CVE-2022-35973
published 2022-09-16CVE-2022-35973: TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a…
PriorityP337high7.5CVSS 3.1
AVNACLPRNUINSUCNINAH
EPSS
0.44%
35.6th percentile
TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. 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
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Debian
CVE-2022-35973: tensorflow - TensorFlow is an open source platform for machine learning. If `QuantizedMatMul`...
vendor_debian·2022·CVSS 5.9
CVE-2022-35973 [MEDIUM] CVE-2022-35973: tensorflow - TensorFlow is an open source platform for machine learning. If `QuantizedMatMul`...
TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. 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 `QuantizedMatMul`
osv·2022-09-16
CVE-2022-35973 [MEDIUM] TensorFlow vulnerable to segfault in `QuantizedMatMul`
TensorFlow vulnerable to segfault in `QuantizedMatMul`
### Impact
If `QuantizedMatMul` is given nonscalar input for:
- `min_a`
- `max_a`
- `min_b`
- `max_b`
It gives a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
Toutput = tf.qint32
transpose_a = False
transpose_b = False
Tactivation = tf.quint8
a = tf.constant(7, shape=[3,4], dtype=tf.quint8)
b = tf.constant(1, shape=[2,3], dtype=tf.quint8)
min_a = tf.constant([], shape=[0], dtype=tf.float32)
max_a = tf.constant(0, shape=[1], dtype=tf.float32)
min_b = tf.constant(0, shape=[1], dtype=tf.float32)
max_b = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedMatMul(a=a, b=b, min_a=min_a, max_a=max_a, min_b=min_b, max_b=max_b, Toutput=Toutput, transpose_a=transpose_a, transpose
GHSA
TensorFlow vulnerable to segfault in `QuantizedMatMul`
ghsa·2022-09-16
CVE-2022-35973 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `QuantizedMatMul`
TensorFlow vulnerable to segfault in `QuantizedMatMul`
### Impact
If `QuantizedMatMul` is given nonscalar input for:
- `min_a`
- `max_a`
- `min_b`
- `max_b`
It gives a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
Toutput = tf.qint32
transpose_a = False
transpose_b = False
Tactivation = tf.quint8
a = tf.constant(7, shape=[3,4], dtype=tf.quint8)
b = tf.constant(1, shape=[2,3], dtype=tf.quint8)
min_a = tf.constant([], shape=[0], dtype=tf.float32)
max_a = tf.constant(0, shape=[1], dtype=tf.float32)
min_b = tf.constant(0, shape=[1], dtype=tf.float32)
max_b = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedMatMul(a=a, b=b, min_a=min_a, max_a=max_a, min_b=min_b, max_b=max_b, Toutput=Toutput, transpose_a=transpose_a, transpose
No detection rules found.
No public exploits indexed.
No writeups or analysis indexed.
https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9vhttps://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v
2022-09-16
Published