CVE-2022-36005
published 2022-09-16CVE-2022-36005: TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is…
PriorityP336high7.5CVSS 3.1
AVNACLPRNUINSUCNINAH
EPSS
0.41%
33.3th percentile
TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. 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 | < 2.7.2 | 2.7.2 | |
| tensorflow | — | — | |
| 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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GHSA
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`
ghsa·2022-09-16
CVE-2022-36005 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`
### Impact
When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack.
```python
import tensorflow as tf
import numpy as np
arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_4=8
arg_5=False
arg_6=''
tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1,
min=arg_2, max=arg_
OSV
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`
osv·2022-09-16
CVE-2022-36005 [MEDIUM] TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`
### Impact
When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack.
```python
import tensorflow as tf
import numpy as np
arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_4=8
arg_5=False
arg_6=''
tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1,
min=arg_2, max=arg_
Debian
CVE-2022-36005: tensorflow - TensorFlow is an open source platform for machine learning. When `tf.quantizatio...
vendor_debian·2022·CVSS 5.9
CVE-2022-36005 [MEDIUM] CVE-2022-36005: tensorflow - TensorFlow is an open source platform for machine learning. When `tf.quantizatio...
TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. 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
No detection rules found.
No public exploits indexed.
No writeups or analysis indexed.
https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feedhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjmhttps://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feedhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjm
2022-09-16
Published