CVE-2021-29547
published 2021-05-14CVE-2021-29547: TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of…
PriorityP421medium5.5CVSS 3.1
AVLACLPRLUINSUCNINAH
EPSS
0.19%
8.8th percentile
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Affected
18 ranges
| Vendor | Product | Version range | Fixed in |
|---|---|---|---|
| debian | tensorflow | — | — |
| tensorflow | < 2.1.4 | 2.1.4 | |
| tensorflow | >= 2.2.0 < 2.2.3 | 2.2.3 | |
| tensorflow | >= 2.3.0 < 2.3.3 | 2.3.3 | |
| tensorflow | >= 2.4.0 < 2.4.2 | 2.4.2 | |
| intel | optimization_for_tensorflow | >= 0 < d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b | d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b |
| intel | optimization_for_tensorflow | >= 0 < 2.2.0rc0 | 2.2.0rc0 |
| intel | optimization_for_tensorflow | >= 0 < 2.1.4 | 2.1.4 |
| intel | optimization_for_tensorflow | >= 2.2.0 < 2.3.0rc0 | 2.3.0rc0 |
| intel | optimization_for_tensorflow | >= 2.2.0 < 2.2.3 | 2.2.3 |
| intel | optimization_for_tensorflow | >= 2.3.0 < 2.3.4 | 2.3.4 |
| intel | optimization_for_tensorflow | >= 2.3.0 < 2.3.3 | 2.3.3 |
| intel | optimization_for_tensorflow | >= 2.4.0 < 2.4.3 | 2.4.3 |
| intel | optimization_for_tensorflow | >= 2.4.0 < 2.4.2 | 2.4.2 |
| tensorflow | tensorflow | < 2.1.4 | 2.1.4 |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
CVSS provenance
nvdv3.15.5MEDIUMCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
nvdv2.02.1LOWAV:L/AC:L/Au:N/C:N/I:N/A:P
vendor_debian2.5LOW
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Debian
CVE-2021-29547: tensorflow - TensorFlow is an end-to-end open source platform for machine learning. An attack...
vendor_debian·2021·CVSS 2.5
CVE-2021-29547 [LOW] CVE-2021-29547: tensorflow - TensorFlow is an end-to-end open source platform for machine learning. An attack...
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Scope:
OSV
Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`
osv·2021-05-21
CVE-2021-29547 [LOW] Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`
Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`
### Impact
An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`:
```python
import tensorflow as tf
t = tf.constant([1], shape=[1, 1, 1, 1], dtype=tf.quint8)
t_min = tf.constant([], shape=[0], dtype=tf.float32)
t_max = tf.constant([], shape=[0], dtype=tf.float32)
m = tf.constant([1], shape=[1], dtype=tf.quint8)
m_min = tf.constant([], shape=[0], dtype=tf.float32)
m_max = tf.constant([], shape=[0], dtype=tf.float32)
v = tf.constant([1], shape=[1], dtype=tf.quint8)
v_min = tf.constant([], shape=[0], dtype=tf.float32)
v_max = tf.constant([], shape=[0], dtype=tf.float32)
beta = tf.constant([1], shape=[1], dtype=tf.quint8)
beta_min
GHSA
Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`
ghsa·2021-05-21
CVE-2021-29547 [LOW] CWE-125 Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`
Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`
### Impact
An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`:
```python
import tensorflow as tf
t = tf.constant([1], shape=[1, 1, 1, 1], dtype=tf.quint8)
t_min = tf.constant([], shape=[0], dtype=tf.float32)
t_max = tf.constant([], shape=[0], dtype=tf.float32)
m = tf.constant([1], shape=[1], dtype=tf.quint8)
m_min = tf.constant([], shape=[0], dtype=tf.float32)
m_max = tf.constant([], shape=[0], dtype=tf.float32)
v = tf.constant([1], shape=[1], dtype=tf.quint8)
v_min = tf.constant([], shape=[0], dtype=tf.float32)
v_max = tf.constant([], shape=[0], dtype=tf.float32)
beta = tf.constant([1], shape=[1], dtype=tf.quint8)
beta_min
OSV
CVE-2021-29547: TensorFlow is an end-to-end open source platform for machine learning
osv·2021-05-14
CVE-2021-29547 CVE-2021-29547: TensorFlow is an end-to-end open source platform for machine learning
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
No detection rules found.
No public exploits indexed.
No writeups or analysis indexed.
https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75bhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xjhttps://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75bhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj
2021-05-14
Published