSS
0.82%
53.4th percentile
Tensorflow is an Open Source Machine Learning Framework. The implementation of `Dequantize` does not fully validate the value of `axis` and can result in heap OOB accesses. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor. 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.
Affected
8 ranges
| Vendor | Product | Version range | Fixed in |
|---|---|---|---|
| debian | tensorflow | — | — |
| tensorflow | <= 2.5.2 | — | |
| tensorflow | — | — | |
| tensorflow | 2.6.0 – 2.6.2 | — | |
| intel | optimization_for_tensorflow | >= 0 < 2.5.3 | 2.5.3 |
| intel | optimization_for_tensorflow | >= 2.6.0 < 2.6.3 | 2.6.3 |
| intel | optimization_for_tensorflow | >= 2.7.0 < 2.7.1 | 2.7.1 |
| linux | linux_kernel | >= 0 < 5.4.0-225.245 | 5.4.0-225.245 |
CVSS provenance
nvdv3.18.8HIGHCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
nvdv2.06.5MEDIUMAV:N/AC:L/Au:S/C:P/I:P/A:P
osv7.8HIGH
vendor_debian8.1LOW
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OSV
linux-raspi, linux-raspi-5.4 vulnerabilities
osv·2026-03-03·CVSS 7.8
CVE-2022-49698 linux-raspi, linux-raspi-5.4 vulnerabilities
linux-raspi, linux-raspi-5.4 vulnerabilities
Several security issues were discovered in the Linux kernel.
An attacker could possibly use these to compromise the system.
This update corrects flaws in the following subsystems:
- Cryptographic API;
- Padata parallel execution mechanism;
- Netfilter;
(CVE-2022-49698, CVE-2025-21726, CVE-2025-40019)
OSV
linux-azure, linux-azure-5.4, linux-azure-fips vulnerabilities
osv·2026-02-20·CVSS 7.8
CVE-2022-49698 linux-azure, linux-azure-5.4, linux-azure-fips vulnerabilities
linux-azure, linux-azure-5.4, linux-azure-fips vulnerabilities
Several security issues were discovered in the Linux kernel.
An attacker could possibly use these to compromise the system.
This update corrects flaws in the following subsystems:
- Cryptographic API;
- Padata parallel execution mechanism;
- Netfilter;
(CVE-2022-49698, CVE-2025-21726, CVE-2025-40019)
OSV
linux-oracle, linux-oracle-5.4 vulnerabilities
osv·2026-02-12·CVSS 7.8
CVE-2022-49698 linux-oracle, linux-oracle-5.4 vulnerabilities
linux-oracle, linux-oracle-5.4 vulnerabilities
Several security issues were discovered in the Linux kernel.
An attacker could possibly use these to compromise the system.
This update corrects flaws in the following subsystems:
- Cryptographic API;
- Padata parallel execution mechanism;
- Netfilter;
(CVE-2022-49698, CVE-2025-21726, CVE-2025-40019)
OSV
linux-xilinx-zynqmp vulnerabilities
osv·2026-02-11·CVSS 7.8
CVE-2022-49698 linux-xilinx-zynqmp vulnerabilities
linux-xilinx-zynqmp vulnerabilities
Several security issues were discovered in the Linux kernel.
An attacker could possibly use these to compromise the system.
This update corrects flaws in the following subsystems:
- Cryptographic API;
- Padata parallel execution mechanism;
- Netfilter;
(CVE-2022-49698, CVE-2025-21726, CVE-2025-40019)
OSV
linux, linux-aws, linux-aws-5.4, linux-gcp, linux-gcp-5.4, linux-hwe-5.4, linux-ibm, linux-ibm-5.4, linux-iot, linux-kvm vulnerabilities
osv·2026-01-30·CVSS 7.8
CVE-2022-49698 linux, linux-aws, linux-aws-5.4, linux-gcp, linux-gcp-5.4, linux-hwe-5.4, linux-ibm, linux-ibm-5.4, linux-iot, linux-kvm vulnerabilities
linux, linux-aws, linux-aws-5.4, linux-gcp, linux-gcp-5.4, linux-hwe-5.4, linux-ibm, linux-ibm-5.4, linux-iot, linux-kvm vulnerabilities
Several security issues were discovered in the Linux kernel.
An attacker could possibly use these to compromise the system.
This update corrects flaws in the following subsystems:
- Cryptographic API;
- Padata parallel execution mechanism;
- Netfilter;
(CVE-2022-49698, CVE-2025-21726, CVE-2025-40019)
OSV
linux-aws-fips, linux-fips, linux-gcp-fips vulnerabilities
osv·2026-01-30·CVSS 7.8
CVE-2022-49698 linux-aws-fips, linux-fips, linux-gcp-fips vulnerabilities
linux-aws-fips, linux-fips, linux-gcp-fips vulnerabilities
Several security issues were discovered in the Linux kernel.
An attacker could possibly use these to compromise the system.
This update corrects flaws in the following subsystems:
- Cryptographic API;
- Padata parallel execution mechanism;
- Netfilter;
(CVE-2022-49698, CVE-2025-21726, CVE-2025-40019)
OSV
Out of bounds read in Tensorflow
osv·2022-02-09
CVE-2022-21726 [HIGH] Out of bounds read in Tensorflow
Out of bounds read in Tensorflow
### Impact
The [implementation of `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153) does not fully validate the value of `axis` and can result in heap OOB accesses:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.Dequantize(
input=tf.constant([1,1],dtype=tf.qint32),
min_range=[1.0],
max_range=[10.0],
mode='MIN_COMBINED',
narrow_range=False,
axis=2**31-1,
dtype=tf.bfloat16)
return y
test()
```
The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the en
GHSA
Out of bounds read in Tensorflow
ghsa·2022-02-09
CVE-2022-21726 [HIGH] CWE-125 Out of bounds read in Tensorflow
Out of bounds read in Tensorflow
### Impact
The [implementation of `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153) does not fully validate the value of `axis` and can result in heap OOB accesses:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.Dequantize(
input=tf.constant([1,1],dtype=tf.qint32),
min_range=[1.0],
max_range=[10.0],
mode='MIN_COMBINED',
narrow_range=False,
axis=2**31-1,
dtype=tf.bfloat16)
return y
test()
```
The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the en
OSV
CVE-2022-21726: Tensorflow is an Open Source Machine Learning Framework
osv·2022-02-03
CVE-2022-21726 CVE-2022-21726: Tensorflow is an Open Source Machine Learning Framework
Tensorflow is an Open Source Machine Learning Framework. The implementation of `Dequantize` does not fully validate the value of `axis` and can result in heap OOB accesses. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor. 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.
Debian
CVE-2022-21726: tensorflow - Tensorflow is an Open Source Machine Learning Framework. The implementation of `...
vendor_debian·2022·CVSS 8.1
CVE-2022-21726 [HIGH] CVE-2022-21726: tensorflow - Tensorflow is an Open Source Machine Learning Framework. The implementation of `...
Tensorflow is an Open Source Machine Learning Framework. The implementation of `Dequantize` does not fully validate the value of `axis` and can result in heap OOB accesses. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor. 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.
Scope: local
forky: resolved
sid: resolved
No detection rules found.
No public exploits indexed.
No writeups or analysis indexed.
https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72
2022-02-03
Published